Open Forum Infectious Diseases MAJOR ARTICLE Creation and Assessment of a Clinical Predictive Calculator and Mortality Associated With Candida krusei Bloodstream Infections 1 2 3 2 2 Ryan Kronen, Kevin Hsueh, Charlotte Lin, William G. Powderly, and Andrej Spec 1 2 3 Washington University School of Medicine, St Louis, Missouri; and Division of Infectious Diseases and Department of Medicine, Washington University School of Medicine, St Louis, Missouri Background. Candida krusei bloodstream infection (CK BSI) is associated with high mortality, but whether this is due to under- lying comorbidities in ae ff cted patients or the organism itself is unknown. Identifying patient characteristics that are associated with CK BSI is crucial for clinical decision-making and prognosis. Methods. We conducted a retrospective analysis of hospitalized patients with Candida BSI at our institution between 2002 and 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and laboratory values. Multivariable logistic and Cox regression were used to identify risk factors associated with CK and mortality, respectively. Results. We identified 1873 individual patients who developed Candida BSI within the study period, 59 of whom had CK BSI. CK BSI was predicted by hematologic malignancy, gastric malignancy, neutropenia, and the use of prophylactic azole antifun- gals, monoclonal antibodies, and β-lactam/β-lactamase inhibitor combinations. The C-statistic was 0.86 (95% confidence interval, 0.81–0.91). The crude mortality rates were 64.4% for CK BSI and 41.4% for non-CK BSI. Although CK was associated with higher mortality in univariable Cox regression, this relationship was no longer significant with the addition of the following confounders: lymphoma, neutropenia, glucocorticoid use, chronic liver disease, and elevated creatinine. Conclusions. Six patient comorbidities predicted the development of CK BSI with high accuracy. Although patients with CK BSI have higher crude mortality rates than patients with non-CK BSI, this difference is not significant when accounting for other patient characteristics. Keywords. Candida krusei; candidemia; clinical predictive model; mortality; risk factors. Candida bloodstream infection (BSI) is the most common form surgery, and age exhibit a high level of heterogeneity across stud- of invasive candidiasis, the fourth leading cause of bloodstream ies [9–12]. Most of these studies are limited by CK sample sizes infections in the United States, and the most common nosoco- of less than 35, and they oen ft analyze for non-albicans Candida , mial BSI with non-albicans BSI, constituting a larger propor- which is influenced by C. glabrata, which has different risk fac- tion of total infections in recent decades [1–3]. es Th e species tors [7–11]. In addition, few studies have addressed the question include C. glabrata, C. tropicalis, C. parapsilosis, and C. krusei of whether the organism is directly responsible for the observed (CK). Together with C. albicans, they make up the vast majority increase in mortality, or whether this association is confounded of Candida BSI. Although relatively rare, CK BSI is known to by other patient characteristics. While several authors have ae ff ct immunocompromised patients and is associated with the examined risk factors for mortality within Candida BSI cohorts highest mortality among the Candida species [4–6]. , the multivariable survival models needed to definitively e uniq Th ue factors contributing to the development of infec- answer this question are lacking. Given the poor outcomes asso- tion by this organism and its clinical consequences are poorly ciated with CK BSI, accurately identifying patients at risk for this infection could be of benefit to clinicians. characterized. Some predisposing factors, such as hematologic We performed a retrospective cohort analysis of all Candida malignancy, are established in the literature as risk factors for BSI infections at our institution over a 13-year period. The pur - CK BSI [7, 8]. Other risk factors including antibiotic exposure, pose of this study was to identify pertinent clinical comorbidi- ties that could accurately predict CK BSI as well as to assess the Received 8 August 2017; editorial decision 10 November 2017; accepted 5 February 2018. impact of comorbidities on the elevated mortality rate seen in Correspondence: A. Spec, MD, MSCI, Infectious Disease Clinical Research Unit, 4523 Clayton Ave., Campus Box 8051 St Louis, MO, 63110-0193 (firstname.lastname@example.org). these patients. Open Forum Infectious Diseases © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative METHODS Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any Setting medium, provided the original work is not altered or transformed in any way, and that the work We collected data from patients admitted to Barnes Jewish is properly cited. For commercial re-use, please contact email@example.com Hospital between January 2002 and January 2015, a 1315-bed DOI: 10.1093/ofid/ofx253 C. krusei Calculator and Mortality • OFID • 1 Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 tertiary care hospital in St. Louis, Missouri. The study was of automated chart extraction and medical chart review by the approved by the Washington University in St. Louis Institutional authors, we collected Candida species and a series of patient Review Board with a waiver of consent. characteristics (Table 1; Supplementary Tables 1 and 2). The most extreme vital signs (highest temperature, respiratory Cohort Construction rate, and heart rate; lowest blood pressure) measured within 24 Patients ≥18 years old who were hospitalized and had at least hours preceding BSI were collected. Comorbidities were deter- 1 blood culture positive for Candida were eligible for study mined via ICD-9 codes (Supplementary Table 1) and included inclusion. Subsequent Candida BSI episodes in the same all Elixhauser comorbidities as they have been shown to be patient within 90 days were excluded. Through a combination good predictors of death in acute illness . We categorized Table 1. Comparison of Characteristics Between Patients With Candida krusei Bloodstream Infections and Those With Non-CK Bloodstream Infections a b Characteristic CK (n = 59) Non-CK (n = 1814) P Value Total (n = 1873) Demographics Age, median (IQR), y 57 (21) 59 (24) .5210 59 (24) Female sex 25 (42.4) 869 (47.9) .4024 894 (47.7) Race .0175 White 43 (72.9) 1134 (62.5) 1177 (62.8) African American 10 (17.0) 588 (32.4) 598 (31.9) Other 6 (10.2) 92 (5.1) 98 (5.2) Malignancy Leukemia 33 (55.9) 199 (11.0) <.0001 232 (12.4) Lymphoma 8 (13.6) 84 (4.6) .0070 92 (4.9) Hematologic 42 (71.2) 304 (16.8) <.0001 346 (18.5) Gastric 2 (3.4) 20 (1.1) .1509 22 (1.2) Other potential predisposing factors Bone marrow transplant 9 (15.3) 27 (1.5) <.0001 36 (1.9) Cancer chemotherapy 12 (20.3) 99 (5.5) .0001 111 (5.9) Neutropenia 17 (28.8) 120 (6.6) <.0001 137 (7.3) Laboratory values Absolute neutrophils count, median (IQR) 2.0 (11.2) 5.8 (6.2) .0092 5.7 (6.4) Neutropenia (ANC < 500) 23 (41.8) 150 (8.6) <.0001 173 (9.6) Platelets, median (IQR) 8 (78.5) 148 (175) <.0001 143 (178) Dichotomized creatinine (reference: ≤1) 21 (38.2) 883 (50.3) .0771 904 (49.9) Medications ordered within 90 days prior to Candida BSI Azole 17 (28.8) 138 (7.6) <.0001 155 (8.3) Fluconazole 14 (23.7) 123 (6.8) <.0001 137 (7.3) Voriconazole 3 (5.1) 10 (0.6) .0068 13 (0.7) Clotrimazole 1 (1.7) 13 (0.7) .3622 14 (0.8) Itraconazole 0 (0) 4 (0.2) 1.0000 4 (0.2) Ketoconazole 0 (0) 1 (0.1) 1.0000 1 (0.05) Monoclonal antibodies 8 (13.6) 16 (0.9) <.0001 24 (1.3) Antilymphocyte 5 (8.5) 10 (0.6) <.0001 15 (0.8) Antimyeloid 1 (1.7) 2 (0.1) .0916 3 (0.2) Anti-TNF 3 (5.1) 4 (0.2) .0010 7 (0.4) Corticosteroids 36 (61.0) 493 (27.2) <.0001 529 (28.2) Antiherpes antivirals 31 (52.5) 240 (13.2) <.0001 271 (14.5) Antimetabolites 23 (39.0) 157 (8.7) <.0001 180 (9.6) Calcineurin inhibitors 14 (23.7) 76 (4.2) <.0001 90 (4.8) Cytotoxic agents 6 (10.2) 36 (2.0) .0016 42 (2.2) Mitotic inhibitors 7 (11.9) 40 (2.2) .0005 47 (2.5) mTOR inhibitors 3 (5.1) 14 (0.8) .0147 17 (0.9) Descriptive statistics for additional variables are presented in Supplementary Table 2. Abbreviations: BMI, body mass index; CK, Candida krusei; IQR, interquartile range; mTOR, mechanistic target of rapamycin; TNF, tumor necrosis factor; TPN, total parenteral nutrition. Unless otherwise specified, characteristics are dichotomized and reported as absolute frequency (percent). P values for continuous variables were based on Mann-Whitney U statistical tests, while categorical variable P values were obtained from either chi-square or Fisher exact tests, as appropriate. The most extreme vital signs (highest temperature, respiratory rate, and heart rate; lowest blood pressure) measured within 24 hours preceding BSI were collected. 2 • OFID • Kronen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 continuous variables that did not follow a linear distribution, Significant and relevant descriptive comparison between and when significantly different, odds ratios were noted for dif- CK and non-CK BSI can be found in Table 1, and the others in ferent levels of the variable in univariate analyses. Supplementary Table 2. Age and sex distributions were similar between the 2 groups, while CK BSI was diagnosed more oen ft Outcomes in white patients (72.9% vs 62.5%). Many comorbidities were For logistic regression, the outcome was defined as CK BSI vs present at similar rates between the groups. However, patients non-CK BSI. For survival analysis, we assessed 90-day all-cause with CK BSI were significantly more likely to have hematologic mortality. Dates of death were extracted from the hospital con- cancer (71.2% vs 16.8%), and were also significantly more likely sortium’s Medical Informatics database and supplemented to have a history of bone marrow transplant (15.3% vs 1.5%) with information from the Social Security Death Index (SSDI). and to have received chemotherapy (20.3% vs 5.5%). CK BSI Patients with a positive Candida blood culture and without con- patients were more likely to have received certain medications firmed death who were not observed in our institution after the within the 90 days leading up to the incident infection, includ- 90-day postdiagnosis period were censored at the date of last visit. ing azole antifungals, echinocandins, and corticosteroids. Statistical Analysis Clinical Predictive Model Statistical analysis was performed using SAS v9.4 Software (SAS In univariate logistic regression analyses, 65 variables were Institute Inc., Cary, NC), and all tests were 2-tailed. Survival found to be associated with the development of CK BSI graphs were created using SPSS V23 (IBM, Armonk, NY). For (Supplementary Table 3). descriptive statistics, we used chi-square or Fisher exact tests for Six variables were included in the final multivariable logistic categorical variables and Mann-Whitney U tests for continuous regression model: hematologic malignancy (odds ratio [OR], variables, as the variables were not normally distributed. 10.7; 95% confidence interval [CI], 5.1–22.4), gastric malig- We performed univariate logistic regression to evaluate the nancy (OR, 14.7; 95% CI, 3.0–72.8), neutropenia (OR, 2.1; 95% association of predisposing factors, comorbidities, medication CI, 1.1–4.1), prior azole use (OR, 2.4; 95% CI, 1.2–4.7), prior use, and laboratory values with the development of CK BSI. We monoclonal antibody use (OR, 5.4; 95% CI, 2.0–14.9), and performed univariable Cox proportional hazards analysis to β-lactam/β-lactamase inhibitor use (OR, 2.4; 95% CI, 1.3–4.7) evaluate the association of these same factors with 90-day mor- within 90 days prior to Candida BSI (Table 2). Prior monoclo- tality. Variables with P < .20 were evaluated in the multivariable nal antibody use included all patients receiving antilymphocyte models. antibodies, antimyeloid antibodies, and/or anti–tumor necrosis We developed the multivariable models in a parsimonious factor antibodies. The C-statistic for this model was 0.86 (95% manner, adding candidate variables sequentially and retaining CI, 0.81–0.91) (Figure 1). them in the model if they were found to be significant (P < .05). Aer a ft ll relevant variables were included, those that were no Mortality longer found to be significant were sequentially removed from Mortality was increased in CK BSI patients compared with the model. We generated a C-statistic and receiver operating non-CK BSI patients in univariable analysis (64.4% vs characteristic (ROC) curve using the final set of predictor var - 41.4%; hazard ratio [HR], 1.8; 95% CI, 1.3–2.4) (Figure 2, iables. The Cox proportional hazards model was constructed Supplementary Table 4). in a similar manner, with the exception that the initial model Five variables were included in the final multivariable Cox contained Candida species as the dependent variable, dichoto- model: neutropenia (HR, 2.0; 95% CI, 1.6–2.5), lymphoma (HR, mized as CK vs non-CK, and all other variables were tested in 1.5; 95% CI, 1.1–2.0), prior glucocorticoid use (HR, 1.4; 95% the model as confounders (changed the CK parameter estimate CI, 1.2–1.7), chronic liver disease (HR, 2.0; 95% CI, 1.6–2.5), by at least 15% in either direction). and creatinine >1 mg/dL (HR, 2.1; 95% CI, 1.8–2.5) (Table 3). With the addition of these covariates, the association between RESULTS CK BSI and mortality was no longer significant (HR, 1.3; 95% CI, 0.9–1.8) (Figure 2). Demographics A total of 1913 hospitalized patients were diagnosed with DISCUSSION Candida BSI in the study period. Forty observations were dis- carded due to duplication, incomplete data collection, and not CK is associated with high mortality and resistance to antifun- fulfilling inclusion/exclusion criteria, resulting in 1873 obser- gal agents; therefore, understanding the underlying risk factors vations analyzed. Of these, 59 were due to CK. Absolute and for development of the infection and mortality in this popu- relative frequency of CK BSI did not significantly change over lation is of high clinical importance. Hematologic malignancy, time. CK constituted between 1.8% and 5.4% of total Candida gastric malignancy, neutropenia, prior azole use, prior mono- BSI events between 2002 and 2015, and there has been no con- clonal antibody use, and prior β-lactam/β-lactamase inhibitor sistent trend over time (Supplementary Figure 1). use are independent risk factors for the development of CK BSI C. krusei Calculator and Mortality • OFID • 3 Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Table 2. Clinical Predictive Calculator for Candida krusei vs Other Candida Bloodstream Infection Variable Parameter Estimate Odds Ratio (95% CI) P Value Intercept –5.1811 n/a n Hematologic malignancy 2.3664 10.659 (5.067–22.422) <.0001 Gastric malignancy 2.6862 14.676 (2.957–72.849) .0010 Neutropenia 0.7471 2.111 (1.091–4.086) .0266 Prior azole use 0.8623 2.369 (1.204–4.658) .0125 Prior monoclonal antibody use 1.6884 5.411 (1.964–14.910) .0011 Prior β-lactam/β-lactamase 0.8880 2.430 (1.251–4.722) .0088 inhibitor use PCK = () -- (. 5 1811++ 2.* 3664 XX 12.* 6862 20 ++ .* 7471 XX 30.* 8623 4 41 ++ .* 6884 XX 50.* 888 6) 1+e where X = hematologic malignancy, X = gastric malignancy, X = neutropenia, X = prior azole use, X = prior monoclonal antibody use, and X = prior β-lactam/β-lactamase inhibitor use. 1 2 3 4 5 6 Abbreviations: CI, confidence interval; CK, Candida krusei. Includes any history or diagnosis of leukemia, lymphoma, or multiple myeloma. b 3 Defined as absolute neutrophil count <500/mm . Medication was ordered within 90 days prior to Candida infection. in our multivariable logistic regression model. The discrimi- to constitute 3.2% of all Candida BSI at our institution, which is nating performance of this model is higher than some of the consistent with a previously published range of 0.9%–10% [7, 12, most commonly used clinical predictive models . Although 16–18]. A systematic review of Candida BSI prevalence world- patients with CK BSI appear to have a higher mortality rate than wide found that CK consistently made up 1%–4% of infections non-CK BSI patients, this association is no longer significant regardless of geographic location, with the exception of 2 studies when taking into account confounders, specifically neutrope- conducted in Finland and France with proportions of 8.5% and nia, lymphoma, prior glucocorticoid use, chronic liver disease, 10.6%, respectively . There is no consensus as to whether and elevated creatinine. the incidence of CK is truly increasing, as results from different While CK is 1 of 5 Candida species composing >90% of all studies are conflicting. However, the majority of studies suggest isolates, it is also the least common of these . We found CK a stable epidemiology, consistent with our findings [4 , 19, 20]. It is possible that the studies that show a rise in CK are either outli- ers or represent localized changes in epidemiology. Several of the risk factors associated with CK BSI in our cohort 1.00 have been corroborated in previous studies. Observationally, CK is disproportionately isolated in hematology units [7, 21] and neutropenia and hematologic malignancy are consistently 0.75 associated with CK BSI across multiple studies [8, 11, 12, 16]. A prospective cohort of patients diagnosed with Candida BSI between 2004 and 2008 found that CK BSI was more com- 0.50 mon in the setting of prior use of antifungal agents, hemato- logic malignancy, stem cell transplantation, neutropenia, and corticosteroid therapy, although the authors did not adjust for multiple variables . In our analysis, bone marrow transplan- 0.25 tation and corticosteroid therapy were significant in univariate analyses but did not meet criteria for inclusion in multivariable analyses, appearing to have no increase in predictability in add- 0.00 ition to hematologic malignancy and neutropenia. We found a specific predilection for CK BSI in patients with 0.00 0.25 0.50 0.75 1.00 1-Speciﬁcity prior azole use, which has biological plausibility given the known intrinsic resistance to fluconazole (and other azoles, to a lesser Figure 1. Receiver operating characteristic curve for the multiple logistic regres- extent) in this species [20, 23, 24]. Various studies have suggested sion predicting Candida krusei bloodstream infection. The C-statistic is 0.8618 (95% increased risk in the setting of fluconazole exposure, with 1 study confidence interval, 0.8094–0.9141). Predictor variables are hematologic malig- demonstrating dose dependency [25–28]. Although 1 case-con- nancy, gastric malignancy, neutropenia, and prior azole, monoclonal antibody, and β-lactam/β-lactamase inhibitor use within 90 days prior to the Candida infection. trol analysis found no relationship between fluconazole and CK 4 • OFID • Kronen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Sensitivity A 1.0 B 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0153045607590 0153045607590 Time, days Time, days Figure 2. Univariable (A) and multivariable (B) proportional hazards model stratified by Candida krusei bloodstream infection (dashed) vs other Candida bloodstream infec- tion (solid). Time was measured from the date of first positive culture. Patients were censored at either date of death or date last seen, as reflected in the medical chart and the Social Security Death Index. The multivariable model is adjusted for lymphoma, neutropenia, glucocorticoid use, chronic liver disease, and elevated creatinine. BSI, this study included only 4 patients with CK . Given that However, the significance of the interaction between certain azole prophylaxis is oen g ft iven in the setting of severe immu- antibiotic classes and Candida species is unclear. Similarly, while nosuppressive disease (eg, hematologic malignancy and trans- the ability of monoclonal antibodies to significantly ae ff ct immu- plantation), some authors have suggested association rather than nologic mechanisms suggests a pathway by which low-virulence a true biological causation . However, in 1 study limited to organisms such as CK gain a foothold in an otherwise overly adult patients with leukemia or status post–bone marrow trans- hostile environment, this area requires further investigation. plant, fluconazole prophylaxis was still associated with CK BSI In our multivariable analysis, gastric malignancy was the . The authors posited that emergence of a relatively low-viru- only solid tumor significantly associated with the development lence organism such as CK was aided by the suppression of other of CK BSI. Gastrointestinal (GI) inoculation as a source of CK more virulent Candida species susceptible to fluconazole. BSI may explain this association, as disruption of the GI barrier In addition to azoles, β-lactam/β-lactamase inhibitors and by tumor cells and associated inflammation could potentially monoclonal antibodies were associated with CK BSI. Although lead to higher inoculation rates, although this mechanism is antibiotics have been infrequently studied in this setting, 1 theoretical and may not be specific to CK [32, 33]. prior case-control study found that β-lactams, vancomycin, and CK BSI was associated with higher mortality as compared with non-CK BSI in the univariate analysis (64.4% vs 41.4%; aminoglycosides were associated with CK BSI, with the strong- HR, 1.8; 95% CI, 1.3–2.4). The mortality in our study is gener - est association seen for antibiotics with anaerobic activity . ally consistent with that cited by other authors [34–36]. While Presumably, antibiotics may predispose individuals to Candida mortality with CK BSI tends to be higher than non-CKI BSI in BSI through alteration of the microbiome at sites of inoculation. the majority of studies, this difference is oen n ft ot statistically significant, likely related to low power in the setting of infre- quent CK infection [8, 12, 34]. In 1 study with comparable sam- Table 3. Multivariable Proportional Hazards (Cox) Model Predicting Mortality in Patients With Candida Bloodstream Infection ple sizes and species distributions, CK was found to have the highest 90-day mortality rate (52.9%) of all species when com- Variable Hazard Ratio (95% CI) P Value paring them individually . Another study found CK BSI to Candida krusei 1.297 (0.909–1.849) .1514 be associated with a similarly poor 90-day mortality of 46.4%, Neutropenia 1.984 (1.593–2.472) <.0001 compared with 38.7% for all Candida BSI . Lymphoma 1.488 (1.124–1.970) .0055 c Our multivariable survival analysis suggests that the higher Prior glucocorticoid use 1.425 (1.218–1.667) <.0001 crude mortality seen with CK BSI reflects the underlying sever - Chronic liver disease 2.005 (1.593–2.525) <.0001 Elevated creatinine 2.125 (1.835–2.461) <.0001 ity of illness in these patients rather than pathogenic virulence of the organism . Indeed, in vitro and in vivo virulence Abbreviation: CI, confidence interval. Models Candida krusei in comparison with all other Candida species. testing has demonstrated that CK is a relatively low-virulence b 3 Defined as absolute neutrophil count <500/mm . organism [18, 33], although several unique intrinsic mecha- Medication was ordered within 90 days prior to Candida infection. nisms are protective against both antifungal medications and Defined as >1 mg/dl. C. krusei Calculator and Mortality • OFID • 5 Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Proportion Surviving Proportion Surviving 4. Abbas J, Bodey GP, Hanna HA, et al. Candida krusei fungemia. An escalating oxidative stress [37, 38]. While hematologic malignancy pre- serious infection in immunocompromised patients. Arch Intern Med 2000; dicted CK BSI, only lymphoma was an independent predictor 160:2659–64. 5. Viudes A, Pemán J, Cantón E, et al. Candidemia at a tertiary-care hospital: epi- of mortality. One possible explanation for this discrepancy may demiology, treatment, clinical outcome and risk factors for death. Eur J Clin be the convergence of leukemia and neutropenia as they relate Microbiol Infect Dis 2002; 21:767–74. to mortality, whereas other sources of immune dysfunction in 6. Klingspor L, Tortorano AM, Peman J, et al. Invasive Candida infections in surgi- cal patients in intensive care units: a prospective, multicentre survey initiated by hematologic malignancy may contribute to the development of the European Confederation of Medical Mycology (ECMM) (2006–2008). Clin CK BSI. Regardless of their role in the initial CK BSI, liver dis- Microbiol Infect 2015; 21:87.e1–.e10. 7. Nawrot U, Pajączkowska M, Fleischer M, et al. Candidaemia in polish hospi- ease, kidney dysfunction, and immunosuppression appear to be tals—a multicentre survey. Mycoses 2013; 56:576–81. strong mediators of CK BSI–associated mortality. 8. Ortega M, Marco F, Soriano A, et al. Candida species bloodstream infection: epi- demiology and outcome in a single institution from 1991 to 2008. J Hosp Infect This study is limited by retrospective data collection. While the 2011; 77:157–61. database was built to maximize comprehensiveness, ICD-9 codes 9. Playford EG, Marriott D, Nguyen Q, et al. Candidemia in nonneutropenic critic- ally ill patients: risk factors for non-albicans Candida spp. Crit Care Med 2008; may not always reflect true diagnoses, leading to misclassification 36:2034–9. bias. Additionally, we were unable to identify changes in diagnostic 10. Schuster MG, Meibohm A, Lloyd L, Strom B. Risk factors and outcomes of accuracy and management over time that may have contributed to Candida krusei bloodstream infection: a matched, case-control study. J Infect 2013; 66:278–84. variations in mortality, although mortality in this cohort did not 11. Rodríguez D, Almirante B, Cuenca-Estrella M, et al; Barcelona Candidemia appear to change significantly over time. Despite a large overall Project Study Group. Predictors of candidaemia caused by non-albicans Candida species: results of a population-based surveillance in Barcelona, Spain. Clin sample size, our analyses were based on comparison with only 59 Microbiol Infect 2010; 16:1676–82. patients with CK BSI due to the relative infrequency of infection 12. Pfaller M, Neofytos D, Diekema D, et al. Epidemiology and outcomes of candi- demia in 3648 patients: data from the Prospective Antifungal Therapy (PATH by this species. This limited our statistical power, although this is Alliance®) registry, 2004–2008. Diagn Microbiol Infect Dis 2012; 74:323–31. the largest study to look at patients with CK BSI. Finally, this study 13. Das I, Nightingale P, Patel M, Jumaa P. Epidemiology, clinical characteristics, and was conducted at a single tertiary care academic center and thus outcome of candidemia: experience in a tertiary referral center in the UK. Int J Infect Dis 2011; 15:e759–63. may not be generalizable to other populations. 14. Johnston JA, Wagner DP, Timmons S, et al. Impact of different measures of In conclusion, we found that a collection of patient comor- comorbid disease on predicted mortality of intensive care unit patients. Med Care 2002; 40:929–40. bidities could both predict the development of CK BSI and 15. Chen JY, Zhang AD, Lu HY, et al. CHADS2 versus CHA2DS2-VASc score in assess- explain the increased mortality seen in these patients. ing the stroke and thromboembolism risk stratification in patients with atrial fibril- lation: a systematic review and meta-analysis. J Geriatr Cardiol 2013; 10:258–66. Supplementary Data 16. Almirante B, Rodríguez D, Park BJ, et al; Barcelona Candidemia Project Study Group. Epidemiology and predictors of mortality in cases of Candida blood- Supplementary materials are available at Open Forum Infectious Diseases stream infection: results from population-based surveillance, Barcelona, Spain, online. Consisting of data provided by the authors to benefit the reader, from 2002 to 2003. J Clin Microbiol 2005; 43:1829–35. the posted materials are not copyedited and are the sole responsibility of 17. Ding X, Yan D, Sun W, et al. Epidemiology and risk factors for nosocomial the authors, so questions or comments should be addressed to the corre- non-Candida albicans candidemia in adult patients at a tertiary care hospital in sponding author. North China. Med Mycol 2015; 53:684–90. 18. Atalay MA, Koc AN, Demir G, Sav H. Investigation of possible virulence factors in Candida strains isolated from blood cultures. Niger J Clin Pract 2015; 18:52–5. Acknowledgments 19. Falagas ME, Roussos N, Vardakas KZ. Relative frequency of albicans and the We would like to acknowledge Cherie Hill and Dorothy Sinclair for their various non-albicans Candida spp among candidemia isolates from inpatients in help in constructing the patient database. various parts of the world: a systematic review. Int J Infect Dis 2010; 14:e954–66. Financial support. This study was funded by Astellas Pharma, Inc., 20. Pfaller MA, Diekema DJ, Gibbs DL, et al; Global Antifungal Surveillance Group. through an investigator-sponsored grant (CRES-17B01). Astellas Pharma, Candida krusei, a multidrug-resistant opportunistic fungal pathogen: geographic Inc., was not involved in study design, implementation, data analysis, man- and temporal trends from the ARTEMIS DISK Antifungal Surveillance Program, 2001 to 2005. J Clin Microbiol 2008; 46:515–21. uscript drafting, or the final approval for publication. This was the sole 21. Gokcebay DG, Yarali N, Isik P, et al. Candida associated bloodstream infections responsibility of the authors. In addition, research reported in this publica- in pediatric hematology patients: a single center experience. Mediterr J Hematol tion was supported by the Washington University Institute of Clinical and Infect Dis 2016; 8:e2016018. Translational Sciences grant UL1TR002345 from the National Center for 22. Horn DL, Neofytos D, Anaissie EJ, et al. Epidemiology and outcomes of candi- Advancing Translational Sciences (NCATS) of the National Institutes of demia in 2019 patients: data from the Prospective Antifungal Therapy Alliance Health (NIH). The content is solely the responsibility of the authors and Registry. Clin Infect Dis 2009; 48:1695–703. does not necessarily represent the official view of the NIH. 23. Hani U, Shivakumar HG, Vaghela R, et al. Candidiasis: a fungal infection–current Potential conifl cts of interest. All authors: no reported conflicts of challenges and progress in prevention and treatment. Infect Disord Drug Targets interest. All authors have submitted the ICMJE Form for Disclosure of 2015; 15:42–52. 24. Samonis G, Kofteridis DP, Saloustros E, et al. Candida albicans versus non-albi- Potential Conflicts of Interest. Conflicts that the editors consider relevant to cans bloodstream infection in patients in a tertiary hospital: an analysis of micro- the content of the manuscript have been disclosed. biological data. Scand J Infect Dis 2008; 40:414–9. 25. Chow JK, Golan Y, Ruthazer R, et al. Factors associated with candidemia caused References by non-albicans Candida species versus Candida albicans in the intensive care 1. Antinori S, Milazzo L, Sollima S, et al. Candidemia and invasive candidiasis in unit. Clin Infect Dis 2008; 46:1206–13. adults: a narrative review. Eur J Intern Med 2016; 34:21–8. 26. Lortholary O, Renaudat C, Sitbon K, et al; French Mycosis Study Group. 2. Arendrup MC. Epidemiology of invasive candidiasis. Curr Opin Crit Care 2010; Worrisome trends in incidence and mortality of candidemia in intensive care 16:445–52. units (Paris area, 2002–2010). Intensive Care Med 2014; 40:1303–12. 3. Magill SS, Edwards JR, Bamberg W, et al; Emerging Infections Program 27. Labbé AC, Pépin J, Patiño C, et al. A single-centre 10-year experience with Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Candida bloodstream infections. Can J Infect Dis Med Microbiol 2009; 20:45–50. Team. Multistate point-prevalence survey of health care-associated infections. N 28. Puig-Asensio M, Padilla B, Garnacho-Montero J, et al; CANDIPOP Project; Engl J Med 2014; 370:1198–208. GEIH-GEMICOMED (SEIMC); REIPI. Epidemiology and predictive factors for 6 • OFID • Kronen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018 early and late mortality in Candida bloodstream infections: a population-based 34. Bassetti M, Taramasso L, Nicco E, et al. Epidemiology, species distribution, anti- surveillance in Spain. Clin Microbiol Infect 2014; 20:O245–54. fungal susceptibility and outcome of nosocomial candidemia in a tertiary care 29. Lin MY, Carmeli Y, Zumsteg J, et al. Prior antimicrobial therapy and risk for hos- hospital in Italy. PLoS One 2011; 6:e24198. pital-acquired Candida glabrata and Candida krusei fungemia: a case-case-con- 35. Choi HK, Jeong SJ, Lee HS, et al. Blood stream infections by Candida glabrata and trol study. Antimicrob Agents Chemother 2005; 49:4555–60. Candida krusei: a single-center experience. Korean J Intern Med 2009; 24:263–9. 30. Girmenia C, Pagano L, Leone G, Martino P. Fluconazole and Candida krusei 36. Pongrácz J, Juhász E, Iván M, Kristóf K. Significance of yeasts in bloodstream infec- fungemia. Arch Intern Med 2001; 161:2267–9. tion: epidemiology and predisposing factors of candidaemia in adult patients at a 31. Wingard JR, Merz WG, Rinaldi MG, et al. Increase in Candida krusei infection university hospital (2010-2014). Acta Microbiol Immunol Hung 2015; 62:317–29. among patients with bone marrow transplantation and neutropenia treated 37. Whaley SG, Berkow EL, Rybak JM, et al. Azole antifungal resistance in Candida prophylactically with fluconazole. N Engl J Med 1991; 325:1274–7. albicans and emerging non-albicans Candida species. Front Microbiol 2016; 32. Krcmery V, Barnes AJ. Non-albicans Candida spp. causing fungaemia: patho- 7:2173. genicity and antifungal resistance. J Hosp Infect 2002; 50:243–60. 38. Costa-de-Oliveira S, Sampaio-Marques B, Barbosa M, et al. An alternative res- 33. Arendrup MC. Candida and candidaemia. Susceptibility and epidemiology. Dan piratory pathway on Candida krusei: implications on susceptibility profile and Med J 2013; 60:B4698. oxidative stress. FEMS Yeast Res 2012; 12:423–9. C. krusei Calculator and Mortality • OFID • 7 Downloaded from https://academic.oup.com/ofid/article-abstract/5/2/ofx253/4848650 by Ed 'DeepDyve' Gillespie user on 16 March 2018
Open Forum Infectious Diseases – Oxford University Press
Published: Feb 1, 2018
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