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Evaluation of the Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries

Evaluation of the Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting... Key Points Question What is the antibiotic IMPORTANCE High levels of antimicrobial resistance in neonatal bloodstream isolates are being coverage offered by empirical neonatal reported globally, including in Asia. Local hospital antibiogram data may include too few isolates to sepsis treatment with aminopenicillin- meaningfully examine the expected coverage of antibiotic regimens. gentamicin, third-generation cephalosporins (cefotaxime or OBJECTIVE To assess the coverage offered by 3 antibiotic regimens for empirical treatment of ceftriaxone), and meropenem in Asian neonatal sepsis in Asian countries. countries? Findings In this decision analytical DESIGN, SETTING, AND PARTICIPANTS A decision analytical model was used to estimate coverage model based on a decision tree, 8376 of 3 prespecified antibiotic regimens according to a weighted-incidence syndromic combination isolates from 10 countries were used to antibiogram. Relevant data to parameterize the models were identified from a systematic search of estimate coverage. Meropenem Ovid MEDLINE and Embase. Data from Asian countries published from 2014 onward were of interest. generally had the highest coverage Only data on blood culture isolates from neonates with sepsis, bloodstream infection, or bacteremia (from 64.0% in India to 90.6% in reported from the relevant setting were included. Data analysis was performed from April 2019 to Cambodia) followed by aminopenicillin- July 2019. gentamicin (from 35.9% in Indonesia to 81.0% in Laos) and cefotaxime or EXPOSURES The prespecified regimens of interest were aminopenicillin-gentamicin, third- ceftriaxone (from 17.9% in Indonesia to generation cephalosporins (cefotaxime or ceftriaxone), and meropenem. The relative incidence of 75.0% in Laos); in all countries except different bacteria and their antimicrobial susceptibility to antibiotics relevant for determining Laos and Nepal, meropenem coverage expected concordance with these regimens were extracted. was higher than that of the other 2 regimens. MAIN OUTCOMES AND MEASURES Coverage was calculated on the basis of a decision-tree model incorporating relative bacterial incidence and antimicrobial susceptibility of relevant isolates. Data Meaning The findings suggest that on 7 bacteria most commonly reported in the included studies were used for estimating coverage, noncarbapenems may provide limited which was reported at the country level. empirical neonatal sepsis coverage in many Asian countries. RESULTS Data from 48 studies reporting on 10 countries and 8376 isolates were used. Individual countries reported 51 (Vietnam) to 6284 (India) isolates. Coverage varied considerably between Invited Commentary countries. Meropenem was generally estimated to provide the highest coverage, ranging from 64.0% (95% credible interval [CrI], 62.6%-65.4%) in India to 90.6% (95% CrI, 86.2%-94.4%) in Supplemental content Cambodia, followed by aminopenicillin-gentamicin (from 35.9% [95% CrI, 27.7%-44.0%] in Author affiliations and article information are Indonesia to 81.0% [95% CrI, 71.1%-89.7%] in Laos) and cefotaxime or ceftriaxone (from 17.9% [95% listed at the end of this article. CrI, 11.7%-24.7%] in Indonesia to 75.0% [95% CrI, 64.8%-84.1%] in Laos). Aminopenicillin- gentamicin coverage was lower than that of meropenem in all countries except Laos (81.0%; 95% CrI, 71.1%-89.7%) and Nepal (74.3%; 95% CrI, 70.3%-78.2%), where 95% CrIs for aminopenicillin- gentamicin and meropenem were overlapping. Third-generation cephalosporin coverage was lowest of the 3 regimens in all countries. The coverage difference between aminopenicillin-gentamicin and meropenem for countries with nonoverlapping 95% CrIs ranged from −15.9% in China to −52.9% in Indonesia. (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 1/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Abstract (continued) CONCLUSIONS AND RELEVANCE This study’s findings suggest that noncarbapenem antibiotic regimens may provide limited coverage for empirical treatment of neonatal sepsis in many Asian countries. Alternative regimens must be studied to limit carbapenem consumption. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 Introduction Although overall maternal and child mortality have substantially declined worldwide since the early 2000s, neonatal mortality associated with bacterial infection has remained high, with nearly half a million estimated annual deaths due to neonatal sepsis. Most of these deaths occur in low- and middle-income countries (LMICs), including many thousands in Asia. In a recent prospective cohort study of more than 13 500 live births in India, the case-fatality 4-7 rate of culture-positive neonatal sepsis episodes was nearly 50%. Recent systematic reviews indicate a high level of bacterial resistance to World Health Organization (WHO)–recommended empirical treatment regimens for serious neonatal and pediatric infections in LMICs, especially in bloodstream isolates. Globally, antimicrobial resistance is estimated to be implicated in up to one-third of neonatal sepsis deaths annually. Clinicians and guideline-setting bodies can be assisted in selecting optimal empirical antibiotic regimens by knowing the coverage of alternative regimens. Regimen coverage refers to the proportion of infection episodes that would be treated by the regimen at a stage when the causative pathogen is not yet known, therefore incorporating the frequencies of different causative bacteria and their resistance patterns. Several techniques are available to estimate coverage. One example is 9-11 the weighted-incidence syndromic combination antibiogram (WISCA), which estimates coverage by accounting for the relative incidence of different bacteria and their resistance patterns for a specific infection syndrome, in this case neonatal sepsis. Coverage can be estimated for both single- drug and combination treatment regimens. International guidelines provide recommendations for the empirical antibiotic treatment of neonatal bacterial infections and should aim to provide adequate coverage in target settings, especially LMICs. The objective of this decision analytical model study was, therefore, to evaluate the coverage offered by 3 prespecified antibiotic regimens according to WISCAs and focusing on Asia, a region with a high prevalence of bacterial resistance. Methods We estimated coverage using data on antimicrobial resistance that were used to create WISCAs for each country with reported data, as identified by a systematic review of the literature. Because only published data were used in the analysis, no formal ethical review was required according to guidance by the NHS Health Research Authority. This study follows the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline, because it is broadly applicable to any decision-model based analyses (eAppendix in the Supplement). Regimens Selected for Coverage Estimation The 3 regimens evaluated in this study were aminopenicillin-gentamicin (WHO-recommended first- line treatment; alternatives, benzylpenicillin or cloxacillin plus gentamicin), third-generation cephalosporins (WHO-recommended second-line treatment, assumed to be cefotaxime or ceftriaxone, not ceftazidime), and meropenem. The last regimen was evaluated because it has now been reported to be the most commonly used empirical treatment in LMICs for neonatal sepsis. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 2/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Identification of Relevant Data for Parameter Estimation A systematic search of the literature was conducted in Ovid MEDLINE and in Embase on January 23, 2019. Using both free-text and MeSH terms, publications on “sepsis” and “antibiotic resistance” and (“neonates” or infants”) and “Asia” were identified (eAppendix in the Supplement). Given increasing antimicrobial resistance, and to obtain contemporaneous estimates, we arbitrarily limited the search to articles published from 2014 onward. No additional limits were applied. Studies were reviewed against prespecified eligibility criteria, and data were extracted using a standardized prepiloted form implemented in REDCap (eAppendix in the Supplement). Extracted data for WISCA calculation included information on the total number of bacterial isolates from relevant blood cultures, the number of isolates of specific bacterial species or genera, the number of isolates tested for susceptibility to the antibiotics relevant for establishing coverage offered by the prespecified regimens of interest, and the number of isolates found to be susceptible to these antibiotics. We excluded bacteria known to frequently represent contamination rather than true infection, most importantly coagulase-negative staphylococci. The exclusion of coagulase- negative staphylococci is likely to result in the overestimation of coverage for β-lactam–based 17,18 regimens because of very high expected rates of methicillin resistance of 66% to more than 90%. Estimation of WISCA Parameters Tables containing the parameter values required for coverage estimation were created by country and regimen. The relative incidence parameters were based only on bacteria reported as contributing to neonatal sepsis in more than 50% of the eligible studies. This meant that estimated coverage was based on the most important and frequent pathogens identified in blood cultures from neonates in the target region. Including rare pathogens within the WISCA would have a minimal impact on the estimated coverage, and including those likely to be contaminants or unusual pathogens (potentially observed as part of unidentified outbreaks) could introduce substantial bias. For the bacteria identified in this way, their relative incidence was based on the frequency reported in the studies. Similarly, regimen susceptibility was derived directly from reported data with the number of tested isolates representing the denominator. Details of the assumptions for determining susceptibility of pathogens to each regimen are provided in the eAppendix in the Supplement. Statistical Analysis Regimen coverage was estimated using a previously described Bayesian WISCA. This approach has various advantages. It addresses the typical clinical approach of treating an infection syndrome, often with incomplete knowledge about the frequency of causative bacteria and their susceptibilities. The Bayesian WISCA also explicitly deals with intrinsic resistance and handles imprecision attributed to a small sample size or incomplete susceptibility testing data. In brief, the WISCA gives the expected levels of therapeutic coverage for an antibiotic regimen—in our case, regimens used to treat neonates with sepsis. The WISCA can be represented as a decision tree (eFigure 1 in the Supplement). Combining the probabilities along the regimen tree branches generates coverage estimates from relative bacterial incidence and proportions of each included pathogen susceptible to the antibiotic regimen. In essence, the WISCA is a weighted mean of the susceptibilities of the bacteria, with the weights defined by their relative incidence. The observed data on pathogen incidence and their susceptibility to the 3 regimens were combined with an appropriate Bayesian prior distribution that corresponded to our prestudy beliefs about these parameters. We had no strong prior belief about the relative incidence of the pathogens or for the majority of what level of susceptibility there might be within a country, and a noninformative prior was used in these cases. However, in some circumstances, specific pathogens were expected to have intrinsic resistance to the regimen and, consequently, not to have 19,20 susceptibility regardless of reported susceptibility testing results. In these situations, an informative prior was used to dominate the observed data. On the basis of European Committee for 19,20 Antimicrobial Susceptibility Testing (EUCAST) recommendations, enterococci, as well as JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 3/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Acinetobacter species and Pseudomonas species, were assumed to be intrinsically resistant to recommended third-generation cephalosporins and therefore not susceptible to third-generation cephalosporins. The value of the pathogen incidence and pathogen regimen-susceptibility parameters were defined as probability distributions to reflect the uncertainty in their respective values. The relative incidence of pathogens was modeled using a Dirichlet distribution, and the susceptibility parameters were defined as beta distributions; 95% credible intervals (95% CrIs) for the coverage estimates were calculated using Monte Carlo simulations, based on 1000 runs (eAppendix in the Supplement). All modeling was undertaken using Stata statistical software version 13.1 (StataCorp) and Excel spreadsheet software version 2010 (Microsoft Corp). Data analysis was performed from April 2019 to July 2019. Results Description of Data Set The literature review included data from 48 publications (eFigure 2 in the Supplement) representing 52 centers in 10 Asian countries (1 center in Cambodia, 5 in China, 33 in India, 1 in Indonesia, 1 in Laos, 1 in Malaysia, 6 in Nepal, 2 in Pakistan, 1 in Taiwan, and 1 in Vietnam). Of the 52 centers, 34 were university or tertiary hospitals, 10 were nonteaching or district hospitals (9 in India and 1 in China), and 8 were maternity or pediatric hospitals (1 in Cambodia, 2 in China, 4 in Nepal, and 1 in Vietnam). Ten articles were published in 2014, 13 in 2015, 10 in 2016, 8 in 2017, 6 in 2018, and 1 in 2019. For 32 of 48 publications, the observation period started in 2010 or later, with the earliest start date being January 1, 1990 (eTable 1 in the Supplement). Five publications did not report calendar dates for their observation period, but 4 of 5 indicated its duration. The median observation period was 2 years, with the shortest and longest periods being 2 months and 12 years, respectively. Most publications (33 of 48) reported on bloodstream isolates from neonates with clinical community-acquired or nosocomial sepsis. Another 12 publications based reporting on microbiologically defined bacteremia. Only 4 publications focused on either nosocomial or community-acquired infections (2 each). Reporting of information on sample processing, including species identification, antibiotic susceptibility testing methods, and interpretive guidelines, was variable (eTable 2 in the Supplement). Reported Bloodstream Isolates Individual publications included between 15 and 2112 isolates, with a median of 98 isolates (eTable 3 in the Supplement). The following bacteria were most frequently reported as contributing to neonatal sepsis or bacteremia: Escherichia coli (46 of 48 publications), Klebsiella species and Staphylococcus aureus (45 of 48 publications each), Pseudomonas species (35 of 48 publications), Acinetobacter species (32 of 48 publications), Enterobacter species (26 of 48 publications), and Enterococcus species (25 of 48 publications). In addition, coagulase-negative staphylococci were reported in 40 of 48 publications. All other bacteria, including Citrobacter species and Streptococcus agalactiae, were reported in less than one-half of the publications. On the basis of the prespecified criteria, E coli, Klebsiella species, S aureus, Pseudomonas species, Acinetobacter species, Enterobacter species, and Enterococcus species were selected for antibiotic regimen coverage estimation. Parameter Values: Isolates Reported and Susceptibility In total, 11 467 isolates were reported, with the greatest number coming from India (6284), China (2043), Pakistan (1875), and Nepal (640) (Table 1). Given the small number of reported isolates from Taiwan (36) and Malaysia (29), antibiotic regimen coverage was not estimated for these 2 countries. Most reported isolates (8584 of 11 467 [74.9%]) were from university or tertiary hospitals, with nonteaching or district hospitals contributing 11.5% (1319 of 11 467) and maternity or pediatric hospitals contributing another 13.6% (1564 of 11 467). JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 4/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries In total, 8376 isolates from 10 countries were used to estimate coverage. The proportion of reported isolates contributing to antibiotic regimen coverage estimation ranged from 91.9% (1723 of 1875) in Pakistan to 44.2% (905 of 2043) in China. Disregarding coagulase-negative staphylococci, the proportion of reported bacterial isolates contributing to coverage estimation ranged from 98.0% (51 of 52) in Vietnam to 69.5% (905 of 1302) in China. Availability of susceptibility testing information for aminopenicillin-gentamicin coverage ranged from 68.8% (623 of 905) in China to 100% in Indonesia (Table 2). For third-generation cephalosporins, this was available for 100% in Cambodia and Indonesia and 76.5% (39 of 51) in Vietnam (Table 3). For meropenem, available susceptibility testing information ranged from 100% in Indonesia to 60.3% (295 of 489) in Nepal (Table 4). Coverage Estimates at Country Level Coverage was consistently lowest for third-generation cephalosporin monotherapy, with some variation across the individual countries, ranging from 56.6% (95% CrI, 52.2%-60.7%) in Nepal to 17.9% (95% CrI, 11.7%-24.7%) in Indonesia (Figure). Similarly, although meropenem had the highest estimated coverage in each country, the proportion of neonates for whom it would be effective empirical treatment varied considerably, from 90.6% (95% CrI, 86.2%-94.4%) in Cambodia to 64.0% (95% CrI, 62.6%-65.4%) in India (Figure). Aminopenicillin-gentamicin offered the second highest level of coverage within each country behind meropenem. Nonetheless, there was again considerable variability in country-level estimates, from 74.3% (95% CrI, 70.3%-78.2%) in Nepal to 35.9% (95% CrI, 27.7%-44.0%) in Indonesia (Figure). Aminopenicillin-gentamicin coverage was higher than that offered by third-generation cephalosporins in China (60.6% [95% CrI, 54.2%-67.5%] vs 44.2% [95% CrI, 40.9%-47.9%]), India (45.1% [95% CrI, 43.7%-46.6%] vs 30.4% [95% CrI, 29.2%-31.6%]), Indonesia (35.9% [95% CrI, 27.7%-44.0%] vs 17.9% [95% CrI, 11.7%-24.7%]), and Nepal (74.3% [95% CrI, 70.3%-78.2%] vs 56.6% [95% CrI, 52.2%-60.7%]). There was greater uncertainty about whether the differences observed for Cambodia (47.4% [95% CrI, 38.1%-56.6%] vs 32.6% [95% CrI, 25.8%-39.9%]), Laos (81.0% [95% CrI, 71.1%-89.7%] vs 75.0% [95% CrI, 64.8%-84.1%]), Pakistan (42.2% [95% CrI, 39.1%-45.0%] vs 37.4% [95% CrI, 34.4%-40.3%]), and Vietnam (36.2% [95% CrI, 24.5%-49.0%] vs 21.5% [95% CrI, 12.0%-32.9%]) were due to chance variation. Table 1. Relative Incidence Data Isolates, No. (%) Cambodia China India Indonesia Laos Malaysia Nepal Pakistan Taiwan Vietnam Total Pathogen (n = 185) (n = 2043) (n = 6284) (n = 225) (n = 75) (n = 29) (n = 640) (n = 1875) (n = 36) (n = 75) (N = 11 467) Contributing to WISCA Escherichia coli 25 (16) 300 (33) 671 (14) 0 8 (13) 6 (33) 50 (10) 976 (57) 11 (92) 2 (4) 2049 (24) Klebsiella species 60 (39) 264 (29) 1065 (22) 49 (40) 9 (14) 1 (6) 45 (9) 159 (9) 1 (8) 18 (35) 1671 (20) Enterobacter species 18 (11) 58 (6) 167 (3) 20 (17) 4 (6) 0 30 (6) 0 0 6 (12) 303 (4) Acinetobacter species 16 (10) 27 (3) 992 (21) 21 (17) 2 (3) 0 63 (13) 0 0 17 (33) 1138 (14) Pseudomonas species 6 (4) 53 (6) 430 (9) 31 (26) 1 (2) 1 (6) 25 (5) 199 (12) 0 4 (8) 750 (9) Staphylococcus aureus 33 (21) 112 (12) 1235 (26) 0 37 (58) 10 (55) 261 (53) 388 (23) 0 4 (8) 2080 (25) Enterococcus species 0 91 (10) 275 (6) 0 3 (5) 0 15 (3) 1 (<1) 0 0 385 (5) Total reported during observation period Total contributing 158 (85) 905 (44) 4835 (77) 121 (54) 64 (85) 18 (62) 489 (76) 1723 (92) 12 (33) 51 (68) 8376 (73) to WISCA Other 27 (15) 1138 (56) 1449 (23) 104 (46) 11 (15) 11 (38) 151 (24) 152 (8) 24 (67) 24 (32) 3091 (27) (not contributing to WISCA) Coagulase-negative 0 741 (36) 980 (16) 63 (28) 0 0 137 (21) 28 (1) 0 23 (31) 1972 (17) staphylococci (not contributing to WISCA) Abbreviation: WISCA, weighted-incidence syndromic combination antibiogram. Percentages may not add to 100% because of rounding. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 5/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 6/13 Table 2. Susceptibility Testing and Susceptibility Data for Aminopenicillin Plus Gentamicin No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T S N T S N T S N TS N T S N TS N T S N TS N T S Escherichia coli 25 25 13 300 290 182 671 655 426 0 NA NA 8 8 6 50 50 31 976 976 340 2 0 NA 2033 2004 998 Klebsiella 60 60 10 264 256 193 1065 1026 402 49 49 3 9 9 7 45 42 23 159 159 36 18 11 2 1669 1612 676 species Enterobacter 18 18 8 58 20 11 167 154 42 20 20 18 4 0 NA 30 30 21 0 NA NA 6 5 3 303 247 103 species Acinetobacter 16 0 NA 27 0 NA 992 930 226 21 21 11 2 0 NA 63 62 34 0 NA NA 17 17 3 1138 1030 274 species Pseudomonas 6 0 NA 53 0 NA 430 422 238 31 31 9 1 0 NA 25 23 18 199 199 74 4 4 1 749 679 340 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 1 0 275 132 44 0 NA NA 3 0 NA 15 15 12 1 0 NA 0 NA NA 385 148 56 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 7/13 Table 3. Susceptibility Testing and Susceptibility Data for Third-Generation Cephalosporins No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T SN T S N T S N T S N T SN T S N T S N T S N T S Escherichia coli 25 25 13 300 289 165 671 657 339 0 NA NA 8 8 7 50 43 25 976 976 317 2 0 NA 2033 1998 866 Klebsiella 60 60 4 264 251 122 1065 1031 346 49 49 2 9 9 6 45 42 12 159 159 52 18 11 1 1669 1612 545 species Enterobacter 18 18 1 58 20 14 167 167 59 20 20 17 4 0 NA 30 28 12 0 NA NA 6 4 1 303 257 104 species Acinetobacter 16 16 0 27 27 0 992 992 0 21 21 0 2 2 0 63 63 0 0 NA NA 17 17 0 1138 1138 0 species Pseudomonas 66053 53 0 430 430 0 31 31 0 1 1 025 25 0 199 199 0 440 749 749 0 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 91 0 275 275 00 NA NA 3 3 0 15 15 01100 NA NA 385 385 0 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility Not based on susceptibility testing because pathogen was assumed to be intrinsically resistant. testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 8/13 Table 4. Susceptibility Testing and Susceptibility Data for Meropenem No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T S N TS N T S N TS N T S N TS N T S N T S N T S Escherichia coli 25 24 24 300 289 289 671 439 379 0 NA NA 8 0 NA 50 3 1 976 811 768 2 0 NA 2033 1566 1461 Klebsiella 60 60 60 264 253 228 1065 882 667 49 49 49 9 0 NA 45 27 27 159 102 87 18 9 9 1669 1382 1127 species Enterobacter 18 18 17 58 20 20 167 157 122 20 20 19 4 0 NA 30 16 14 0 NA NA 6 3 3 303 234 195 species Acinetobacter 16 16 14 27 0 NA 992 926 475 21 21 21 2 0 NA 63 7 3 0 NA NA 17 16 15 1138 986 528 species Pseudomonas 6 5 5 53 0 NA 430 415 354 31 31 23 1 0 NA 25 0 NA 199 199 188 4 3 3 749 653 573 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 91 0 275 275 0 0 NA NA 3 3 0 15 15 0 1 1 0 0 NA NA 385 385 0 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility Not based on susceptibility testing because pathogen was assumed to be intrinsically resistant. testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Meropenem coverage was higher than aminopenicillin-gentamicin coverage in Cambodia (90.6% [95% CrI, 86.2%-94.4%] vs 47.4% [95% CrI, 38.1%-56.6%]), China (76.5% [95% CrI, 71.8%- 80.9%] vs 60.6% [95% CrI, 54.2%-67.5%]), India (64.0% [95% CrI, 62.6%-65.4%] vs 45.1% [95% CrI, 43.7%-46.6%]), Indonesia (88.8% [95% CrI, 83.2%-93.6%] vs 35.9% [95% CrI, 27.7%-44.0%]), Pakistan (88.1% [95% CrI, 85.6%-90.3%] vs 42.2% [95% CrI, 39.1%-45.0%]), and Vietnam (84.1% [95% CrI, 73.2%-92.6%] vs 36.2% [95% CrI, 24.5%-49.0%]) on the basis of nonoverlapping 95% CrIs. The largest percentage differences in coverage were observed in Indonesia (52.9%), Pakistan (45.9%), and Cambodia (43.2%); the smallest was in China (15.9%). For meropenem and third- generation cephalosporins, the percentage difference was largest for Indonesia (70.9%), Vietnam (62.6%), and Cambodia (58.0%). Of note, for Laos and Nepal, imprecision around estimated meropenem coverage, which was comparable with that of aminopenicillin-gentamicin with overlapping 95% CrIs, was largely because of low proportions of isolates (62.5% [40 of 64] for Laos and 60.3% [295 of 489] for Nepal) contributing to the meropenem susceptibility parameter. Discussion We estimated the coverage offered by 3 antibiotic regimens—aminopenicillin-gentamicin (WHO- recommended first-line regimen), third-generation cephalosporins (WHO-recommended second- line regimen), and meropenem—in Asian countries for the empirical treatment of neonatal sepsis caused by 7 specified bacteria. The coverage estimates were based on a systematic review of recent studies reporting on the relative incidence of common bacteria and their resistance. In general, coverage estimates supported the identification of better-performing or worse- performing regimens for most countries. Coverage offered by aminopenicillin-gentamicin (WHO- recommended first-line regimen) was less than 50% for Cambodia, India, Indonesia, Pakistan, and Vietnam and less than 75% for China and Nepal. Even lower coverage was offered by the WHO-recommended second-line third-generation cephalosporin monotherapy regimen: below 50% Figure. Coverage Estimates for 8 Asian Countries Aminopenicillin and gentamicin Third-generation cephalosporin Meropenem a a a a b b a a Cambodia China India Indonesia Laos Nepal Pakistan Vietnam (n = 158) (n = 905) (n = 4835) (n = 121) (n = 64) (n = 489) (n = 1723) (n = 51) Point estimates are shown with 95% credible intervals, as denoted by error bars. The highest coverage offered by aminopenicillin-gentamicin combination was in Laos Nonoverlapping 95% credible intervals indicate likely within-country differences in (81.0%) and Nepal (74.3%). regimen coverage. Countries are shown together with the overall number of isolates used for estimating coverage. The highest coverage offered by meropenem was in Cambodia (90.6%), China (76.5%), India (64.0%), Indonesia (88.8%), Pakistan (88.1%), and Vietnam (84.1%). JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 9/13 Isolates Covered by Regimen, % JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries in all represented countries except Laos (75.0%) and Nepal (56.6%). Meropenem coverage was generally highest and was greater than 80% in Cambodia, Indonesia, Pakistan, and Vietnam, but lower than 80% in China, Laos, and Nepal and as low as 64.0% in India. Considerable between- country differences were observed for all 3 regimens, even for countries bordering each other, such as Cambodia, Laos, Thailand, and Vietnam. Coverage estimates are clinically highly relevant for the development of local and national empirical treatment guidelines, incorporating both the relative incidence of bacteria and their susceptibility. This concept has not, to our knowledge, been previously applied to neonatal sepsis in LMICs. Instead, reports have focused on susceptibility for individual pathogen-drug combinations, 4,6,7 an approach that does not directly incorporate the spectrum of causative bacteria. One important question is whether global setting-independent recommendations for empirical neonatal sepsis treatment can be supported in an era of changing and highly variable epidemiology. In some settings, difficult-to-treat pathogens and multidrug-resistant isolates now contribute considerably to neonatal sepsis. Stratified guidance moving between recommended regimens according to microbiology and coverage by patient-level factors (eg, presence of certain underlying conditions or timing of sepsis onset) or setting, may be a solution. One challenge will be the lack of defined coverage thresholds to move between regimens. Given sufficiently large data sets, coverage estimates could help inform such shifting by supporting inferences about true differences between regimens. Limitations This study has some limitations. Our coverage estimates were based on data from predominantly university or teaching hospitals. Infants with complex medical issues and those at higher risk of nosocomial bloodstream infections may, therefore, be overrepresented. At the same time, microbiology data from infants managed in district hospitals are lacking precluding confirmation that presented coverage estimates are applicable to them as well. Clinicians applying WHO recommendations to infants with nosocomial infection or those managed in tertiary hospitals would, on the basis of our observations, need to consider alternatives for this population. We chose to estimate coverage according to the pathogens frequently reported across included studies, which are likely to be associated with severe neonatal sepsis and the so-called ESKAPE organisms (ie, Enterococcus faecium, S aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa), which are known to be problematic in terms of emerging antimicrobial resistance. Inclusion of other pathogens would be expected to have a variable influence on the expected coverage of considered antibiotics, leading to either higher or lower estimates. This may be particularly important in individual hospitals with ongoing outbreaks where a single bacterial strain is dominant. In such situations, regional coverage estimates may not be applicable. Coverage estimation requires a number of assumptions to be made when calculating the susceptibility parameters, such as the incorporation of intrinsic resistance, extrapolations from susceptibility testing for 1 representative of an antibiotic class to other members of this class, and the interpretation of multiple testing for 1 antibiotic class. We based our calculations of regimen susceptibility on EUCAST algorithms and, whenever possible, used susceptibility testing information for the specific drug of interest. Importantly, however, all included studies used versions of Clinical and Laboratory Standards Institute interpretive criteria, which may diverge from EUCAST in terms of both break points and assumptions about intrinsic resistance. Debate about the merits and challenges of switching from Clinical and Laboratory Standards Institute to EUCAST and about the implications of such a transition for interpretation of routine data in the context of surveillance 23,24 is ongoing. To support coverage estimation, it is important that the microbiological data used are collected in equivalent ways. However, the data used for this analysis may have been subject to various random or systematic errors that could bias the coverage estimates. Possible sources of error include duplicate isolates, contaminants, nonstandardized susceptibility testing, combining data from JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 10/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries different patient populations (children and adults), and reflex susceptibility testing based on resistance identified in a first-line testing panel. These requirements have important implications for global surveillance initiatives, such as the Global Antimicrobial Resistance Surveillance System, if data collected are to be used at the interface between surveillance and clinical practice. Conclusions Recently, machine learning approaches and more elaborate multivariable Bayesian models using clinical and demographic information combined with microbiological data have been proposed as 27,28 optimizing the selection of empirical antibiotic treatment for sepsis. Although these models may help in selecting patient-adapted regimens, the approach used in our study only requires estimates of pathogen incidence and susceptibility and could already substantially improve clinical decision- making based on routine microbiological data alone, provided that the data used to produce these estimates are of sufficient quality. Our analysis indicates that the recommendation for third- generation cephalosporin monotherapy as a second-line regimen may no longer be valid for many infants receiving treatment for neonatal sepsis in several Asian countries. Our findings could explain 14,29 the high reported empirical meropenem use in this population in Asia. Evaluation of potential alternatives will be essential to reducing consumption of last-resort antibiotics for the empirical treatment of neonatal sepsis in settings with a high prevalence of antimicrobial resistance. ARTICLE INFORMATION Accepted for Publication: December 13, 2019. Published: February 12, 2020. doi:10.1001/jamanetworkopen.2019.21124 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Bielicki JA et al. JAMA Network Open. Corresponding Author: Julia A. Bielicki, MD, Paediatric Infectious Diseases Research Group, Institute of Infection and Immunity, St George’s University of London, Jenner Wing, Level 2, Room 2.215E, Cranmer Terrace, London SW17 0RE, United Kingdom (jbielick@sgul.ac.uk). Author Affiliations: Paediatric Infectious Diseases Research Group, Institute of Infection and Immunity, St George’s University of London, London, United Kingdom (Bielicki, Sharland, Heath); Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom (Bielicki, Cromwell); Paediatric Pharmacology and Paediatric Infectious Diseases, University of Basel Children’s Hospital, Basel, Switzerland (Bielicki); Medical Research Council Clinical Trials Unit at University College London, London, United Kingdom (Walker); Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India (Agarwal); Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom (Agarwal, Turner); Cambodia Oxford Medical Research Unit, Siem Reap, Cambodia (Turner). Author Contributions: Dr Bielicki had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Bielicki, Sharland, Heath, Cromwell. Acquisition, analysis, or interpretation of data: Bielicki, Walker, Agarwal, Turner, Cromwell. Drafting of the manuscript: Bielicki, Heath. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Bielicki. Supervision: Sharland, Heath, Walker, Cromwell. Conflict of Interest Disclosures: Dr Bielicki reported that her spouse is senior corporate counsel at Novartis International AG and holds Novartis stock and stock options. No other disclosures were reported. REFERENCES 1. Seale AC, Blencowe H, Manu AA, et al; pSBI Investigator Group. Estimates of possible severe bacterial infection in neonates in sub-Saharan Africa, south Asia, and Latin America for 2012: a systematic review and meta- analysis. 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Wellcome Open Res. 2018;3:131. doi:10.12688/ wellcomeopenres.14847.1 29. Hsia Y, Lee BR, Versporten A, et al; GARPEC and Global-PPS networks. Use of the WHO Access, Watch, and Reserve classification to define patterns of hospital antibiotic use (AWaRe): an analysis of paediatric survey data from 56 countries. Lancet Glob Health. 2019;7(7):e861-e871. doi:10.1016/S2214-109X(19)30071-3 SUPPLEMENT. eAppendix. Supplemental Methods eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest eFigure 2. Flow Chart: Systematic Review of the Literature eReferences. eTable 1. Description of Included Publications eTable 2. Information on Sample Processing Provided in Included Publications eTable 3. Relative Incidence of Bacteria in Included Studies JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 13/13 Supplementary Online Content Bielicki JA, Sharland M, Heath PT, et al. Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries. JAMA Netw Open. 2020:3(2):e1921124. doi:10.1001.jamanetworkopen.2019.21124 eAppendix. Supplemental Methods eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest eFigure 2. Flow Chart: Systematic Review of the Literature eReferences. eTable 1. Description of Included Publications eTable 2. Information on Sample Processing Provided in Included Publications eTable 3. Relative Incidence of Bacteria in Included Studies This supplementary material has been provided by the authors to give readers additional information about their work. © 2020 Bielicki JA et al. JAMA Network Open. eAppendix. Supplemental Methods Search strategy for systematic literature review Ovid MEDLINE® 1946 to April 25 2019 1 exp SEPSIS/ or exp NEONATAL SEPSIS/ 2 exp BACTEREMIA 3 bacter?emia.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 4 (blood?stream adj3 infect*).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 5 (blood adj2 culture adj2 (positive* or isolat*)).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 6 1 or 2 or 3 or 4 or 5 7 ((anti?biotic* or anti?infect* or anti?microb*) adj2 (resist* or suscep* or sensitive*)).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 8 exp Drug Resistance, Microbial/ 9 7 or 8 10 exp infant/ or exp infant, newborn/ 11 (infant* or neonat* or new?born).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 12 10 or 11 13 6 and 9 and 12 14 Exp ASIA/ 15 13 and 14 16 - Embase 1974 to 2019 Week 16 1 exp bacteremia/ 2 exp sepsis/ or newborn sepsis/ 3 bacter?emia.mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 4 (blood?stream adj2 infect*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 5 (blood adj2 culture adj2 (positive* or isolate*)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 6 1 or 2 or 3 or 4 or 5 7 ((anti?biotic* or anti?infect* or anti?microb*) adj2 (resist* or suscep* or sensitiv*)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 8 exp antibiotic resistance/ 9 7 or 8 10 infant/ 11 newborn/ 12 (infant or new?born or neonat*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 13 10 or 11 or 12 14 6 and 9 and 13 15 14 16 14 and 15 © 2020 Bielicki JA et al. JAMA Network Open. 17 limit - Systematic review of the literature: selection of publications Studies were eligible for inclusion if they examined blood culture isolates and (i) provided information specific to newborns up to 28 days of age or infants managed on neonatal units, (ii) reported on the relative incidence of different bacteria at species or genus level during the indicated surveillance period and (iii) included data on antimicrobial resistance for at least one bacterial species or genus. Publications reporting on isolates from sources other than blood, and those from which data for neonatal blood cultures (e.g. reporting pooled data across age groups) could not be extracted were excluded. Equally studies focusing on single organisms from which the relative incidence of other bacteria could not be obtained were excluded. Further we excluded studies presenting only aggregate data by region or internationally. After exclusion of duplicates, titles or abstracts of retrieved studies were reviewed by one author (JB) to identify those meeting inclusion criteria. A random subset of retrieved studies was reviewed by a second author (MS) to ensure consistency in selection based on the pre-specified inclusion and exclusion criteria with no disagreements. Selected publications were primarily used to inform parameter estimation for calculating coverage. Additional extracted data included contextual information (namely the year of publication, the country from where the data originated, the surveillance/reporting period, and the number and type of hospitals surveyed), and whether studies reported on blood culture isolates from community-acquired infections, hospital-acquired infections or both. Early onset of neonatal sepsis defined as infection occurring in the first 3 days of life was considered a community-acquired infection. We also extracted information on approaches to species identification, susceptibility testing and evaluation of testing results, if provided. Species identification and susceptibility testing results were recorded as reported. As the study was focused on the reporting of routine microbiological or surveillance data, we did not undertake a formal grading of the quality of the studies or an evaluation of the appropriateness of microbiological approaches. Assumptions for determining susceptibility of pathogens to pre-specified regimens Aminopenicillin susceptibility was based on either ampicillin or amoxicillin susceptibility testing results, whichever was available. Gentamicin susceptibility was based on results for gentamicin rather than other aminoglycosides whenever possible, because susceptibility to gentamicin cannot be reliably inferred from results for other aminoglycosides. If no gentamicin susceptibility data were provided, data from other aminogylcosides (mostly amikacin) were used. Third-generation cephalosporin susceptibility was based on either cefotaxime or ceftriaxone, whichever was available. Meropenem susceptibility was based on results for meropenem rather than other carbapenems whenever possible, because susceptibility to meropenem cannot be reliably inferred from results for other carbapenems. If no meropenem susceptibility data were provided, data from other carbapenems (mostly imipenem) were used. For Staphylococcus aureus, third-generation cephalosporin and meropenem susceptibility was derived from information on methicillin resistance, as these antibiotics are not generally specifically tested for S. aureus. For the combined regimen (i), the one with the higher susceptibility was taken to reflect overall susceptibility. For example, if Escherichia coli in a specific country exhibited 20% ampicillin susceptibility and 70% gentamicin susceptibility, susceptibility to aminopenicillin plus gentamicin for E. coli was assumed to be 70%. Technical appendix on calculation of the weighted-incidence syndromic combination antibiogram (WISCA) In the WISCA decision tree, the first square node represents the clinical decision to start empiric antibiotic therapy and the regimen choices. Subsequent circular nodes and branches describe chance events, which are the range of relevant bacteria causing neonatal sepsis, their relative incidence and the percentages of each pathogen susceptible to each antibiotic regimen. Combining the probabilities along the regimen tree branches provides an estimate of coverage for each regimen. A difficulty in adopting a Bayesian perspective is the specification of the prior distributions for the parameters. The value of the relative incidence and pathogen regimen susceptibility parameters for each regimen were therefore defined as probability distributions that reflected the uncertainty in their value. Given that susceptibility percentages are simple proportions, we selected a binomial distribution to describe our prior belief defined using the conjugate Beta distribution. This approach results in the posterior also being a Beta distribution. The relative incidence data were assumed to be drawn from a multinomial distribution with nine possible outcomes. The prior was accordingly distribution, and is the generalisation of the Beta distribution to situations described by more than two categories. © 2020 Bielicki JA et al. JAMA Network Open. means that the posterior distribution is largely determined by the observed data. Using the Dirichlet distribution as the prior, for example, results in the posterior taking the form Dirichlet (1+n1, 1+n2,. . ., 1+n9). Equally, in most cases, when there were no strong prior beliefs about pathogen-regimen susceptibility, the non-informative prior beta(1,1) was used. Adopting a Bayesian perspective allows the use of informative priors for the situation in which a pathogen has intrinsic resistance or is assumed to be fully susceptible. For these, we chose a pragmatic posterior Beta distribution, chosen to have an appropriate standard deviation. For example, susceptibility for a pathogen with intrinsic resistance was specified as a Beta(1,9999), which has a standard deviation of 0.01%. Sampling from this distribution only gives pathogen resistance below 99.9% in 1 in 20000 draws. The calculation of the 95% credible interval describing the precision of coverage estimates requires Monte Carlo simulation, which involves running a large number of experiments (in our case 1000) and combining their results. In each experiment, parameter values for the parameters of interest (relative incidence and pathogen-regimen susceptibility) are randomly drawn from their specified distributions. The values of each parameter are then combined to derive a coverage estimate. Together, the individual coverage estimates from all the experiments give the posterior the interval between 2.5% and 97% percentile of this distribution. Analytical steps for basic WISCA coverage estimation using a Bayesian decision tree model. 1. Identify the total number of isolates contributing to the infection syndrome of interest for a given setting and period. 2. Select from 1. clinically relevant bacteria contributing to the infection syndrome and with data available to define model parameters. 3. Specify assumptions used for determining susceptibility to the regimen, including extrapolation from standard bug-drug susceptibility testing, definitions of intrinsic resistance and, when relevant, intrinsic susceptibility (corresponding to unusual resistance phenotypes) 4. For the bacteria specified in 2. identify the number of isolates contributed by each (to determine relative frequency = first circular node and branches) and the number of isolates tested for and susceptible to the regimen of interest (second circular node and branches). 5. Select appropriate informative priors for bacteria with intrinsic resistance or expected susceptibility as set out in 3. 6. Select non-informative priors for relative bacterial incidence and susceptibility with the exceptions as outlined in 5. 7. Use appropriate probability distributions to reflect uncertainty in the relative frequency of bacteria (multinomial, Dirichlet distribution) and susceptibility to the regimen (binomial, Beta distribution). 8. Model coverage by running a Monte Carlo simulation with n experiments sampling parameter values for relative bacterial frequency and regimen susceptibility from their specified distributions. 9. Combine estimates from n experiments to calculate coverage estimates with their 2.5% and 97% percentiles, corresponding to the 95% uncertainty or credible interval. 10. Repeat this process for each regimen of interest, noting that for comparisons within a given setting the bacteria included in the WISCA should stay the same (meaning that number of isolates contributed by each will be the same), but that the number tested and susceptible will vary by regimen. © 2020 Bielicki JA et al. JAMA Network Open. eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest ET: empiric therapy. Square node: clinical decision to treat; circular node: chance event (causal bacteria and their regimen susceptibility). The decision tree is shown for illustration only, and dashed lines indicate where the decision tree has been left incomplete. All branches are included in the WISCA calculations to estimate coverage. © 2020 Bielicki JA et al. JAMA Network Open. eFigure 2. Flow Chart: Systematic Review of the Literature © 2020 Bielicki JA et al. JAMA Network Open. eReferences: Reference list for included publications 1. Abu NA, Nor FM, Mohamad M, et al. Community-acquired Bacteremia in paediatrics: Epidemiology, aetiology and patterns of antimicrobial resistance in a tertiary care centre, Malaysia. Medical Journal of Malaysia 2016; 71(3): 117-21. 2. Adhikari N, Shah PK, Acharya G, Vaidya KM. 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Pakistan Journal of Medical and Health Sciences 2016; 10(4): 1129-31. © 2020 Bielicki JA et al. JAMA Network Open. 26. Marwah P, Chawla D, Chander J, Guglani V, Marwah A. Bacteriological profile of neonatal sepsis in a tertiary-care hospital of Northern India. Indian pediatrics 2015; 52(2): 158-9. 27. Mehta A.M., Navinchandra M. K., Tukaram K.P. Microbial Profile of Neonatal septicaemia in a tertiary care hospital of Bhopal. International Journal of Biomedical and Advance Research 2014; 5(10): 499-501. 28. Muley VA, Ghadage DP, Bhore AV. Bacteriological profile of neonatal septicemia in a tertiary care hospital from Western India. Journal of Global Infectious Diseases 2015; 7(2): 75-7. 29. Mustafa M., Ahmed S.L. Bacteriological profile and antibiotic susceptiblity patterns in neonatal septicemia in view of emerging drug resistance. Journal of Medical and Allied Sciences 2014; 4(1): 2-8. 30. Nayak S, Rai R, Kumar V, Sanjeev H, Pai A, Ganesh H. Distribution of microorganisms in neonatal sepsis and antimicrobial susceptibility patterns in a tertiary care hospital. Archives of Medicine and Health Sciences 2014; 2(2): 136-9. 31. Pandita N, Wasim S, Bhat NK, Chandra V, Kakati B. Identification of the bacterial isolates in neonatal septicaemia and their antimicrobial susceptibility in a tertiary care hospital in Uttarakhand, India: a retrospective study. 2016 2016; 3(1): 6. 32. Panigrahi P, Chandel DS, Hansen NI, et al. Neonatal sepsis in rural India: Timing, microbiology and antibiotic resistance in a population-based prospective study in the community setting. Journal of Perinatology 2017; 37(8): 911-21. 33. Patel D, Nimbalkar A, Sethi A, Kungwani A, Nimbalkar S. Blood culture isolates in neonatal sepsis and their sensitivity in Anand District of India. Indian journal of pediatrics 2014; 81(8): 785-90. 34. Pavan Kumar DV, Mohan J, Rakesh PS, Prasad J, Joseph L. Bacteriological profile of neonatal sepsis in a secondary care hospital in rural Tamil Nadu, Southern India. Journal of family medicine and primary care 2017; 6(4): 735-8. 35. Pokhrel B, Koirala T, Shah G, Joshi S, Baral P. Bacteriological profile and antibiotic susceptibility of neonatal sepsis in neonatal intensive care unit of a tertiary hospital in Nepal. BMC Pediatrics 2018; 18: 208. 36. Ponugoti ML, Venkatakrishna M, Jithendra K, Reddy PS. Incidence of Early Onset Septicemia, Isolation and Resistant Patterns of Causative Organisms: A Study in a Tertiary Care Hospital A.P. Journal of Medical Science and Clinical Research 2015; 3(2): 4227-32. 37. Roy MP, Bhatt M, Maurya V, Arya S, Gaind R, Chellani HK. Changing trend in bacterial etiology and antibiotic resistance in sepsis of intramural neonates at a tertiary care hospital. Journal of postgraduate medicine 2017; 63(3): 162-8. 38. Sarangi KK, Pattnaik D, Mishra SN, Nayak MK, Jena J. Bacteriological profile and antibiogram of blood culture isolates done by automated culture and sensitivity method in a neonatal intensive care unit in a tertiary care hospital in Odisha, India. 2017 2017; 2(4): 6. 39. Sari IP, Nuryastuti T, Wahyono D. The study of multidrug-resistance in neonatal intensive care unit at the central java hospital. Asian Journal of Pharmaceutical and Clinical Research 2017; 10(Special Issue may): 80-4. 40. Singh HK, Sharja P, Onkar K. Bacteriological profile of neonatal sepsis in neonatal intensive care unit (NICU) in a tertiary care hospital: prevalent bugs and their susceptibility patterns. European Journal of Pharmaceutical and Medical Research 2016; 3(3): 241-5. 41. Thakur S, Thakur K, Sood A, Chaudhary S. Bacteriological profile and antibiotic sensitivity pattern of neonatal septicaemia in a rural tertiary care hospital in North India. Indian Journal of Medical Microbiology 2016; 34(1): 67-71. 42. Ting YT, Lu CY, Shao PL, et al. Epidemiology of community-acquired bacteremia among infants in a medical center in Taiwan, 2002-2011. Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2015; 48(4): 413-8. 43. Tran HT, Doyle LW, Lee KJ, Dang NM, Graham SM. A high burden of late-onset sepsis among newborns admitted to the largest neonatal unit in central Vietnam. Journal of Perinatology 2015; 35(10): 846-51. 44. Tudu NK, Dey R, Bhattacharya I, Roy S, Dey JB. A pilot study on bacterial profile of neonatal sepsis in a tertiary care hospital serving rural population. Journal of Evolution of Medical and Dental Sciences 2014; 3(23): 6378-81. 45. Ullah O, Khan A, Ambreen A, et al. Antibiotic Sensitivity pattern of Bacterial Isolates of Neonatal Septicemia in Peshawar, Pakistan. Arch Iran Med 2016; 19(12): 866-9. 46. Venkatnarayan K, Bej PK, Thapar RK. Neonatal Sepsis: A Profile of a Changing Spectrum. J Nepal Paediatr Soc 2014; 34(3): 207-14. 47. Wang S, Chen S, Feng W, et al. Clinical characteristics of nosocomial bloodstream infections in neonates in two hospitals, China. Journal of Tropical Pediatrics 2018; 64(3): 231-6. 48. Yadav NS, Sharma S, Chaudhary DK, et al. Bacteriological profile of neonatal sepsis and antibiotic susceptibility pattern of isolates admitted at Kanti Children's Hospital, Kathmandu, Nepal. BMC research notes 2018; 11(1): 301. © 2020 Bielicki JA et al. JAMA Network Open. eTable 1. Description of Included Publications Publication year, First author, Journal Country, City/Town N hospitals, type Observation period start and Infections surveyed end* 2014 Adhikari Nepal Medical Nepal Thapathali 1 Maternity 01Aug11 31Mar12 Sepsis with positive College Journal BC Anderson Journal of Tropical Laos Vientiane 1 U/T 01Feb00 01Sep11 Sepsis with positive Pediatrics BC Javali Journal of Evidence India Raichur 1 NT/D 01Jun13 30Jul13 LONS with positive Based Medicine & BC Healthcare Khanal Journal of Nepal Nepal Kathmandu 1 Maternity 01Dec10 31Mar11 Sepsis with positive Paediatric Society BC Mehta International Journal India Bhanpur 1 U/T 01Jul12 31Dec13 Sepsis with positive of Biomedical And BC Advance Research Mustafa Journal of Medical and India Hyderabad 1 U/T Unknown (1 year) Sepsis with positive Allied Sciences BC Nayak Archives of Medicine India Deralakatte 1 U/T 01Jun11 31May12 Sepsis with positive and Health Sciences BC Patel The Indian Journal of India Karamsad 1 NT/D 01Nov07 31Oct11 Bacteraemia Pediatrics Tudu Journal of Evoluation India Kenduadihi 1 U/T 01Jun13 31Aug13 Sepsis with positive of Medical and Dental BC Science Venkatna Journal of Nepal India Pune 1 U/T 01Jan11 01Jul12 Sepsis with positive rayan Paediatric Society BC 2015 Agarwal Journal of India Mangalore 1 U/T 01Feb14 31Jul14 Sepsis with positive International Medicine BC and Dentistry Ambade Journal of Medical India Dhule 1 U/T 01Aug12 31Jul14 Sepsis with positive Science and Clinical BC Research Chapagai Journal of the Nepal Kathmandu 1 Paediatric 01Aug14 01Aug15 Sepsis with positive n Nepalese Health BC Research Council Dhanalak Journal of Clinical and India Madurai 1 U/T 01Dec13 30Sep2014 Sepsis with positive shmi Diagnostic Research BC Gupta International Journal India Rohtak 1 NT/D Unknown (1year) Bacteraemia of Pharma and Bio Sciences Kamble International Journal India Ambajogai 1 U/T 01Jun08 21Dec10 Sepsis with positive of Current BC © 2020 Bielicki JA et al. JAMA Network Open. Microbiology and Applied Sciences Madavi International Journal India Nagpur 1 U/T 01Aug11 01Sep13 Sepsis with positive of Current Research BC and Review Marwah Indian Pediatrics India Chandigarh 1 U/T 01Jan08 31Dec12 Bacteraemia Muley Journal of Global India Pune 1 NT/D Unknown Bacteraemia Infectious Diseases Ponugoti Journal of Medical India Nellore 1 U/T Unknown (6 months) Sepsis with positive Science And Clinical BC Research Sarangi International Journal India Bhubaneswar 1 U/T 01Nov12 30Apr14 Sepsis with positive of Advances in BC Medicine Ting Journal of Republic Taipei 1 U/T 01Jan02 31Dec11 CA bacteraemia, Microbiology, of limited to 0-7 day- Immunology and (Taiwan) olds Infection Tran Journal of Vietnam Da Nang 1 Maternity/ 01Nov10 31Oct11 Sepsis with positive Perinatology Paediatric BC 2016 Abu Medical Journal of Malaysia Baru 1 U/T 01Jan01 31Dec11 CA bacteraemia Malaysia Selayang excluding EOS Amin International Journal India Vadodara 1 U/T 01Apr13 30Sep13 Sepsis with positive of Pharmaceutical BC Sciences and Research DeNIS Lancet Global Health India Delhi 3 U/T 18Jul11 28Feb14 Sepsis with positive BC Jiang Internal Medicine China Missing 1 Maternity/ 01Jan08 31Dec12 Sepsis with positive Paediatric BC Lu Journal of Pediatrics China Chongqing 1 Paediatric 01Jan90 31Dec14 Sepsis with positive and Child Health BC Mahmood Pakistan Journal of Pakistan Faisalabad 1 U/T 01Jan13 01Jan15 Bacteraemia Medical and Health Sciences Pandita International Journal India Dehradun 1 U/T 01Jan13 30Jun15 Sepsis with positive of Contemporary BC Pediatrics Singh European Journal of India Raipur 1 U/T 01Jan13 31Dec13 Sepsis with positive Pharmaceutical and BC Medical Research Thakur Indian Journal of India Tanda 1 NT/D 01Apr12 31Mar13 Sepsis with positive Medical Microbiology BC © 2020 Bielicki JA et al. JAMA Network Open. Ullah Archives of Iranian Pakistan Peshawar 1 U/T 01Jan12 31Dec15 Bacteraemia Medicine 2017 Dalal International Journal India Rohtak 1 U/T 01Jul10 30Sep13 Sepsis with positive of Research in BC Medical Sciences Dong BMC Pediatrics China Bengbu 1 NT/D 01Jan10 31Aug14 Sepsis with positive BC Ingale International Journal India Pune 1 U/T Unknown (1 year) Sepsis with positive of Contemporary BC Pediatrics Kanodia Journal of College of Nepal Dharan 1 U/T 01Jan14 31Dec14 Sepsis with positive Medical Sciences BC Nepal Panigrahi Journal of India Multiple in 2 NT/D 01Apr02 31Mar05 Invasive bacterial Perinatology area of infections Odisha Pavan Journal of Family India Dindigul 1 NT/D 01Oct13 30Sep15 Sepsis with positive Medicine and Primary BC Care Roy Journal of India New Delhi 1 U/T 01Jan11 31Dec14 Bacteraemia Postgraduate Medicine Sari Asian Journal of Indonesi Yogyakarta 1 U/T 01Jan14 31Dec15 Bacteraemia Pharmaceutical and a Clinical Research 2018 Dhaneria Diseases India Ujjain 1 U/T 01Jun12 31Jan14 Nosocomial bacteraemia, including EONS and LONS Fox- Emerging Infectious Cambodi Siem Reap 1 Paediatric 01Jan07 31Dec16 Invasive bacterial Lewis Diseases a infections Jajoo PloS One India Delhi 1 NT/D 01Jul11 31Jan15 Sepsis with positive BC Pokhrel BMC Pediatrics Nepal Lalitpur 1 U/T 15Apr14 15Apr17 Sepsis with positive BC Wang Journal of Tropical China Chongqing, 2 U/T 01Jan03 31Dec13 Nosocomial Pediatrics Henan bacteraemia Yadav BMC Research Notes Nepal Kathmandu 1 Paediatric 01Apr15 30Sep15 Sepsis with positive BC 2019 Li Medicine China Shanghai 1 U/T 01Jan13 31Aug17 Sepsis with positive BC U/T hospital: University/Tertiary hospital; NT/D hospital: Non-teaching/District hospital *Start year of data collection for all studies with exception of Lu et al, 2016 in the 2000s, end year for all studies in the 2000s. © 2020 Bielicki JA et al. JAMA Network Open. eTable 2. Information on Sample Processing Provided in Included Publications Publication year, First author Species identification Antibiotic susceptibility Interpretive guidelines Other comments testing method 2014 Adhikari Yes (Standard Yes (Disc diffusion) Yes (CLSI M2-A9, 2006) bacteriological techniques) Anderson No details provided Yes (Disc diffusion) Yes (CLSI M100-S20, 2010) ESBL detection by (standard blood culture) cefpodoxime screening with confirmation by CLSI-recommended disc diffusion methods Javali No details provided Yes (Disc diffusion) Yes (CLSI, 2008) (standard blood culture) Khanal Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S16, 2007) bacteriological techniques) Methta Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S18, 2010) Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing Mustafa Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) ESBL confirmation by bacteriological techniques) phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion) Nayak Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Use of control strains bacteriological techniques) Meropenem SIR based on imipenem susceptibility testing Patel Yes (BacT/ALERT, API) Yes (automated API) No details provided Tudu Yes (BacT/ALERT, API) Yes (Disc diffusion) Yes (CLSI, no specified) Gentamicin SIR based on amikacin susceptibility testing, meropenem SIR based on imipenem susceptibility testing Venkatnara No details provided No details provided No details provided Gentamicin SIR based yan on amikacin susceptibility testing 2015 Agarwal Yes (BacT/ALERT, Vitek Yes (Disc diffusion) Yes (CLSI M02-A11, 2012) ESBL confirmed using II) CLSI-recommended disc diffusion methods, © 2020 Bielicki JA et al. JAMA Network Open. MRSA detection using cefoxitin disc Ambade Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) bacteriological techniques) Chapagain No details provided No details provided No details provided Gentamicin SIR based on amikacin susceptibility testing Dhanalaksh Yes (Standard Yes (Disc diffusion) No details provided mi bacteriological techniques) Gupta Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S24, 2014) Use of control strains bacteriological techniques) Kamble Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Extensive detail on bacteriological techniques) testing for ESBL and Metallo-beta-lactamases provided Meropenem SIR based on imipenem susceptibility testing Madavi No details provided No details provided No details provided Meropenem SIR based on imipenem susceptibility testing Marwah Yes (Standard No details provided Yes (CLSI, incorrect Meropenem SIR based bacteriological techniques) (standard methods) referencing) on imipenem susceptibility testing Muley Yes (standard Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) bacteriological techniques) Ponugoti Yes (standard Yes (Disc diffusion) Yes (CLSI M2A7 Vol.20 No1 Meropenem SIR based bacteriological techniques) & 2, 2000) on imipenem susceptibility testing Sarangi Yes (BacT/ALERT) Yes (automated API) No details provided Ting No details provided No details provided Yes (CLSI, not specified) Tran Yes (Standard Yes (Disc diffusion) No details provided Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing 2016 Abu Yes (API/Vitek) Yes (Disc diffusion) Yes (CLSI M100-S24) ESBL confirmation by phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion) Amin Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Microbiology laboratory bacteriological techniques) accredited by National Accreditation Board for © 2020 Bielicki JA et al. JAMA Network Open. Testing and Calibration Laboratory in India DeNIS Yes (Standard No details provided Yes (CLSI M100-S21 & Flowchart of sample bacteriological techniques) M100-S22 & M100-S23, handling provided in 2011-2013) web-extra material Jiang Yes (BacT/ALERT, Yes (Disc diffusion or Yes (CLSI, not specified) API/Vitek) Etests) Lu No details provided No details provided No details provided Results recorded based on routine laboratory testing Meropenem SIR based on imipenem susceptibility testing Mahmood No details provided No details provided No details provided Standard procedures for sample processing and interpretation Pandita Yes (Bactec/API) Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) Meropenem SIR based on imipenem susceptibility testing Singh Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S18, 2008) Gentamicin SIR based bacteriological techniques) on amikacin susceptibility testing Thakur Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) Use of control strains, bacteriological techniques) MRSA screening using cefoxitin disc, ESBL screening using ceftazidime disc, confirmation of ESBL by double disc synergy test Meropenem SIR based on imipenem susceptibility testing Ullah Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing 2017 Dalal No details provided Yes (Disc diffusion) No details provided Meropenem SIR based (standard blood culture) susceptibility testing Dong Yes (BacT/ALERT) Yes (Disc diffusion) No details provided Additional information on species identification © 2020 Bielicki JA et al. JAMA Network Open. and susceptibility testing provided in methods Ingale Yes (Bactec/API) Yes (Disc diffusion) Yes (CLSI M100-S23, 2013) Extensive detail on microbiological sample handling provided Kanodia No details provided Yes (Disc diffusion) No details provided Panigrahi Yes (Bactec/API) No details provided Yes (CLSI M23-A2, 2001) Extensive detail on microbiological sample handling provided Meropenem SIR based on imipenem susceptibility testing Pavan Yes (Bactec/API) Yes (automated API) No details provided Roy Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S19, 2009) Extensive detail on bacteriological techniques) microbiological sample handling provided; ESBL confirmation by phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion); Use of control strains; MRSA screening using oxacillin disc Gentamicin SIR based on amikacin susceptibility testing Sari Yes (Vitek) Yes (Disc diffusion) No details provided 2018 Dhaneria Yes (Standard Yes (Disc diffusion, Yes (CLSI M100-S21, 2011) Extensive detail on bacteriological techniques) confirmation using microbiological sample Vitek 2) handling provided Meropenem SIR based on imipenem susceptibility testing Fox-Lewis Yes (Standard Yes (Disc diffusion or Yes (CLSI, 2012) Meropenem SIR based bacteriological techniques) Etests) on imipenem susceptibility testing Jajoo Yes (Bactec/Vitek) No details provided Yes (CLSI M100-S21 & Aminoglycosides and M100-S22 & M100-S23, carbapenems grouped in 2011-2013) susceptibility reporting Pokhrel Yes (Bactec) Yes (Disc diffusion) Yes (CLSI M100-S24, 2014) © 2020 Bielicki JA et al. JAMA Network Open. Wang Yes (Vitek/API) Yes (Disc diffusion) Yes (CLSI, 2015) Use of control strains, ESBL screening using ceftazidime disc, confirmation of ESBL by combination discs Meropenem SIR based on imipenem susceptibility testing Yadav Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S23, 2014) Use of control strains bacteriological techniques) 2019 Li No details provided Yes (Disc diffusion) Yes (CLSI, not specified) CLSI: Clinical and Laboratory Standards Institute; ESBL: extended-spectrum beta-lactamases; MRSA: methicillin-resistant Staphylococcus aureus © 2020 Bielicki JA et al. JAMA Network Open. eTable 3. Relative Incidence of Bacteria in Included Studies Publication year, Bacteria reported in studies (% incidence within study shown) First author 2 Adhikari 94 57 27 4 1 11 43 100 Anderson* 75 3 3 11 5 4 12 1 1 1 3 49 3 3 1 85 85 Javali 32 9 34 13 19 9 9 6 50 77 Khanal 61 77 10 4 2 7 23 100 Mehta 169 5 2 9 4 5 14 5 56 89 98 Mustafa 62 11 23 35 7 24 89 100 Nayak 67 20 3 5 4 3 31 4 20 82 96 Patel* 249 5 12 10 10 2 47 6 1 6 81 93 Tudu 22 5 9 18 9 5 55 91 95 Venkatnarayan 15 13 20 13 7 47 80 92 2 Agarwal* 34 15 9 24 3 27 21 90 100 Ambade 119 6 10 14 35 13 22 90 100 Chapagain 30 7 7 3 3 80 90 97 Dhanalakshmi 41 10 10 68 5 7 85 94 Gupta 325 12 5 13 8 2 8 13 20 20 83 94 Kamble 71 14 1 17 7 1 6 23 21 7 1 79 98 Madavi 103 19 1 16 6 1 7 22 17 5 <1 1 5 77 92 Marwah 167 15 7 15 47 16 84 84 Muley 48 10 6 17 35 8 23 93 100 Ponugoti 188 2 3 15 22 19 1 25 2 12 83 97 Sarangi 74 3 5 62 11 8 8 3 30 79 Ting* 36 31 3 8 42 17 34 34 Tran 75 23 31 3 8 24 5 5 1 68 99 2 Abu* 29 21 3 3 3 21 35 3 7 3 62 63 Amin 101 23 4 12 13 28 8 13 97 100 DeNIS 998 22 15 14 4 6 17 7 1 12 1 82 98 Jiang* 131 1 1 43 19 6 5 13 3 1 6 1 1 50 88 Lu* 929 3 26 14 3 7 12 2 4 6 5 18 49 66 © 2020 Bielicki JA et al. JAMA Network Open. Total bacterial isolates Acinetobacter spp. Burkholderia spp. Citrobacter spp. CONS E. coli Enterobacter spp. Enterococcus spp. H. influenzae Klebsiella spp. L. monocytogenes Morganella spp. N. meningitidis Proteus spp. Pseudomonas spp. S. agalactiae S. aureus S. pneumoniae S. pyogenes Salmonella spp. Serratia spp. Other streptococci Others % accounted for by 7 target species % accounted for by 7 target species excluding CONS Mahmood 341 48 <1 17 9 26 <1 91 91 Pandita 124 6 6 26 11 6 2 27 3 8 1 4 63 85 Singh 141 5 27 4 50 8 7 96 100 Thakur* 188 1 4 19 5 5 10 15 2 40 76 93 Ullah 1534 2 53 7 6 13 20 <1 93 94 2 Dalal 356 15 4 12 1 2 4 47 12 93 100 Dong* 93 73 6 2 1 11 1 1 2 1 1 23 88 Ingale 48 13 25 2 6 10 29 13 2 75 100 Kanodia 327 14 1 2 3 3 4 1 6 62 3 93 96 Panigrahi* 56 14 2 52 20 12 88 88 Pavan 28 11 21 4 36 4 24 72 72 Roy* 2112 21 21 8 5 8 25 12 67 85 Sari 225 9 9 28 9 22 14 9 54 75 2 Dhaneria* 46 17 11 24 9 13 21 5 69 83 Fox-Lewis* 185 9 2 14 10 1 32 1 3 18 1 9 1 86 85 Jajoo 300 15 4 1 14 11 8 5 <1 18 <1 1 1 1 6 1 1 <1 <1 <1 12 64 75 Pokhrel* 69 12 20 4 19 33 3 2 4 2 2 73 90 Wang 571 39 18 3 17 5 2 16 43 70 Yadav 59 12 2 10 7 10 15 7 36 2 87 96 2 Li* 339 <1 44 10 1 6 9 <1 5 6 5 1 3 10 36 64 *a priori exclusion of contaminants with or without definitions for exclusion process provided includes A. baumannii, A. lwoffi includes E. cloacae includes K. pneumoniae, K. ornithinolytica, K. oxytoca, K. ozaenae includes P. aeruginosa includes S. marcescens, S. rubidaea © 2020 Bielicki JA et al. JAMA Network Open. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Network Open American Medical Association

Evaluation of the Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries

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References (31)

Publisher
American Medical Association
Copyright
Copyright 2020 Bielicki JA et al. JAMA Network Open.
eISSN
2574-3805
DOI
10.1001/jamanetworkopen.2019.21124
Publisher site
See Article on Publisher Site

Abstract

Key Points Question What is the antibiotic IMPORTANCE High levels of antimicrobial resistance in neonatal bloodstream isolates are being coverage offered by empirical neonatal reported globally, including in Asia. Local hospital antibiogram data may include too few isolates to sepsis treatment with aminopenicillin- meaningfully examine the expected coverage of antibiotic regimens. gentamicin, third-generation cephalosporins (cefotaxime or OBJECTIVE To assess the coverage offered by 3 antibiotic regimens for empirical treatment of ceftriaxone), and meropenem in Asian neonatal sepsis in Asian countries. countries? Findings In this decision analytical DESIGN, SETTING, AND PARTICIPANTS A decision analytical model was used to estimate coverage model based on a decision tree, 8376 of 3 prespecified antibiotic regimens according to a weighted-incidence syndromic combination isolates from 10 countries were used to antibiogram. Relevant data to parameterize the models were identified from a systematic search of estimate coverage. Meropenem Ovid MEDLINE and Embase. Data from Asian countries published from 2014 onward were of interest. generally had the highest coverage Only data on blood culture isolates from neonates with sepsis, bloodstream infection, or bacteremia (from 64.0% in India to 90.6% in reported from the relevant setting were included. Data analysis was performed from April 2019 to Cambodia) followed by aminopenicillin- July 2019. gentamicin (from 35.9% in Indonesia to 81.0% in Laos) and cefotaxime or EXPOSURES The prespecified regimens of interest were aminopenicillin-gentamicin, third- ceftriaxone (from 17.9% in Indonesia to generation cephalosporins (cefotaxime or ceftriaxone), and meropenem. The relative incidence of 75.0% in Laos); in all countries except different bacteria and their antimicrobial susceptibility to antibiotics relevant for determining Laos and Nepal, meropenem coverage expected concordance with these regimens were extracted. was higher than that of the other 2 regimens. MAIN OUTCOMES AND MEASURES Coverage was calculated on the basis of a decision-tree model incorporating relative bacterial incidence and antimicrobial susceptibility of relevant isolates. Data Meaning The findings suggest that on 7 bacteria most commonly reported in the included studies were used for estimating coverage, noncarbapenems may provide limited which was reported at the country level. empirical neonatal sepsis coverage in many Asian countries. RESULTS Data from 48 studies reporting on 10 countries and 8376 isolates were used. Individual countries reported 51 (Vietnam) to 6284 (India) isolates. Coverage varied considerably between Invited Commentary countries. Meropenem was generally estimated to provide the highest coverage, ranging from 64.0% (95% credible interval [CrI], 62.6%-65.4%) in India to 90.6% (95% CrI, 86.2%-94.4%) in Supplemental content Cambodia, followed by aminopenicillin-gentamicin (from 35.9% [95% CrI, 27.7%-44.0%] in Author affiliations and article information are Indonesia to 81.0% [95% CrI, 71.1%-89.7%] in Laos) and cefotaxime or ceftriaxone (from 17.9% [95% listed at the end of this article. CrI, 11.7%-24.7%] in Indonesia to 75.0% [95% CrI, 64.8%-84.1%] in Laos). Aminopenicillin- gentamicin coverage was lower than that of meropenem in all countries except Laos (81.0%; 95% CrI, 71.1%-89.7%) and Nepal (74.3%; 95% CrI, 70.3%-78.2%), where 95% CrIs for aminopenicillin- gentamicin and meropenem were overlapping. Third-generation cephalosporin coverage was lowest of the 3 regimens in all countries. The coverage difference between aminopenicillin-gentamicin and meropenem for countries with nonoverlapping 95% CrIs ranged from −15.9% in China to −52.9% in Indonesia. (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 1/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Abstract (continued) CONCLUSIONS AND RELEVANCE This study’s findings suggest that noncarbapenem antibiotic regimens may provide limited coverage for empirical treatment of neonatal sepsis in many Asian countries. Alternative regimens must be studied to limit carbapenem consumption. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 Introduction Although overall maternal and child mortality have substantially declined worldwide since the early 2000s, neonatal mortality associated with bacterial infection has remained high, with nearly half a million estimated annual deaths due to neonatal sepsis. Most of these deaths occur in low- and middle-income countries (LMICs), including many thousands in Asia. In a recent prospective cohort study of more than 13 500 live births in India, the case-fatality 4-7 rate of culture-positive neonatal sepsis episodes was nearly 50%. Recent systematic reviews indicate a high level of bacterial resistance to World Health Organization (WHO)–recommended empirical treatment regimens for serious neonatal and pediatric infections in LMICs, especially in bloodstream isolates. Globally, antimicrobial resistance is estimated to be implicated in up to one-third of neonatal sepsis deaths annually. Clinicians and guideline-setting bodies can be assisted in selecting optimal empirical antibiotic regimens by knowing the coverage of alternative regimens. Regimen coverage refers to the proportion of infection episodes that would be treated by the regimen at a stage when the causative pathogen is not yet known, therefore incorporating the frequencies of different causative bacteria and their resistance patterns. Several techniques are available to estimate coverage. One example is 9-11 the weighted-incidence syndromic combination antibiogram (WISCA), which estimates coverage by accounting for the relative incidence of different bacteria and their resistance patterns for a specific infection syndrome, in this case neonatal sepsis. Coverage can be estimated for both single- drug and combination treatment regimens. International guidelines provide recommendations for the empirical antibiotic treatment of neonatal bacterial infections and should aim to provide adequate coverage in target settings, especially LMICs. The objective of this decision analytical model study was, therefore, to evaluate the coverage offered by 3 prespecified antibiotic regimens according to WISCAs and focusing on Asia, a region with a high prevalence of bacterial resistance. Methods We estimated coverage using data on antimicrobial resistance that were used to create WISCAs for each country with reported data, as identified by a systematic review of the literature. Because only published data were used in the analysis, no formal ethical review was required according to guidance by the NHS Health Research Authority. This study follows the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline, because it is broadly applicable to any decision-model based analyses (eAppendix in the Supplement). Regimens Selected for Coverage Estimation The 3 regimens evaluated in this study were aminopenicillin-gentamicin (WHO-recommended first- line treatment; alternatives, benzylpenicillin or cloxacillin plus gentamicin), third-generation cephalosporins (WHO-recommended second-line treatment, assumed to be cefotaxime or ceftriaxone, not ceftazidime), and meropenem. The last regimen was evaluated because it has now been reported to be the most commonly used empirical treatment in LMICs for neonatal sepsis. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 2/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Identification of Relevant Data for Parameter Estimation A systematic search of the literature was conducted in Ovid MEDLINE and in Embase on January 23, 2019. Using both free-text and MeSH terms, publications on “sepsis” and “antibiotic resistance” and (“neonates” or infants”) and “Asia” were identified (eAppendix in the Supplement). Given increasing antimicrobial resistance, and to obtain contemporaneous estimates, we arbitrarily limited the search to articles published from 2014 onward. No additional limits were applied. Studies were reviewed against prespecified eligibility criteria, and data were extracted using a standardized prepiloted form implemented in REDCap (eAppendix in the Supplement). Extracted data for WISCA calculation included information on the total number of bacterial isolates from relevant blood cultures, the number of isolates of specific bacterial species or genera, the number of isolates tested for susceptibility to the antibiotics relevant for establishing coverage offered by the prespecified regimens of interest, and the number of isolates found to be susceptible to these antibiotics. We excluded bacteria known to frequently represent contamination rather than true infection, most importantly coagulase-negative staphylococci. The exclusion of coagulase- negative staphylococci is likely to result in the overestimation of coverage for β-lactam–based 17,18 regimens because of very high expected rates of methicillin resistance of 66% to more than 90%. Estimation of WISCA Parameters Tables containing the parameter values required for coverage estimation were created by country and regimen. The relative incidence parameters were based only on bacteria reported as contributing to neonatal sepsis in more than 50% of the eligible studies. This meant that estimated coverage was based on the most important and frequent pathogens identified in blood cultures from neonates in the target region. Including rare pathogens within the WISCA would have a minimal impact on the estimated coverage, and including those likely to be contaminants or unusual pathogens (potentially observed as part of unidentified outbreaks) could introduce substantial bias. For the bacteria identified in this way, their relative incidence was based on the frequency reported in the studies. Similarly, regimen susceptibility was derived directly from reported data with the number of tested isolates representing the denominator. Details of the assumptions for determining susceptibility of pathogens to each regimen are provided in the eAppendix in the Supplement. Statistical Analysis Regimen coverage was estimated using a previously described Bayesian WISCA. This approach has various advantages. It addresses the typical clinical approach of treating an infection syndrome, often with incomplete knowledge about the frequency of causative bacteria and their susceptibilities. The Bayesian WISCA also explicitly deals with intrinsic resistance and handles imprecision attributed to a small sample size or incomplete susceptibility testing data. In brief, the WISCA gives the expected levels of therapeutic coverage for an antibiotic regimen—in our case, regimens used to treat neonates with sepsis. The WISCA can be represented as a decision tree (eFigure 1 in the Supplement). Combining the probabilities along the regimen tree branches generates coverage estimates from relative bacterial incidence and proportions of each included pathogen susceptible to the antibiotic regimen. In essence, the WISCA is a weighted mean of the susceptibilities of the bacteria, with the weights defined by their relative incidence. The observed data on pathogen incidence and their susceptibility to the 3 regimens were combined with an appropriate Bayesian prior distribution that corresponded to our prestudy beliefs about these parameters. We had no strong prior belief about the relative incidence of the pathogens or for the majority of what level of susceptibility there might be within a country, and a noninformative prior was used in these cases. However, in some circumstances, specific pathogens were expected to have intrinsic resistance to the regimen and, consequently, not to have 19,20 susceptibility regardless of reported susceptibility testing results. In these situations, an informative prior was used to dominate the observed data. On the basis of European Committee for 19,20 Antimicrobial Susceptibility Testing (EUCAST) recommendations, enterococci, as well as JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 3/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Acinetobacter species and Pseudomonas species, were assumed to be intrinsically resistant to recommended third-generation cephalosporins and therefore not susceptible to third-generation cephalosporins. The value of the pathogen incidence and pathogen regimen-susceptibility parameters were defined as probability distributions to reflect the uncertainty in their respective values. The relative incidence of pathogens was modeled using a Dirichlet distribution, and the susceptibility parameters were defined as beta distributions; 95% credible intervals (95% CrIs) for the coverage estimates were calculated using Monte Carlo simulations, based on 1000 runs (eAppendix in the Supplement). All modeling was undertaken using Stata statistical software version 13.1 (StataCorp) and Excel spreadsheet software version 2010 (Microsoft Corp). Data analysis was performed from April 2019 to July 2019. Results Description of Data Set The literature review included data from 48 publications (eFigure 2 in the Supplement) representing 52 centers in 10 Asian countries (1 center in Cambodia, 5 in China, 33 in India, 1 in Indonesia, 1 in Laos, 1 in Malaysia, 6 in Nepal, 2 in Pakistan, 1 in Taiwan, and 1 in Vietnam). Of the 52 centers, 34 were university or tertiary hospitals, 10 were nonteaching or district hospitals (9 in India and 1 in China), and 8 were maternity or pediatric hospitals (1 in Cambodia, 2 in China, 4 in Nepal, and 1 in Vietnam). Ten articles were published in 2014, 13 in 2015, 10 in 2016, 8 in 2017, 6 in 2018, and 1 in 2019. For 32 of 48 publications, the observation period started in 2010 or later, with the earliest start date being January 1, 1990 (eTable 1 in the Supplement). Five publications did not report calendar dates for their observation period, but 4 of 5 indicated its duration. The median observation period was 2 years, with the shortest and longest periods being 2 months and 12 years, respectively. Most publications (33 of 48) reported on bloodstream isolates from neonates with clinical community-acquired or nosocomial sepsis. Another 12 publications based reporting on microbiologically defined bacteremia. Only 4 publications focused on either nosocomial or community-acquired infections (2 each). Reporting of information on sample processing, including species identification, antibiotic susceptibility testing methods, and interpretive guidelines, was variable (eTable 2 in the Supplement). Reported Bloodstream Isolates Individual publications included between 15 and 2112 isolates, with a median of 98 isolates (eTable 3 in the Supplement). The following bacteria were most frequently reported as contributing to neonatal sepsis or bacteremia: Escherichia coli (46 of 48 publications), Klebsiella species and Staphylococcus aureus (45 of 48 publications each), Pseudomonas species (35 of 48 publications), Acinetobacter species (32 of 48 publications), Enterobacter species (26 of 48 publications), and Enterococcus species (25 of 48 publications). In addition, coagulase-negative staphylococci were reported in 40 of 48 publications. All other bacteria, including Citrobacter species and Streptococcus agalactiae, were reported in less than one-half of the publications. On the basis of the prespecified criteria, E coli, Klebsiella species, S aureus, Pseudomonas species, Acinetobacter species, Enterobacter species, and Enterococcus species were selected for antibiotic regimen coverage estimation. Parameter Values: Isolates Reported and Susceptibility In total, 11 467 isolates were reported, with the greatest number coming from India (6284), China (2043), Pakistan (1875), and Nepal (640) (Table 1). Given the small number of reported isolates from Taiwan (36) and Malaysia (29), antibiotic regimen coverage was not estimated for these 2 countries. Most reported isolates (8584 of 11 467 [74.9%]) were from university or tertiary hospitals, with nonteaching or district hospitals contributing 11.5% (1319 of 11 467) and maternity or pediatric hospitals contributing another 13.6% (1564 of 11 467). JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 4/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries In total, 8376 isolates from 10 countries were used to estimate coverage. The proportion of reported isolates contributing to antibiotic regimen coverage estimation ranged from 91.9% (1723 of 1875) in Pakistan to 44.2% (905 of 2043) in China. Disregarding coagulase-negative staphylococci, the proportion of reported bacterial isolates contributing to coverage estimation ranged from 98.0% (51 of 52) in Vietnam to 69.5% (905 of 1302) in China. Availability of susceptibility testing information for aminopenicillin-gentamicin coverage ranged from 68.8% (623 of 905) in China to 100% in Indonesia (Table 2). For third-generation cephalosporins, this was available for 100% in Cambodia and Indonesia and 76.5% (39 of 51) in Vietnam (Table 3). For meropenem, available susceptibility testing information ranged from 100% in Indonesia to 60.3% (295 of 489) in Nepal (Table 4). Coverage Estimates at Country Level Coverage was consistently lowest for third-generation cephalosporin monotherapy, with some variation across the individual countries, ranging from 56.6% (95% CrI, 52.2%-60.7%) in Nepal to 17.9% (95% CrI, 11.7%-24.7%) in Indonesia (Figure). Similarly, although meropenem had the highest estimated coverage in each country, the proportion of neonates for whom it would be effective empirical treatment varied considerably, from 90.6% (95% CrI, 86.2%-94.4%) in Cambodia to 64.0% (95% CrI, 62.6%-65.4%) in India (Figure). Aminopenicillin-gentamicin offered the second highest level of coverage within each country behind meropenem. Nonetheless, there was again considerable variability in country-level estimates, from 74.3% (95% CrI, 70.3%-78.2%) in Nepal to 35.9% (95% CrI, 27.7%-44.0%) in Indonesia (Figure). Aminopenicillin-gentamicin coverage was higher than that offered by third-generation cephalosporins in China (60.6% [95% CrI, 54.2%-67.5%] vs 44.2% [95% CrI, 40.9%-47.9%]), India (45.1% [95% CrI, 43.7%-46.6%] vs 30.4% [95% CrI, 29.2%-31.6%]), Indonesia (35.9% [95% CrI, 27.7%-44.0%] vs 17.9% [95% CrI, 11.7%-24.7%]), and Nepal (74.3% [95% CrI, 70.3%-78.2%] vs 56.6% [95% CrI, 52.2%-60.7%]). There was greater uncertainty about whether the differences observed for Cambodia (47.4% [95% CrI, 38.1%-56.6%] vs 32.6% [95% CrI, 25.8%-39.9%]), Laos (81.0% [95% CrI, 71.1%-89.7%] vs 75.0% [95% CrI, 64.8%-84.1%]), Pakistan (42.2% [95% CrI, 39.1%-45.0%] vs 37.4% [95% CrI, 34.4%-40.3%]), and Vietnam (36.2% [95% CrI, 24.5%-49.0%] vs 21.5% [95% CrI, 12.0%-32.9%]) were due to chance variation. Table 1. Relative Incidence Data Isolates, No. (%) Cambodia China India Indonesia Laos Malaysia Nepal Pakistan Taiwan Vietnam Total Pathogen (n = 185) (n = 2043) (n = 6284) (n = 225) (n = 75) (n = 29) (n = 640) (n = 1875) (n = 36) (n = 75) (N = 11 467) Contributing to WISCA Escherichia coli 25 (16) 300 (33) 671 (14) 0 8 (13) 6 (33) 50 (10) 976 (57) 11 (92) 2 (4) 2049 (24) Klebsiella species 60 (39) 264 (29) 1065 (22) 49 (40) 9 (14) 1 (6) 45 (9) 159 (9) 1 (8) 18 (35) 1671 (20) Enterobacter species 18 (11) 58 (6) 167 (3) 20 (17) 4 (6) 0 30 (6) 0 0 6 (12) 303 (4) Acinetobacter species 16 (10) 27 (3) 992 (21) 21 (17) 2 (3) 0 63 (13) 0 0 17 (33) 1138 (14) Pseudomonas species 6 (4) 53 (6) 430 (9) 31 (26) 1 (2) 1 (6) 25 (5) 199 (12) 0 4 (8) 750 (9) Staphylococcus aureus 33 (21) 112 (12) 1235 (26) 0 37 (58) 10 (55) 261 (53) 388 (23) 0 4 (8) 2080 (25) Enterococcus species 0 91 (10) 275 (6) 0 3 (5) 0 15 (3) 1 (<1) 0 0 385 (5) Total reported during observation period Total contributing 158 (85) 905 (44) 4835 (77) 121 (54) 64 (85) 18 (62) 489 (76) 1723 (92) 12 (33) 51 (68) 8376 (73) to WISCA Other 27 (15) 1138 (56) 1449 (23) 104 (46) 11 (15) 11 (38) 151 (24) 152 (8) 24 (67) 24 (32) 3091 (27) (not contributing to WISCA) Coagulase-negative 0 741 (36) 980 (16) 63 (28) 0 0 137 (21) 28 (1) 0 23 (31) 1972 (17) staphylococci (not contributing to WISCA) Abbreviation: WISCA, weighted-incidence syndromic combination antibiogram. Percentages may not add to 100% because of rounding. JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 5/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 6/13 Table 2. Susceptibility Testing and Susceptibility Data for Aminopenicillin Plus Gentamicin No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T S N T S N T S N TS N T S N TS N T S N TS N T S Escherichia coli 25 25 13 300 290 182 671 655 426 0 NA NA 8 8 6 50 50 31 976 976 340 2 0 NA 2033 2004 998 Klebsiella 60 60 10 264 256 193 1065 1026 402 49 49 3 9 9 7 45 42 23 159 159 36 18 11 2 1669 1612 676 species Enterobacter 18 18 8 58 20 11 167 154 42 20 20 18 4 0 NA 30 30 21 0 NA NA 6 5 3 303 247 103 species Acinetobacter 16 0 NA 27 0 NA 992 930 226 21 21 11 2 0 NA 63 62 34 0 NA NA 17 17 3 1138 1030 274 species Pseudomonas 6 0 NA 53 0 NA 430 422 238 31 31 9 1 0 NA 25 23 18 199 199 74 4 4 1 749 679 340 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 1 0 275 132 44 0 NA NA 3 0 NA 15 15 12 1 0 NA 0 NA NA 385 148 56 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 7/13 Table 3. Susceptibility Testing and Susceptibility Data for Third-Generation Cephalosporins No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T SN T S N T S N T S N T SN T S N T S N T S N T S Escherichia coli 25 25 13 300 289 165 671 657 339 0 NA NA 8 8 7 50 43 25 976 976 317 2 0 NA 2033 1998 866 Klebsiella 60 60 4 264 251 122 1065 1031 346 49 49 2 9 9 6 45 42 12 159 159 52 18 11 1 1669 1612 545 species Enterobacter 18 18 1 58 20 14 167 167 59 20 20 17 4 0 NA 30 28 12 0 NA NA 6 4 1 303 257 104 species Acinetobacter 16 16 0 27 27 0 992 992 0 21 21 0 2 2 0 63 63 0 0 NA NA 17 17 0 1138 1138 0 species Pseudomonas 66053 53 0 430 430 0 31 31 0 1 1 025 25 0 199 199 0 440 749 749 0 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 91 0 275 275 00 NA NA 3 3 0 15 15 01100 NA NA 385 385 0 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility Not based on susceptibility testing because pathogen was assumed to be intrinsically resistant. testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 8/13 Table 4. Susceptibility Testing and Susceptibility Data for Meropenem No. of Isolates Cambodia China India Indonesia Laos Nepal Pakistan Vietnam Total Pathogen N T S N TS N T S N TS N T S N TS N T S N T S N T S Escherichia coli 25 24 24 300 289 289 671 439 379 0 NA NA 8 0 NA 50 3 1 976 811 768 2 0 NA 2033 1566 1461 Klebsiella 60 60 60 264 253 228 1065 882 667 49 49 49 9 0 NA 45 27 27 159 102 87 18 9 9 1669 1382 1127 species Enterobacter 18 18 17 58 20 20 167 157 122 20 20 19 4 0 NA 30 16 14 0 NA NA 6 3 3 303 234 195 species Acinetobacter 16 16 14 27 0 NA 992 926 475 21 21 21 2 0 NA 63 7 3 0 NA NA 17 16 15 1138 986 528 species Pseudomonas 6 5 5 53 0 NA 430 415 354 31 31 23 1 0 NA 25 0 NA 199 199 188 4 3 3 749 653 573 species Staphylococcus 33 33 32 112 56 31 1235 1142 655 0 NA NA 37 37 37 261 227 195 388 88 63 4 3 3 2070 1586 1016 aureus Enterococcus 0 NA NA 91 91 0 275 275 0 0 NA NA 3 3 0 15 15 0 1 1 0 0 NA NA 385 385 0 species Abbreviations: N, total isolates; NA, not applicable; S, isolates identified as susceptible on testing; T, susceptibility Not based on susceptibility testing because pathogen was assumed to be intrinsically resistant. testing available for regimen of interest. JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries Meropenem coverage was higher than aminopenicillin-gentamicin coverage in Cambodia (90.6% [95% CrI, 86.2%-94.4%] vs 47.4% [95% CrI, 38.1%-56.6%]), China (76.5% [95% CrI, 71.8%- 80.9%] vs 60.6% [95% CrI, 54.2%-67.5%]), India (64.0% [95% CrI, 62.6%-65.4%] vs 45.1% [95% CrI, 43.7%-46.6%]), Indonesia (88.8% [95% CrI, 83.2%-93.6%] vs 35.9% [95% CrI, 27.7%-44.0%]), Pakistan (88.1% [95% CrI, 85.6%-90.3%] vs 42.2% [95% CrI, 39.1%-45.0%]), and Vietnam (84.1% [95% CrI, 73.2%-92.6%] vs 36.2% [95% CrI, 24.5%-49.0%]) on the basis of nonoverlapping 95% CrIs. The largest percentage differences in coverage were observed in Indonesia (52.9%), Pakistan (45.9%), and Cambodia (43.2%); the smallest was in China (15.9%). For meropenem and third- generation cephalosporins, the percentage difference was largest for Indonesia (70.9%), Vietnam (62.6%), and Cambodia (58.0%). Of note, for Laos and Nepal, imprecision around estimated meropenem coverage, which was comparable with that of aminopenicillin-gentamicin with overlapping 95% CrIs, was largely because of low proportions of isolates (62.5% [40 of 64] for Laos and 60.3% [295 of 489] for Nepal) contributing to the meropenem susceptibility parameter. Discussion We estimated the coverage offered by 3 antibiotic regimens—aminopenicillin-gentamicin (WHO- recommended first-line regimen), third-generation cephalosporins (WHO-recommended second- line regimen), and meropenem—in Asian countries for the empirical treatment of neonatal sepsis caused by 7 specified bacteria. The coverage estimates were based on a systematic review of recent studies reporting on the relative incidence of common bacteria and their resistance. In general, coverage estimates supported the identification of better-performing or worse- performing regimens for most countries. Coverage offered by aminopenicillin-gentamicin (WHO- recommended first-line regimen) was less than 50% for Cambodia, India, Indonesia, Pakistan, and Vietnam and less than 75% for China and Nepal. Even lower coverage was offered by the WHO-recommended second-line third-generation cephalosporin monotherapy regimen: below 50% Figure. Coverage Estimates for 8 Asian Countries Aminopenicillin and gentamicin Third-generation cephalosporin Meropenem a a a a b b a a Cambodia China India Indonesia Laos Nepal Pakistan Vietnam (n = 158) (n = 905) (n = 4835) (n = 121) (n = 64) (n = 489) (n = 1723) (n = 51) Point estimates are shown with 95% credible intervals, as denoted by error bars. The highest coverage offered by aminopenicillin-gentamicin combination was in Laos Nonoverlapping 95% credible intervals indicate likely within-country differences in (81.0%) and Nepal (74.3%). regimen coverage. Countries are shown together with the overall number of isolates used for estimating coverage. The highest coverage offered by meropenem was in Cambodia (90.6%), China (76.5%), India (64.0%), Indonesia (88.8%), Pakistan (88.1%), and Vietnam (84.1%). JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 9/13 Isolates Covered by Regimen, % JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries in all represented countries except Laos (75.0%) and Nepal (56.6%). Meropenem coverage was generally highest and was greater than 80% in Cambodia, Indonesia, Pakistan, and Vietnam, but lower than 80% in China, Laos, and Nepal and as low as 64.0% in India. Considerable between- country differences were observed for all 3 regimens, even for countries bordering each other, such as Cambodia, Laos, Thailand, and Vietnam. Coverage estimates are clinically highly relevant for the development of local and national empirical treatment guidelines, incorporating both the relative incidence of bacteria and their susceptibility. This concept has not, to our knowledge, been previously applied to neonatal sepsis in LMICs. Instead, reports have focused on susceptibility for individual pathogen-drug combinations, 4,6,7 an approach that does not directly incorporate the spectrum of causative bacteria. One important question is whether global setting-independent recommendations for empirical neonatal sepsis treatment can be supported in an era of changing and highly variable epidemiology. In some settings, difficult-to-treat pathogens and multidrug-resistant isolates now contribute considerably to neonatal sepsis. Stratified guidance moving between recommended regimens according to microbiology and coverage by patient-level factors (eg, presence of certain underlying conditions or timing of sepsis onset) or setting, may be a solution. One challenge will be the lack of defined coverage thresholds to move between regimens. Given sufficiently large data sets, coverage estimates could help inform such shifting by supporting inferences about true differences between regimens. Limitations This study has some limitations. Our coverage estimates were based on data from predominantly university or teaching hospitals. Infants with complex medical issues and those at higher risk of nosocomial bloodstream infections may, therefore, be overrepresented. At the same time, microbiology data from infants managed in district hospitals are lacking precluding confirmation that presented coverage estimates are applicable to them as well. Clinicians applying WHO recommendations to infants with nosocomial infection or those managed in tertiary hospitals would, on the basis of our observations, need to consider alternatives for this population. We chose to estimate coverage according to the pathogens frequently reported across included studies, which are likely to be associated with severe neonatal sepsis and the so-called ESKAPE organisms (ie, Enterococcus faecium, S aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa), which are known to be problematic in terms of emerging antimicrobial resistance. Inclusion of other pathogens would be expected to have a variable influence on the expected coverage of considered antibiotics, leading to either higher or lower estimates. This may be particularly important in individual hospitals with ongoing outbreaks where a single bacterial strain is dominant. In such situations, regional coverage estimates may not be applicable. Coverage estimation requires a number of assumptions to be made when calculating the susceptibility parameters, such as the incorporation of intrinsic resistance, extrapolations from susceptibility testing for 1 representative of an antibiotic class to other members of this class, and the interpretation of multiple testing for 1 antibiotic class. We based our calculations of regimen susceptibility on EUCAST algorithms and, whenever possible, used susceptibility testing information for the specific drug of interest. Importantly, however, all included studies used versions of Clinical and Laboratory Standards Institute interpretive criteria, which may diverge from EUCAST in terms of both break points and assumptions about intrinsic resistance. Debate about the merits and challenges of switching from Clinical and Laboratory Standards Institute to EUCAST and about the implications of such a transition for interpretation of routine data in the context of surveillance 23,24 is ongoing. To support coverage estimation, it is important that the microbiological data used are collected in equivalent ways. However, the data used for this analysis may have been subject to various random or systematic errors that could bias the coverage estimates. Possible sources of error include duplicate isolates, contaminants, nonstandardized susceptibility testing, combining data from JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 10/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries different patient populations (children and adults), and reflex susceptibility testing based on resistance identified in a first-line testing panel. These requirements have important implications for global surveillance initiatives, such as the Global Antimicrobial Resistance Surveillance System, if data collected are to be used at the interface between surveillance and clinical practice. Conclusions Recently, machine learning approaches and more elaborate multivariable Bayesian models using clinical and demographic information combined with microbiological data have been proposed as 27,28 optimizing the selection of empirical antibiotic treatment for sepsis. Although these models may help in selecting patient-adapted regimens, the approach used in our study only requires estimates of pathogen incidence and susceptibility and could already substantially improve clinical decision- making based on routine microbiological data alone, provided that the data used to produce these estimates are of sufficient quality. Our analysis indicates that the recommendation for third- generation cephalosporin monotherapy as a second-line regimen may no longer be valid for many infants receiving treatment for neonatal sepsis in several Asian countries. Our findings could explain 14,29 the high reported empirical meropenem use in this population in Asia. Evaluation of potential alternatives will be essential to reducing consumption of last-resort antibiotics for the empirical treatment of neonatal sepsis in settings with a high prevalence of antimicrobial resistance. ARTICLE INFORMATION Accepted for Publication: December 13, 2019. Published: February 12, 2020. doi:10.1001/jamanetworkopen.2019.21124 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Bielicki JA et al. JAMA Network Open. Corresponding Author: Julia A. Bielicki, MD, Paediatric Infectious Diseases Research Group, Institute of Infection and Immunity, St George’s University of London, Jenner Wing, Level 2, Room 2.215E, Cranmer Terrace, London SW17 0RE, United Kingdom (jbielick@sgul.ac.uk). Author Affiliations: Paediatric Infectious Diseases Research Group, Institute of Infection and Immunity, St George’s University of London, London, United Kingdom (Bielicki, Sharland, Heath); Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom (Bielicki, Cromwell); Paediatric Pharmacology and Paediatric Infectious Diseases, University of Basel Children’s Hospital, Basel, Switzerland (Bielicki); Medical Research Council Clinical Trials Unit at University College London, London, United Kingdom (Walker); Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India (Agarwal); Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom (Agarwal, Turner); Cambodia Oxford Medical Research Unit, Siem Reap, Cambodia (Turner). Author Contributions: Dr Bielicki had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Bielicki, Sharland, Heath, Cromwell. Acquisition, analysis, or interpretation of data: Bielicki, Walker, Agarwal, Turner, Cromwell. Drafting of the manuscript: Bielicki, Heath. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Bielicki. Supervision: Sharland, Heath, Walker, Cromwell. Conflict of Interest Disclosures: Dr Bielicki reported that her spouse is senior corporate counsel at Novartis International AG and holds Novartis stock and stock options. No other disclosures were reported. REFERENCES 1. Seale AC, Blencowe H, Manu AA, et al; pSBI Investigator Group. Estimates of possible severe bacterial infection in neonates in sub-Saharan Africa, south Asia, and Latin America for 2012: a systematic review and meta- analysis. 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Coagulase-negative staphylococci. Clin Microbiol Rev. 2014;27(4):870-926. doi:10.1128/CMR.00109-13 17. Jain A, Agarwal J, Bansal S. Prevalence of methicillin-resistant, coagulase-negative staphylococci in neonatal intensive care units: findings from a tertiary care hospital in India. J Med Microbiol. 2004;53(9):941-944. doi:10. 1099/jmm.0.45565-0 18. Krediet TG, Mascini EM, van Rooij E, et al. Molecular epidemiology of coagulase-negative staphylococci causing sepsis in a neonatal intensive care unit over an 11-year period. J Clin Microbiol. 2004;42(3):992-995. doi: 10.1128/JCM.42.3.992-995.2004 19. Leclercq R, Cantón R, Brown DFJ, et al. EUCAST expert rules in antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-160. doi:10.1111/j.1469-0691.2011.03703.x 20. European Committee on Antimicrobial Susceptibility Testing. EUCAST clinical breakpoints: bacteria (v 9.0). http:// www.eucast.org/clinical_breakpoints/. Published 2019. Accessed June 13, 2019. 21. Cressman AM, MacFadden DR, Verma AA, Razak F, Daneman N. Empiric antibiotic treatment thresholds for serious bacterial infections: a scenario-based survey study. Clin Infect Dis. 2019;69(6):930-937. doi:10.1093/cid/ ciy1031 22. Boucher HW, Talbot GH, Bradley JS, et al. Bad bugs, no drugs: no ESKAPE! an update from the Infectious Diseases Society of America. Clin Infect Dis. 2009;48(1):1-12. doi:10.1086/595011 JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 12/13 JAMA Network Open | Global Health Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries 23. Cusack TP, Ashley EA, Ling CL, et al. Time to switch from CLSI to EUCAST? a Southeast Asian perspective. Clin Microbiol Infect. 2019;25(7):782-785. doi:10.1016/j.cmi.2019.03.016 24. Seale AC, Gordon NC, Islam J, Peacock SJ, Scott JAG. AMR surveillance in low and middle-income settings: a roadmap for participation in the Global Antimicrobial Surveillance System (GLASS). Wellcome Open Res. 2017;2:92. doi:10.12688/wellcomeopenres.12527.1 25. Rempel OR, Laupland KB. Surveillance for antimicrobial resistant organisms: potential sources and magnitude of bias. Epidemiol Infect. 2009;137(12):1665-1673. doi:10.1017/S0950268809990100 26. World Health Organization. Global Antimicrobial Resistance Surveillance System (GLASS). https://www.who. int/glass/en/. Published October 2015. Accessed January 7, 2020. 27. MacFadden DR, Coburn B, Shah N, et al Decision-support models for empiric antibiotic selection in Gram- negative bloodstream infections. Clin Microbiol Infect. 2019;25(1):108.e1-108.e7. doi:10.1016/j.cmi.2018.03.029 28. Oonsivilai M, Mo Y, Luangasanatip N, et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia. Wellcome Open Res. 2018;3:131. doi:10.12688/ wellcomeopenres.14847.1 29. Hsia Y, Lee BR, Versporten A, et al; GARPEC and Global-PPS networks. Use of the WHO Access, Watch, and Reserve classification to define patterns of hospital antibiotic use (AWaRe): an analysis of paediatric survey data from 56 countries. Lancet Glob Health. 2019;7(7):e861-e871. doi:10.1016/S2214-109X(19)30071-3 SUPPLEMENT. eAppendix. Supplemental Methods eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest eFigure 2. Flow Chart: Systematic Review of the Literature eReferences. eTable 1. Description of Included Publications eTable 2. Information on Sample Processing Provided in Included Publications eTable 3. Relative Incidence of Bacteria in Included Studies JAMA Network Open. 2020;3(2):e1921124. doi:10.1001/jamanetworkopen.2019.21124 (Reprinted) February 12, 2020 13/13 Supplementary Online Content Bielicki JA, Sharland M, Heath PT, et al. Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries. JAMA Netw Open. 2020:3(2):e1921124. doi:10.1001.jamanetworkopen.2019.21124 eAppendix. Supplemental Methods eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest eFigure 2. Flow Chart: Systematic Review of the Literature eReferences. eTable 1. Description of Included Publications eTable 2. Information on Sample Processing Provided in Included Publications eTable 3. Relative Incidence of Bacteria in Included Studies This supplementary material has been provided by the authors to give readers additional information about their work. © 2020 Bielicki JA et al. JAMA Network Open. eAppendix. Supplemental Methods Search strategy for systematic literature review Ovid MEDLINE® 1946 to April 25 2019 1 exp SEPSIS/ or exp NEONATAL SEPSIS/ 2 exp BACTEREMIA 3 bacter?emia.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 4 (blood?stream adj3 infect*).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 5 (blood adj2 culture adj2 (positive* or isolat*)).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 6 1 or 2 or 3 or 4 or 5 7 ((anti?biotic* or anti?infect* or anti?microb*) adj2 (resist* or suscep* or sensitive*)).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 8 exp Drug Resistance, Microbial/ 9 7 or 8 10 exp infant/ or exp infant, newborn/ 11 (infant* or neonat* or new?born).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms] 12 10 or 11 13 6 and 9 and 12 14 Exp ASIA/ 15 13 and 14 16 - Embase 1974 to 2019 Week 16 1 exp bacteremia/ 2 exp sepsis/ or newborn sepsis/ 3 bacter?emia.mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 4 (blood?stream adj2 infect*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 5 (blood adj2 culture adj2 (positive* or isolate*)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 6 1 or 2 or 3 or 4 or 5 7 ((anti?biotic* or anti?infect* or anti?microb*) adj2 (resist* or suscep* or sensitiv*)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 8 exp antibiotic resistance/ 9 7 or 8 10 infant/ 11 newborn/ 12 (infant or new?born or neonat*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 13 10 or 11 or 12 14 6 and 9 and 13 15 14 16 14 and 15 © 2020 Bielicki JA et al. JAMA Network Open. 17 limit - Systematic review of the literature: selection of publications Studies were eligible for inclusion if they examined blood culture isolates and (i) provided information specific to newborns up to 28 days of age or infants managed on neonatal units, (ii) reported on the relative incidence of different bacteria at species or genus level during the indicated surveillance period and (iii) included data on antimicrobial resistance for at least one bacterial species or genus. Publications reporting on isolates from sources other than blood, and those from which data for neonatal blood cultures (e.g. reporting pooled data across age groups) could not be extracted were excluded. Equally studies focusing on single organisms from which the relative incidence of other bacteria could not be obtained were excluded. Further we excluded studies presenting only aggregate data by region or internationally. After exclusion of duplicates, titles or abstracts of retrieved studies were reviewed by one author (JB) to identify those meeting inclusion criteria. A random subset of retrieved studies was reviewed by a second author (MS) to ensure consistency in selection based on the pre-specified inclusion and exclusion criteria with no disagreements. Selected publications were primarily used to inform parameter estimation for calculating coverage. Additional extracted data included contextual information (namely the year of publication, the country from where the data originated, the surveillance/reporting period, and the number and type of hospitals surveyed), and whether studies reported on blood culture isolates from community-acquired infections, hospital-acquired infections or both. Early onset of neonatal sepsis defined as infection occurring in the first 3 days of life was considered a community-acquired infection. We also extracted information on approaches to species identification, susceptibility testing and evaluation of testing results, if provided. Species identification and susceptibility testing results were recorded as reported. As the study was focused on the reporting of routine microbiological or surveillance data, we did not undertake a formal grading of the quality of the studies or an evaluation of the appropriateness of microbiological approaches. Assumptions for determining susceptibility of pathogens to pre-specified regimens Aminopenicillin susceptibility was based on either ampicillin or amoxicillin susceptibility testing results, whichever was available. Gentamicin susceptibility was based on results for gentamicin rather than other aminoglycosides whenever possible, because susceptibility to gentamicin cannot be reliably inferred from results for other aminoglycosides. If no gentamicin susceptibility data were provided, data from other aminogylcosides (mostly amikacin) were used. Third-generation cephalosporin susceptibility was based on either cefotaxime or ceftriaxone, whichever was available. Meropenem susceptibility was based on results for meropenem rather than other carbapenems whenever possible, because susceptibility to meropenem cannot be reliably inferred from results for other carbapenems. If no meropenem susceptibility data were provided, data from other carbapenems (mostly imipenem) were used. For Staphylococcus aureus, third-generation cephalosporin and meropenem susceptibility was derived from information on methicillin resistance, as these antibiotics are not generally specifically tested for S. aureus. For the combined regimen (i), the one with the higher susceptibility was taken to reflect overall susceptibility. For example, if Escherichia coli in a specific country exhibited 20% ampicillin susceptibility and 70% gentamicin susceptibility, susceptibility to aminopenicillin plus gentamicin for E. coli was assumed to be 70%. Technical appendix on calculation of the weighted-incidence syndromic combination antibiogram (WISCA) In the WISCA decision tree, the first square node represents the clinical decision to start empiric antibiotic therapy and the regimen choices. Subsequent circular nodes and branches describe chance events, which are the range of relevant bacteria causing neonatal sepsis, their relative incidence and the percentages of each pathogen susceptible to each antibiotic regimen. Combining the probabilities along the regimen tree branches provides an estimate of coverage for each regimen. A difficulty in adopting a Bayesian perspective is the specification of the prior distributions for the parameters. The value of the relative incidence and pathogen regimen susceptibility parameters for each regimen were therefore defined as probability distributions that reflected the uncertainty in their value. Given that susceptibility percentages are simple proportions, we selected a binomial distribution to describe our prior belief defined using the conjugate Beta distribution. This approach results in the posterior also being a Beta distribution. The relative incidence data were assumed to be drawn from a multinomial distribution with nine possible outcomes. The prior was accordingly distribution, and is the generalisation of the Beta distribution to situations described by more than two categories. © 2020 Bielicki JA et al. JAMA Network Open. means that the posterior distribution is largely determined by the observed data. Using the Dirichlet distribution as the prior, for example, results in the posterior taking the form Dirichlet (1+n1, 1+n2,. . ., 1+n9). Equally, in most cases, when there were no strong prior beliefs about pathogen-regimen susceptibility, the non-informative prior beta(1,1) was used. Adopting a Bayesian perspective allows the use of informative priors for the situation in which a pathogen has intrinsic resistance or is assumed to be fully susceptible. For these, we chose a pragmatic posterior Beta distribution, chosen to have an appropriate standard deviation. For example, susceptibility for a pathogen with intrinsic resistance was specified as a Beta(1,9999), which has a standard deviation of 0.01%. Sampling from this distribution only gives pathogen resistance below 99.9% in 1 in 20000 draws. The calculation of the 95% credible interval describing the precision of coverage estimates requires Monte Carlo simulation, which involves running a large number of experiments (in our case 1000) and combining their results. In each experiment, parameter values for the parameters of interest (relative incidence and pathogen-regimen susceptibility) are randomly drawn from their specified distributions. The values of each parameter are then combined to derive a coverage estimate. Together, the individual coverage estimates from all the experiments give the posterior the interval between 2.5% and 97% percentile of this distribution. Analytical steps for basic WISCA coverage estimation using a Bayesian decision tree model. 1. Identify the total number of isolates contributing to the infection syndrome of interest for a given setting and period. 2. Select from 1. clinically relevant bacteria contributing to the infection syndrome and with data available to define model parameters. 3. Specify assumptions used for determining susceptibility to the regimen, including extrapolation from standard bug-drug susceptibility testing, definitions of intrinsic resistance and, when relevant, intrinsic susceptibility (corresponding to unusual resistance phenotypes) 4. For the bacteria specified in 2. identify the number of isolates contributed by each (to determine relative frequency = first circular node and branches) and the number of isolates tested for and susceptible to the regimen of interest (second circular node and branches). 5. Select appropriate informative priors for bacteria with intrinsic resistance or expected susceptibility as set out in 3. 6. Select non-informative priors for relative bacterial incidence and susceptibility with the exceptions as outlined in 5. 7. Use appropriate probability distributions to reflect uncertainty in the relative frequency of bacteria (multinomial, Dirichlet distribution) and susceptibility to the regimen (binomial, Beta distribution). 8. Model coverage by running a Monte Carlo simulation with n experiments sampling parameter values for relative bacterial frequency and regimen susceptibility from their specified distributions. 9. Combine estimates from n experiments to calculate coverage estimates with their 2.5% and 97% percentiles, corresponding to the 95% uncertainty or credible interval. 10. Repeat this process for each regimen of interest, noting that for comparisons within a given setting the bacteria included in the WISCA should stay the same (meaning that number of isolates contributed by each will be the same), but that the number tested and susceptible will vary by regimen. © 2020 Bielicki JA et al. JAMA Network Open. eFigure 1. Illustration of Decision Tree for Estimating Coverage From Weighted Incidence Syndromic Combination Antibiograms for Three Antibiotic Regimens of Interest ET: empiric therapy. Square node: clinical decision to treat; circular node: chance event (causal bacteria and their regimen susceptibility). The decision tree is shown for illustration only, and dashed lines indicate where the decision tree has been left incomplete. All branches are included in the WISCA calculations to estimate coverage. © 2020 Bielicki JA et al. JAMA Network Open. eFigure 2. Flow Chart: Systematic Review of the Literature © 2020 Bielicki JA et al. JAMA Network Open. eReferences: Reference list for included publications 1. Abu NA, Nor FM, Mohamad M, et al. Community-acquired Bacteremia in paediatrics: Epidemiology, aetiology and patterns of antimicrobial resistance in a tertiary care centre, Malaysia. Medical Journal of Malaysia 2016; 71(3): 117-21. 2. Adhikari N, Shah PK, Acharya G, Vaidya KM. Bacteriological profile and associated risk factors of neonatal sepsis in Paropakar Maternity and Women's Hospital Thapathali, Kathmandu. Nepal Medical College journal : NMCJ 2014; 16(2-4): 161-4. 3. Agarwal A, Bhat S. Clinico-microbiological study of neonatal sepsis. Journal of International Medicine and Dentistry 2015; 2(1): 22-9. 4. Ambade VN, Kolpe D, Tumram N, Meshram S, Pawar M, Kukde H. Characteristic Features of Hanging: A Study in Rural District of Central India. Journal of forensic sciences 2015; 60(5): 1216-23. 5. Amin AJ, Malam PP, Asari PD, Patel UR, Behl AB. Sensitivity and resistance pattern of antimicrobial agents used in cases of neonatal sepsis at a tertiary care centre in western India. International Journal of Pharmaceutical Sciences and Research 2016; 7(7): 3060-7. 6. Anderson M, Luangxay K, Sisouk K, et al. Epidemiology of bacteremia in young hospitalized infants in vientiane, laos, 2000-2011. Journal of Tropical Pediatrics 2014; 60(1): 10-6. 7. Chapagain RH, Acharya R, Shrestha N, Giri BR, Bagale BB, Kayastha M. Bacteriological Profile of Neonatal Sepsis in Neonatal Intermediate Care Unit of Central Paediatric Referral Hospital in Nepal. Journal of Nepal Health Research Council 2015; 13(31): 205-8. 8. Dalal P, Gathwala G, Gupta M, Singh J. Bacteriological profile and antimicrobial sensitivity pattern in neonatal sepsis: a study from North India. 2017 2017; 5(4): 5. 9. Dhanalakshmi V, Sivakumar ES. Comparative Study in Early Neonates with Septicemia by Blood Culture, Staining Techniques and C - Reactive Protein (CRP). Journal of clinical and diagnostic research : JCDR 2015; 9(3): Dc12-5. 10. Dhaneria M, Jain S, Singh P, Mathur A, Lundborg CS, Pathak A. Incidence and Determinants of Health Care- Associated Blood Stream Infection at a Neonatal Intensive Care Unit in Ujjain, India: A Prospective Cohort Study. Diseases 2018; 6(1). 11. Dong H, Cao H, Zheng H. Pathogenic bacteria distributions and drug resistance analysis in 96 cases of neonatal sepsis. BMC Pediatrics 2017; 17(1): 44. 12. Fox-Lewis A, Takata J, Miliya T, et al. Antimicrobial resistance in invasive bacterial infections in hospitalized children, Cambodia, 2007-2016. Emerging Infectious Diseases 2018; 24(5): 841-51. 13. Gupta M, Chaudhary U. Bacteriologic profile and antibiogram of paediatric blood cultures in a tertiary care centre. International Journal of Pharma and Bio Sciences 2015; 6(2): B341-B6. 14. Ingale HD, Kongre VA, Bharadwaj RS. A study of infections in neonatal intensive care unit at a tertiary care hospital. 2017 2017; 4(4): 8. 15. Investigators of the Delhi Neonatal Infection Study c. Characterisation and antimicrobial resistance of sepsis pathogens in neonates born in tertiary care centres in Delhi, India: a cohort study. Lancet Glob Health 2016; 4(10): e752-60. 16. Jajoo M, Manchanda V, Chaurasia S, et al. Alarming rates of antimicrobial resistance and fungal sepsis in outborn neonates in North India. PLoS ONE 2018; 13 (6) (e0180705). 17. Javali NS, Banu N, Indi SM. Incidence and Microbiological Profile of Late Onset Neonatal Sepsis in Preterm and Low Birth Weight Neonates, NICU, RIMS, Raichur. J of Evidence Based Med & Hlthcare 2014; 1(16): 2036-48. 18. Jiang Y, Kuang L, Wang H, Li L, Zhou W, Li M. The clinical characteristics of neonatal sepsis infection in Southwest China. Internal Medicine 2016; 55(6): 597-603. 19. Kamble R, Ovhal R. Bacteriological profile of neonatal septicemia. International Journal of Current Microbiology and Applied Sciences 2015; 4(2): 172-82. 20. Kanodia P, Yadav SK, Sigh RR, Bhatta NK. Bacteriological Profile of Blood Culture Positive Sepsis in Newborn at BPKIHS, Dharan Nepal. Journal of College of Medical Sciences - Nepal 2017; 13(1): 193-6. 21. Khanal R, Manandhar S, Acharya GP. Bacteriological profile of neonatal sepsis in a tertiary level hospital of Nepal. Journal of Nepal Paediatric Society 2014; 34(3): 175-80. 22. Li X, Ding X, Shi P, et al. Clinical features and antimicrobial susceptibility profiles of culture-proven neonatal sepsis in a tertiary children's hospital, 2013 to 2017. Medicine 2019; 98(12): e14686. 23. Lu Q, Zhou M, Tu Y, Yao Y, Yu J, Cheng S. Pathogen and antimicrobial resistance profiles of culture-proven neonatal sepsis in Southwest China, 1990-2014. Journal of Paediatrics and Child Health 2016; 52(10): 939-43. 24. Madavi D, Aziz F, Agrawal G. Clinica-Bacteriological Profile and Antibiotic Sensitivity Pattern of Neonatal Septicaemia - A Prospective Observational Study. International Journal of Current Research and Review 2015; 7(5): 13-20. 25. Mahmood T, Javed N, Subhani H, Ali FA. Bacteria isolated from Blood Cultures of Septicemic children at a teaching Unit. Pakistan Journal of Medical and Health Sciences 2016; 10(4): 1129-31. © 2020 Bielicki JA et al. JAMA Network Open. 26. Marwah P, Chawla D, Chander J, Guglani V, Marwah A. Bacteriological profile of neonatal sepsis in a tertiary-care hospital of Northern India. Indian pediatrics 2015; 52(2): 158-9. 27. Mehta A.M., Navinchandra M. K., Tukaram K.P. Microbial Profile of Neonatal septicaemia in a tertiary care hospital of Bhopal. International Journal of Biomedical and Advance Research 2014; 5(10): 499-501. 28. Muley VA, Ghadage DP, Bhore AV. Bacteriological profile of neonatal septicemia in a tertiary care hospital from Western India. Journal of Global Infectious Diseases 2015; 7(2): 75-7. 29. Mustafa M., Ahmed S.L. Bacteriological profile and antibiotic susceptiblity patterns in neonatal septicemia in view of emerging drug resistance. Journal of Medical and Allied Sciences 2014; 4(1): 2-8. 30. Nayak S, Rai R, Kumar V, Sanjeev H, Pai A, Ganesh H. Distribution of microorganisms in neonatal sepsis and antimicrobial susceptibility patterns in a tertiary care hospital. Archives of Medicine and Health Sciences 2014; 2(2): 136-9. 31. Pandita N, Wasim S, Bhat NK, Chandra V, Kakati B. Identification of the bacterial isolates in neonatal septicaemia and their antimicrobial susceptibility in a tertiary care hospital in Uttarakhand, India: a retrospective study. 2016 2016; 3(1): 6. 32. Panigrahi P, Chandel DS, Hansen NI, et al. Neonatal sepsis in rural India: Timing, microbiology and antibiotic resistance in a population-based prospective study in the community setting. Journal of Perinatology 2017; 37(8): 911-21. 33. Patel D, Nimbalkar A, Sethi A, Kungwani A, Nimbalkar S. Blood culture isolates in neonatal sepsis and their sensitivity in Anand District of India. Indian journal of pediatrics 2014; 81(8): 785-90. 34. Pavan Kumar DV, Mohan J, Rakesh PS, Prasad J, Joseph L. Bacteriological profile of neonatal sepsis in a secondary care hospital in rural Tamil Nadu, Southern India. Journal of family medicine and primary care 2017; 6(4): 735-8. 35. Pokhrel B, Koirala T, Shah G, Joshi S, Baral P. Bacteriological profile and antibiotic susceptibility of neonatal sepsis in neonatal intensive care unit of a tertiary hospital in Nepal. BMC Pediatrics 2018; 18: 208. 36. Ponugoti ML, Venkatakrishna M, Jithendra K, Reddy PS. Incidence of Early Onset Septicemia, Isolation and Resistant Patterns of Causative Organisms: A Study in a Tertiary Care Hospital A.P. Journal of Medical Science and Clinical Research 2015; 3(2): 4227-32. 37. Roy MP, Bhatt M, Maurya V, Arya S, Gaind R, Chellani HK. Changing trend in bacterial etiology and antibiotic resistance in sepsis of intramural neonates at a tertiary care hospital. Journal of postgraduate medicine 2017; 63(3): 162-8. 38. Sarangi KK, Pattnaik D, Mishra SN, Nayak MK, Jena J. Bacteriological profile and antibiogram of blood culture isolates done by automated culture and sensitivity method in a neonatal intensive care unit in a tertiary care hospital in Odisha, India. 2017 2017; 2(4): 6. 39. Sari IP, Nuryastuti T, Wahyono D. The study of multidrug-resistance in neonatal intensive care unit at the central java hospital. Asian Journal of Pharmaceutical and Clinical Research 2017; 10(Special Issue may): 80-4. 40. Singh HK, Sharja P, Onkar K. Bacteriological profile of neonatal sepsis in neonatal intensive care unit (NICU) in a tertiary care hospital: prevalent bugs and their susceptibility patterns. European Journal of Pharmaceutical and Medical Research 2016; 3(3): 241-5. 41. Thakur S, Thakur K, Sood A, Chaudhary S. Bacteriological profile and antibiotic sensitivity pattern of neonatal septicaemia in a rural tertiary care hospital in North India. Indian Journal of Medical Microbiology 2016; 34(1): 67-71. 42. Ting YT, Lu CY, Shao PL, et al. Epidemiology of community-acquired bacteremia among infants in a medical center in Taiwan, 2002-2011. Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2015; 48(4): 413-8. 43. Tran HT, Doyle LW, Lee KJ, Dang NM, Graham SM. A high burden of late-onset sepsis among newborns admitted to the largest neonatal unit in central Vietnam. Journal of Perinatology 2015; 35(10): 846-51. 44. Tudu NK, Dey R, Bhattacharya I, Roy S, Dey JB. A pilot study on bacterial profile of neonatal sepsis in a tertiary care hospital serving rural population. Journal of Evolution of Medical and Dental Sciences 2014; 3(23): 6378-81. 45. Ullah O, Khan A, Ambreen A, et al. Antibiotic Sensitivity pattern of Bacterial Isolates of Neonatal Septicemia in Peshawar, Pakistan. Arch Iran Med 2016; 19(12): 866-9. 46. Venkatnarayan K, Bej PK, Thapar RK. Neonatal Sepsis: A Profile of a Changing Spectrum. J Nepal Paediatr Soc 2014; 34(3): 207-14. 47. Wang S, Chen S, Feng W, et al. Clinical characteristics of nosocomial bloodstream infections in neonates in two hospitals, China. Journal of Tropical Pediatrics 2018; 64(3): 231-6. 48. Yadav NS, Sharma S, Chaudhary DK, et al. Bacteriological profile of neonatal sepsis and antibiotic susceptibility pattern of isolates admitted at Kanti Children's Hospital, Kathmandu, Nepal. BMC research notes 2018; 11(1): 301. © 2020 Bielicki JA et al. JAMA Network Open. eTable 1. Description of Included Publications Publication year, First author, Journal Country, City/Town N hospitals, type Observation period start and Infections surveyed end* 2014 Adhikari Nepal Medical Nepal Thapathali 1 Maternity 01Aug11 31Mar12 Sepsis with positive College Journal BC Anderson Journal of Tropical Laos Vientiane 1 U/T 01Feb00 01Sep11 Sepsis with positive Pediatrics BC Javali Journal of Evidence India Raichur 1 NT/D 01Jun13 30Jul13 LONS with positive Based Medicine & BC Healthcare Khanal Journal of Nepal Nepal Kathmandu 1 Maternity 01Dec10 31Mar11 Sepsis with positive Paediatric Society BC Mehta International Journal India Bhanpur 1 U/T 01Jul12 31Dec13 Sepsis with positive of Biomedical And BC Advance Research Mustafa Journal of Medical and India Hyderabad 1 U/T Unknown (1 year) Sepsis with positive Allied Sciences BC Nayak Archives of Medicine India Deralakatte 1 U/T 01Jun11 31May12 Sepsis with positive and Health Sciences BC Patel The Indian Journal of India Karamsad 1 NT/D 01Nov07 31Oct11 Bacteraemia Pediatrics Tudu Journal of Evoluation India Kenduadihi 1 U/T 01Jun13 31Aug13 Sepsis with positive of Medical and Dental BC Science Venkatna Journal of Nepal India Pune 1 U/T 01Jan11 01Jul12 Sepsis with positive rayan Paediatric Society BC 2015 Agarwal Journal of India Mangalore 1 U/T 01Feb14 31Jul14 Sepsis with positive International Medicine BC and Dentistry Ambade Journal of Medical India Dhule 1 U/T 01Aug12 31Jul14 Sepsis with positive Science and Clinical BC Research Chapagai Journal of the Nepal Kathmandu 1 Paediatric 01Aug14 01Aug15 Sepsis with positive n Nepalese Health BC Research Council Dhanalak Journal of Clinical and India Madurai 1 U/T 01Dec13 30Sep2014 Sepsis with positive shmi Diagnostic Research BC Gupta International Journal India Rohtak 1 NT/D Unknown (1year) Bacteraemia of Pharma and Bio Sciences Kamble International Journal India Ambajogai 1 U/T 01Jun08 21Dec10 Sepsis with positive of Current BC © 2020 Bielicki JA et al. JAMA Network Open. Microbiology and Applied Sciences Madavi International Journal India Nagpur 1 U/T 01Aug11 01Sep13 Sepsis with positive of Current Research BC and Review Marwah Indian Pediatrics India Chandigarh 1 U/T 01Jan08 31Dec12 Bacteraemia Muley Journal of Global India Pune 1 NT/D Unknown Bacteraemia Infectious Diseases Ponugoti Journal of Medical India Nellore 1 U/T Unknown (6 months) Sepsis with positive Science And Clinical BC Research Sarangi International Journal India Bhubaneswar 1 U/T 01Nov12 30Apr14 Sepsis with positive of Advances in BC Medicine Ting Journal of Republic Taipei 1 U/T 01Jan02 31Dec11 CA bacteraemia, Microbiology, of limited to 0-7 day- Immunology and (Taiwan) olds Infection Tran Journal of Vietnam Da Nang 1 Maternity/ 01Nov10 31Oct11 Sepsis with positive Perinatology Paediatric BC 2016 Abu Medical Journal of Malaysia Baru 1 U/T 01Jan01 31Dec11 CA bacteraemia Malaysia Selayang excluding EOS Amin International Journal India Vadodara 1 U/T 01Apr13 30Sep13 Sepsis with positive of Pharmaceutical BC Sciences and Research DeNIS Lancet Global Health India Delhi 3 U/T 18Jul11 28Feb14 Sepsis with positive BC Jiang Internal Medicine China Missing 1 Maternity/ 01Jan08 31Dec12 Sepsis with positive Paediatric BC Lu Journal of Pediatrics China Chongqing 1 Paediatric 01Jan90 31Dec14 Sepsis with positive and Child Health BC Mahmood Pakistan Journal of Pakistan Faisalabad 1 U/T 01Jan13 01Jan15 Bacteraemia Medical and Health Sciences Pandita International Journal India Dehradun 1 U/T 01Jan13 30Jun15 Sepsis with positive of Contemporary BC Pediatrics Singh European Journal of India Raipur 1 U/T 01Jan13 31Dec13 Sepsis with positive Pharmaceutical and BC Medical Research Thakur Indian Journal of India Tanda 1 NT/D 01Apr12 31Mar13 Sepsis with positive Medical Microbiology BC © 2020 Bielicki JA et al. JAMA Network Open. Ullah Archives of Iranian Pakistan Peshawar 1 U/T 01Jan12 31Dec15 Bacteraemia Medicine 2017 Dalal International Journal India Rohtak 1 U/T 01Jul10 30Sep13 Sepsis with positive of Research in BC Medical Sciences Dong BMC Pediatrics China Bengbu 1 NT/D 01Jan10 31Aug14 Sepsis with positive BC Ingale International Journal India Pune 1 U/T Unknown (1 year) Sepsis with positive of Contemporary BC Pediatrics Kanodia Journal of College of Nepal Dharan 1 U/T 01Jan14 31Dec14 Sepsis with positive Medical Sciences BC Nepal Panigrahi Journal of India Multiple in 2 NT/D 01Apr02 31Mar05 Invasive bacterial Perinatology area of infections Odisha Pavan Journal of Family India Dindigul 1 NT/D 01Oct13 30Sep15 Sepsis with positive Medicine and Primary BC Care Roy Journal of India New Delhi 1 U/T 01Jan11 31Dec14 Bacteraemia Postgraduate Medicine Sari Asian Journal of Indonesi Yogyakarta 1 U/T 01Jan14 31Dec15 Bacteraemia Pharmaceutical and a Clinical Research 2018 Dhaneria Diseases India Ujjain 1 U/T 01Jun12 31Jan14 Nosocomial bacteraemia, including EONS and LONS Fox- Emerging Infectious Cambodi Siem Reap 1 Paediatric 01Jan07 31Dec16 Invasive bacterial Lewis Diseases a infections Jajoo PloS One India Delhi 1 NT/D 01Jul11 31Jan15 Sepsis with positive BC Pokhrel BMC Pediatrics Nepal Lalitpur 1 U/T 15Apr14 15Apr17 Sepsis with positive BC Wang Journal of Tropical China Chongqing, 2 U/T 01Jan03 31Dec13 Nosocomial Pediatrics Henan bacteraemia Yadav BMC Research Notes Nepal Kathmandu 1 Paediatric 01Apr15 30Sep15 Sepsis with positive BC 2019 Li Medicine China Shanghai 1 U/T 01Jan13 31Aug17 Sepsis with positive BC U/T hospital: University/Tertiary hospital; NT/D hospital: Non-teaching/District hospital *Start year of data collection for all studies with exception of Lu et al, 2016 in the 2000s, end year for all studies in the 2000s. © 2020 Bielicki JA et al. JAMA Network Open. eTable 2. Information on Sample Processing Provided in Included Publications Publication year, First author Species identification Antibiotic susceptibility Interpretive guidelines Other comments testing method 2014 Adhikari Yes (Standard Yes (Disc diffusion) Yes (CLSI M2-A9, 2006) bacteriological techniques) Anderson No details provided Yes (Disc diffusion) Yes (CLSI M100-S20, 2010) ESBL detection by (standard blood culture) cefpodoxime screening with confirmation by CLSI-recommended disc diffusion methods Javali No details provided Yes (Disc diffusion) Yes (CLSI, 2008) (standard blood culture) Khanal Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S16, 2007) bacteriological techniques) Methta Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S18, 2010) Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing Mustafa Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) ESBL confirmation by bacteriological techniques) phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion) Nayak Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Use of control strains bacteriological techniques) Meropenem SIR based on imipenem susceptibility testing Patel Yes (BacT/ALERT, API) Yes (automated API) No details provided Tudu Yes (BacT/ALERT, API) Yes (Disc diffusion) Yes (CLSI, no specified) Gentamicin SIR based on amikacin susceptibility testing, meropenem SIR based on imipenem susceptibility testing Venkatnara No details provided No details provided No details provided Gentamicin SIR based yan on amikacin susceptibility testing 2015 Agarwal Yes (BacT/ALERT, Vitek Yes (Disc diffusion) Yes (CLSI M02-A11, 2012) ESBL confirmed using II) CLSI-recommended disc diffusion methods, © 2020 Bielicki JA et al. JAMA Network Open. MRSA detection using cefoxitin disc Ambade Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) bacteriological techniques) Chapagain No details provided No details provided No details provided Gentamicin SIR based on amikacin susceptibility testing Dhanalaksh Yes (Standard Yes (Disc diffusion) No details provided mi bacteriological techniques) Gupta Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S24, 2014) Use of control strains bacteriological techniques) Kamble Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Extensive detail on bacteriological techniques) testing for ESBL and Metallo-beta-lactamases provided Meropenem SIR based on imipenem susceptibility testing Madavi No details provided No details provided No details provided Meropenem SIR based on imipenem susceptibility testing Marwah Yes (Standard No details provided Yes (CLSI, incorrect Meropenem SIR based bacteriological techniques) (standard methods) referencing) on imipenem susceptibility testing Muley Yes (standard Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) bacteriological techniques) Ponugoti Yes (standard Yes (Disc diffusion) Yes (CLSI M2A7 Vol.20 No1 Meropenem SIR based bacteriological techniques) & 2, 2000) on imipenem susceptibility testing Sarangi Yes (BacT/ALERT) Yes (automated API) No details provided Ting No details provided No details provided Yes (CLSI, not specified) Tran Yes (Standard Yes (Disc diffusion) No details provided Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing 2016 Abu Yes (API/Vitek) Yes (Disc diffusion) Yes (CLSI M100-S24) ESBL confirmation by phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion) Amin Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Microbiology laboratory bacteriological techniques) accredited by National Accreditation Board for © 2020 Bielicki JA et al. JAMA Network Open. Testing and Calibration Laboratory in India DeNIS Yes (Standard No details provided Yes (CLSI M100-S21 & Flowchart of sample bacteriological techniques) M100-S22 & M100-S23, handling provided in 2011-2013) web-extra material Jiang Yes (BacT/ALERT, Yes (Disc diffusion or Yes (CLSI, not specified) API/Vitek) Etests) Lu No details provided No details provided No details provided Results recorded based on routine laboratory testing Meropenem SIR based on imipenem susceptibility testing Mahmood No details provided No details provided No details provided Standard procedures for sample processing and interpretation Pandita Yes (Bactec/API) Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) Meropenem SIR based on imipenem susceptibility testing Singh Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S18, 2008) Gentamicin SIR based bacteriological techniques) on amikacin susceptibility testing Thakur Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S21, 2011) Use of control strains, bacteriological techniques) MRSA screening using cefoxitin disc, ESBL screening using ceftazidime disc, confirmation of ESBL by double disc synergy test Meropenem SIR based on imipenem susceptibility testing Ullah Yes (Standard Yes (Disc diffusion) Yes (CLSI, not specified) Meropenem SIR based bacteriological techniques) on imipenem susceptibility testing 2017 Dalal No details provided Yes (Disc diffusion) No details provided Meropenem SIR based (standard blood culture) susceptibility testing Dong Yes (BacT/ALERT) Yes (Disc diffusion) No details provided Additional information on species identification © 2020 Bielicki JA et al. JAMA Network Open. and susceptibility testing provided in methods Ingale Yes (Bactec/API) Yes (Disc diffusion) Yes (CLSI M100-S23, 2013) Extensive detail on microbiological sample handling provided Kanodia No details provided Yes (Disc diffusion) No details provided Panigrahi Yes (Bactec/API) No details provided Yes (CLSI M23-A2, 2001) Extensive detail on microbiological sample handling provided Meropenem SIR based on imipenem susceptibility testing Pavan Yes (Bactec/API) Yes (automated API) No details provided Roy Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S19, 2009) Extensive detail on bacteriological techniques) microbiological sample handling provided; ESBL confirmation by phenotypic confirmatory test (ceftazidime/cefotaxime +/- clavulanate disc diffusion); Use of control strains; MRSA screening using oxacillin disc Gentamicin SIR based on amikacin susceptibility testing Sari Yes (Vitek) Yes (Disc diffusion) No details provided 2018 Dhaneria Yes (Standard Yes (Disc diffusion, Yes (CLSI M100-S21, 2011) Extensive detail on bacteriological techniques) confirmation using microbiological sample Vitek 2) handling provided Meropenem SIR based on imipenem susceptibility testing Fox-Lewis Yes (Standard Yes (Disc diffusion or Yes (CLSI, 2012) Meropenem SIR based bacteriological techniques) Etests) on imipenem susceptibility testing Jajoo Yes (Bactec/Vitek) No details provided Yes (CLSI M100-S21 & Aminoglycosides and M100-S22 & M100-S23, carbapenems grouped in 2011-2013) susceptibility reporting Pokhrel Yes (Bactec) Yes (Disc diffusion) Yes (CLSI M100-S24, 2014) © 2020 Bielicki JA et al. JAMA Network Open. Wang Yes (Vitek/API) Yes (Disc diffusion) Yes (CLSI, 2015) Use of control strains, ESBL screening using ceftazidime disc, confirmation of ESBL by combination discs Meropenem SIR based on imipenem susceptibility testing Yadav Yes (Standard Yes (Disc diffusion) Yes (CLSI M100-S23, 2014) Use of control strains bacteriological techniques) 2019 Li No details provided Yes (Disc diffusion) Yes (CLSI, not specified) CLSI: Clinical and Laboratory Standards Institute; ESBL: extended-spectrum beta-lactamases; MRSA: methicillin-resistant Staphylococcus aureus © 2020 Bielicki JA et al. JAMA Network Open. eTable 3. Relative Incidence of Bacteria in Included Studies Publication year, Bacteria reported in studies (% incidence within study shown) First author 2 Adhikari 94 57 27 4 1 11 43 100 Anderson* 75 3 3 11 5 4 12 1 1 1 3 49 3 3 1 85 85 Javali 32 9 34 13 19 9 9 6 50 77 Khanal 61 77 10 4 2 7 23 100 Mehta 169 5 2 9 4 5 14 5 56 89 98 Mustafa 62 11 23 35 7 24 89 100 Nayak 67 20 3 5 4 3 31 4 20 82 96 Patel* 249 5 12 10 10 2 47 6 1 6 81 93 Tudu 22 5 9 18 9 5 55 91 95 Venkatnarayan 15 13 20 13 7 47 80 92 2 Agarwal* 34 15 9 24 3 27 21 90 100 Ambade 119 6 10 14 35 13 22 90 100 Chapagain 30 7 7 3 3 80 90 97 Dhanalakshmi 41 10 10 68 5 7 85 94 Gupta 325 12 5 13 8 2 8 13 20 20 83 94 Kamble 71 14 1 17 7 1 6 23 21 7 1 79 98 Madavi 103 19 1 16 6 1 7 22 17 5 <1 1 5 77 92 Marwah 167 15 7 15 47 16 84 84 Muley 48 10 6 17 35 8 23 93 100 Ponugoti 188 2 3 15 22 19 1 25 2 12 83 97 Sarangi 74 3 5 62 11 8 8 3 30 79 Ting* 36 31 3 8 42 17 34 34 Tran 75 23 31 3 8 24 5 5 1 68 99 2 Abu* 29 21 3 3 3 21 35 3 7 3 62 63 Amin 101 23 4 12 13 28 8 13 97 100 DeNIS 998 22 15 14 4 6 17 7 1 12 1 82 98 Jiang* 131 1 1 43 19 6 5 13 3 1 6 1 1 50 88 Lu* 929 3 26 14 3 7 12 2 4 6 5 18 49 66 © 2020 Bielicki JA et al. JAMA Network Open. Total bacterial isolates Acinetobacter spp. Burkholderia spp. Citrobacter spp. CONS E. coli Enterobacter spp. Enterococcus spp. H. influenzae Klebsiella spp. L. monocytogenes Morganella spp. N. meningitidis Proteus spp. Pseudomonas spp. S. agalactiae S. aureus S. pneumoniae S. pyogenes Salmonella spp. Serratia spp. Other streptococci Others % accounted for by 7 target species % accounted for by 7 target species excluding CONS Mahmood 341 48 <1 17 9 26 <1 91 91 Pandita 124 6 6 26 11 6 2 27 3 8 1 4 63 85 Singh 141 5 27 4 50 8 7 96 100 Thakur* 188 1 4 19 5 5 10 15 2 40 76 93 Ullah 1534 2 53 7 6 13 20 <1 93 94 2 Dalal 356 15 4 12 1 2 4 47 12 93 100 Dong* 93 73 6 2 1 11 1 1 2 1 1 23 88 Ingale 48 13 25 2 6 10 29 13 2 75 100 Kanodia 327 14 1 2 3 3 4 1 6 62 3 93 96 Panigrahi* 56 14 2 52 20 12 88 88 Pavan 28 11 21 4 36 4 24 72 72 Roy* 2112 21 21 8 5 8 25 12 67 85 Sari 225 9 9 28 9 22 14 9 54 75 2 Dhaneria* 46 17 11 24 9 13 21 5 69 83 Fox-Lewis* 185 9 2 14 10 1 32 1 3 18 1 9 1 86 85 Jajoo 300 15 4 1 14 11 8 5 <1 18 <1 1 1 1 6 1 1 <1 <1 <1 12 64 75 Pokhrel* 69 12 20 4 19 33 3 2 4 2 2 73 90 Wang 571 39 18 3 17 5 2 16 43 70 Yadav 59 12 2 10 7 10 15 7 36 2 87 96 2 Li* 339 <1 44 10 1 6 9 <1 5 6 5 1 3 10 36 64 *a priori exclusion of contaminants with or without definitions for exclusion process provided includes A. baumannii, A. lwoffi includes E. cloacae includes K. pneumoniae, K. ornithinolytica, K. oxytoca, K. ozaenae includes P. aeruginosa includes S. marcescens, S. rubidaea © 2020 Bielicki JA et al. JAMA Network Open.

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JAMA Network OpenAmerican Medical Association

Published: Feb 12, 2020

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