Background: Development of sepsis is a process with significant variation among individuals. The precise elements of this variation need to be defined. This study was designed to define the way in which comorbidities contribute to sepsis development. Methods: Three thousand five hundred nine patients with acute pyelonephritis (AP), community-acquired pneumonia (CAP), intraabdominal infections (IAI) or primary bacteremia (BSI) and at least two signs of the systemic inflammatory response syndrome were analyzed. The study primary endpoint was to define how comorbidities as expressed in the Charlson’s comorbidity index (CCI) and the underlying type of infection contribute to development of organ dysfunction. The precise comorbidities that mediate sepsis development and risk for death among 18 comorbidities recorded were the secondary study endpoints. Results: CCImorethan2hadanodds ratioof5.67for sepsis progression in patients with IAI between significantly higher than AP and BSI. Forward logistic regression analysis indicated seven comorbidities that determine transition into sepsis in patients with AP, four comorbidities in CAP, six comorbidities in IAI and one in BSI. The odds ratio both for progression to sepsis and death with one comorbidity or with two and more comorbidities was greater than in the absence of comorbidities. Conclusions: The study described how different kinds of infection vary in the degree to which they lead to sepsis. The number of comorbidities that enhances the risk of sepsis and death varies depending on the underlying infections. Keywords: Infection, Sepsis, Comorbidities, Mortality, Intrabdominal Background the Sepsis-3 expert panel as an expression of the Despite progress in our understanding of the mechanism of co-morbidities . pathogenesis, sepsis remains a leading cause of death. The Since 2006, the Hellenic sepsis study Group (HSSG) is Sepsis-3 expert committee developed diagnostic criteria for collectively collecting clinical data for patients with sepsis in which co-morbidities played a considerable role. infections presenting with at least two signs of the According to their analysis, clinical signs prognostic of the systemic inflammatory response syndrome (SIRS). Re- added risk for death to the risk coming from comorbidities sults from these studies on the traits of the innate and of were used to develop the diagnostic criteria for sepsis . the adaptive immune activation as well as on genotyping The Charlson’s co-morbidity index (CCI) was applied by characteristics indicated that progression to organ dysfunction varied greatly among individuals and it was * Correspondence: email@example.com dependent on the type of infection [3, 4]. 4th Department of Internal Medicine, National and Kapodistrian University We have recently re-classified all the patients in our of Athens, Medical School, Athens, Greece 4th Department of Internal Medicine, ATTIKON University Hospital, 1 Rimini database into non-sepsis and sepsis according to the Street, 12462 Athens, Greece new Sepsis-3 definitions . We asked the question if Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 2 of 9 co-morbidities of patients admitted in the emergency The study secondary endpoints were: a) the precise co- department (ED) influence the development of organ morbidities that influence development of sepsis within dysfunction and whether this depends on the underlying the subgroups of patients with a specific infection; b) the infection. We tried to identify how each of the individual influence of the number of comorbidities for the devel- co-morbidities and how their constellation, expressed by opment of sepsis within the subgroups of patients with a the CCI, impacts on the development of organ failure specific infection; c) the precise comorbidities that and final outcome. impact on 28-day mortality within the subgroups of patients with a specific infection; d) the influence of the Methods number of comorbidities on 28-day mortality within the Study design subgroups of patients with a specific infection; and e) if This is the analysis of the prospective collection of clinical comorbidities as expressed by the CCI have a different information for patients admitted with at least two signs impact for 28-day mortality in relation to the underlying of SIRS at the ED of 38 hospitals in Greece from January type of infection. 2007 until September 2016. The study protocol was approved by the Ethics Committees of the participating Statistical analysis hospitals. Written informed consent was provided by the The Sepsis-3 expert panel has decided to introduce 90% patients or by a legal representative in case of patients sensitivity as the cut-off of discrimination in the analysis unable to consent. The study design and study endpoints of Receiver Operator Characteristics (ROC) curves for were defined before the start of the study. variables that influence sepsis outcome . As a conse- Inclusion criteria were: a) age equal to or more than quence, we selected 90% sensitivity as the criterion to 18 years; b) both genders; c) written informed consent; d) define a value of CCI that can discriminate an adequate presence of at least two signs of SIRS as defined elsewhere probability for death after 28 days in the entire population. ; and e) acute pyelonephritis (AP), community-acquired Specificity, positive and negative predictive value of the pneumonia (CAP), intraabdominal infections (IAI) and selected cut-off of CCI for 28-day mortality were also primary bacteremia (BSI) as the cause of SIRS. These calculated. The odds ratio and 95% confidence intervals infections were defined according to internationally (CIs) for sepsis compared to non-sepsis at the selected accepted criteria [7–9]. Exclusion criteria were: a) age CCI cut-off was calculated for patients with and without a below 18 years; b) deny to consent; c) neutropenia defined specific type of infection; ORs were compared by the as an absolute neutrophil count lower than 1000/mm for Tarone’s test. The same analysis of ORs was done for reason other than SIRS; and d) any metastatic solid tumor 28-day mortality. To define the role of each comorbidity, malignancy. frequencies of each of the 18 comorbidities among For all patients the following information was recorded: non-sepsis and sepsis patients and among survivors and demographics, sequential organ failure assessment (SOFA) non-survivors were compared within each infection score, acute physiology and chronic health evaluation sub-group by the Fisher exact test. Comorbidities with a (APACHE) II score, CCI, co-morbidities and 28-day p-value of difference less than 0.05 entered a logistic outcome. Eighteen comorbidities were recorded: type 2 forward conditional regression analysis to define the diabetes mellitus, chronic heart failure, chronic obstruct- precise comorbidities that influence patients within each ive pulmonary disorder (COPD), chronic renal disease, specific infection. The OR and 95%CIs for sepsis and for solid tumor malignancy, any hematological malignancy, 28-day mortality in relation to the number of comorbidi- chronic intake of corticosteroids, coronary heart disease, ties was calculated; ORs were compared by the Tarone’s vascular hypertension, atrial fibrillation, dyslipidemia, test. Any value of p below 0.05 was considered significant. obesity, history of stroke, dementia, nephrolithiasis, gallstones, liver cirrhosis and depression based on each Results patient medical history. The study flow chart is shown in Fig. 1. A total of 3509 patients were analyzed; 2341 had sepsis as defined by Study endpoints the new Sepsis-3 definitions. The baseline characteristics The study primary endpoint was to define if CCI interacts of these patients are shown in Table 1. additively with the underlying type of infection for the development of organ dysfunction. At the original study Primary study endpoint protocol, organ dysfunctions were defined by the 2001 ROC curve analysis conducted in the overall study popula- definitions. After the publication of the new Sepsis-3 defi- tion showed that CCI more than 2 was accompanied by nitions, it was decided to re-classify all patients in the 89.3% sensitivity (86.9–91.2%) to predict 28-day mortality database as non-sepsis and sepsis based on total SOFA (Fig. 2). Figure 3 shows the ORs for the development of score equal to or more than 2 . sepsis in relation to the underlying infection for patients Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 3 of 9 Fig. 1 Study flow chart. AP: acute pyelonephritis; BSI: primary bacteremia; CAP: community-acquired pneumonia; ED: emergency department; IAI: intraabdominal infection; SIRS: systemic inflammatory response syndrome; SOFA: sequential organ failure assessment with CCI more than 2. Findings suggest that although the within each infection subgroup. At first, comparisons were OR for sepsis was significantly increased under the pressure done to define the comorbidities that differ between of CCI more than 2 for all types of infection, this effect was non-sepsis and sepsis patients within each infection farmorepronounced forpatients withIAIs. subgroup. The analysis indicated 10 comorbidities that differ between non-sepsis and sepsis patients in the case of Secondary study endpoints AP (Additional file 1: Table S1), seven comorbidities in the The next question was what the exact co-morbidities are case of CAP (Additional file 2: Table S2), 11 comorbidities that can enhance the development of sepsis among patients in the case of IAIs (Additional file 3:Table S3)and three Table 1 Comparison of demographics of patients without sepsis and with sepsis in relation to the underlying infection Acute pyelonephritis Community-acquired Intra-abdominal infections Primary bacteremia pneumonia No sepsis Sepsis p No sepsis Sepsis p No sepsis Sepsis p No sepsis Sepsis p Number of 542 901 146 853 399 334 81 253 patients Male gender 145 (26.8) 361 (40.0) < 0.0001 78 473 (55.4) 0.897 180 148 1.000 40 123 1.000 (n, %) (53.4) (45.2) (45.1) (49.4) (48.6) Age 61.1 ± 22.0 74.8 ± 15.3 < 0.0001 61.4 ± 20.2 75.3 ± 14.9 < 0.0001 55.4 ± 24.3 74.7 ± < 0.0001 63.3 ± 19.7 72.9 ± 14.5 < 0.0001 (mean ± SD, years) 14.8 APACHE II score 9.2 ± 6.0 16.5 ± 7.4 < 0.0001 8.4 ± 4.8 18.4 ± 7.6 < 0.0001 7.5 ± 4.9 17.5 ± 8.9 < 0.0001 9.2 ± 4.6 19.8 ± 7.5 < 0.0001 (mean ± SD) SOFA score 0.4 ± 0.5 5.9 ± 3.0 < 0.0001 0.7 ± 0.5 5.8 ± 3.2 < 0.0001 0.2 ± 0.5 5.3 ± 3.0 < 0.0001 0.5 ± 0.5 5.9 ± 3.4 < 0.0001 (mean ± SD) CCI 3.0 ± 2.5 5.0 ± 2.6 < 0.0001 2.7 ± 2.3 4.8 ± 2.4 < 0.0001 2.3 ± 2.3 4.6 ± 2.4 < 0.0001 3.0 ± 2.0 4.5 ± 2.4 < 0.0001 (mean ± SD) 28-day mortality 21 208 (23.1) < 0.0001 7 323 < 0.0001 17 109 < 0.0001 4 98 < 0.0001 (n, %) (3.9) (4.8) (37.9)* (4.3) (32.6)* (4.9) (38.6)* Abbreviations: APACHE acute physiology and chronic health evaluation, CCI Charlson’s comorbidity index, SOFA sequential organ failure assessment *p < 0.0001 compared to the respective mortality of acute pyelonephritis Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 4 of 9 Fig. 2 Charlson’s comorbidity index (CCI) influences final outcome. a ROC curve of CCI for 28-day mortality. b Prognostic performance of CCI more than 2 for 28-day mortality. NPV: negative predictive value; PPV: positive predictive value; Se: sensitivity; Sp: specificity Fig. 3 Modulation of the risk for sepsis in relation to the underlying infection and the Charlson’s comorbidity index (CCI). Each line represents the odds ratios and confidence intervals (CI) for death of each individual infection when CCI is more than 2 compared to CCI ≤2. AP: acute pyelonephritis; BSI: primary bacteremia; CAP: community-acquired pneumonia; IAI: intraabdominal infection Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 5 of 9 comorbidities in the case of BSI (Additional file 4:Table One major question was whether the number of comor- S4). These comorbidities entered into a logistic forward bidities may influence the susceptibility for sepsis. Figure 4 conditional regression analysis to conclude which are the shows the ORs for sepsis for each individual infection precise comorbidities that are associated with the develop- under the pressure of one or at least two comorbidities ment of sepsis within each subgroup. Seven comorbidities compared to the absence of comorbidities. Even the were found in the case of AP, four in the case of CAP, six in presence of at least one of the comorbidities listed in the case of IAIs and only one in the case of BSI (Table 2). Table 2 increased significantly the risk for sepsis. In all Among patients with type 2 diabetes mellitus, the need for types of infection, the OR under the pressure of two or intake of insulin for glycemic control did not modify the more comorbidities was significantly greater than under risk for sepsis compared to diabetic patients without insulin the pressure of only one comorbidity. intake in the case of AP (OR: 1.29; 95%CIs: 0.86–1.95; p: The comorbidities found in Table 2 to impose consider- 0.212), of CAP (OR: 1.15; 95%CIs: 0.46–2.87; p: 0.761) and ably for the development of sepsis entered into a logistic of IAIs (OR: 1.84; 95%CIs: 0.87–3.90; p: 0.113). Among pa- forward conditional regression analysis to decipher their tients with chronic heart failure, those at end stage had impact on 28-day mortality within infection subgroups. Six greater risk for sepsis in the case of AP (OR: 5.40; 95%CIs: comorbidities were found in the case of AP, two in the case 2.22–13.14; p < 0.0001) but not of CAP (OR: 1.73; 95%CIs: of CAP, six in the case of IAIs and only one in the case of 0.54–5.56; p: 0.357). Among patients with chronic renal BSI (Table 3). Among patients with chronic heart failure, disease, the number of patients who developed sepsis and those at end stage had greater risk for sepsis in the case of who were on chronic hemodialysis was too low to allow AP (OR: 1.18; 95%CIs: 0.68–2.05; p: 0.543). Among patients stratification by disease severity. with type 2 diabetes mellitus, the need for intake of insulin for glycemic control did not modify the risk for death in Table 2 Impact of precise co-morbidities on the development the case of IAIs (OR: 1.21; 95%CIs: 0.58–2.51; p: 0.607). of sepsis Figure 5 shows the odds ratios for death by each infection Co-morbidity Odds 95% confidence p-value under the pressure of one or at least two comorbidities ratio intervals compared to the absence of comorbidities. Even the pres- Patients with acute pyelonephritis ence of at least one of the comorbidities listed in Table 3 Type 2 diabetes mellitus 1.31 1.02–1.68 0.033 significantly increased the risk for death. In all types of Chronic heart failure 1.93 1.39–2.69 < 0.0001 infection, the OR for death under the pressure of two or Chronic renal disease 29.31 9.26–92.86 < 0.0001 more comorbidities was significantly greater than under the pressure of only one comorbidity. Non-metastatic solid tumor 2.03 1.40–2.89 < 0.0001 malignancy Although the OR for 28-day mortality was significantly Corticosteroid intake 2.08 1.08–3.98 0.028 increased under the pressure of CCI more than 2 for all types of infection, this effect was far more pronounced Stroke 1.70 1.21–2.39 0.002 for patients with IAIs (Fig. 6). Dementia 1.97 1.37–2.84 < 0.0001 Patients with community-acquired Discussion pneumonia The present analysis managed to demonstrate that the risk Type 2 diabetes mellitus 1.73 1.06–2.82 0.027 for the development of sepsis, as this is defined by the new Chronic heart failure 1.99 1.13–3.49 0.016 Sepsis-3 definitions, is modified in an individualized way in Coronary heart disease 3.72 1.69–8.19 0.001 relation to the type of underlying infection. Findings clearly Dementia 3.44 1.64–7.24 0.001 showed that comorbidities increased considerably the risk for sepsis and for unfavorable outcome after 28-days and Patients with intraabdominal infections that this effect varied greatly with the number of existing Type 2 diabetes mellitus 3.29 2.15–5.03 < 0.0001 comorbidities. When using CCI as an expression of the constellation of comorbidities of the host, it was found that Chronic renal disease 26.77 3.51–204.37 0.002 the susceptibility for both the development of sepsis and Corticosteroid intake 3.86 1.41–10.53 0.008 death after 28 days was far greater under the pressure of Atrial fibrillation 2.73 1.41–5.28 0.003 CCI more than 2 in intraabdominal infections that with Dementia 9.33 3.79–22.97 < 0.0001 any other type of infection. Regarding the influence of Liver cirrhosis 9.16 1.06–79.53 0.044 individual comorbidities, some comorbidities like type 2 Patients with primary bacteremia diabetes mellitus, chronic renal disease and dementia were associated with sepsis risk in almost all types of infection. Dementia 8.55 1.12–65.20 0.038 Others like chronic heart disease and non-metastatic tumor Only variables remaining significant after the final step of logistic forward conditional regression analysis are included malignancy introduced sepsis risk in CAP and IAI, whereas Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 6 of 9 Fig. 4 Modulation of the risk for sepsis in relation to the underlying infection and the number of comorbidities. Each line represents the odds ratios and confidence intervals (CI) for sepsis in the presence of one or at least two comorbidities, as defined for each infection in Table 1. P values represent comparisons with patients without any comorbidity. The p-values of comparisons between odds ratio for one comoborditiy and for at least two comorbidities are: *0.00002; **0.033; 0.0018 Table 3 Impact of precise co-morbidities on 28-day mortality Co-morbidity Odds 95% confidence p-value coronary heart disease introduced risk only in CAP. Atrial ratio intervals fibrillation and liver cirrhosis increased sepsis risk only in Patients with acute IAI. Surprisingly, the risk for sepsis after BSI was increased pyelonephritis only in demented patients. A similar pattern was found Chronic heart failure 2.54 1.82–3.55 < 0.0001 regarding the impact of each comorbidity on 28-day Chronic renal disease 1.71 1.13–2.60 0.011 mortality. It should be underscored that risk for sepsis and Non-metastatic solid 2.13 1.44–3.17 < 0.0001 death was not significantly modified among patients with tumor malignancy advanced type 2 diabetes mellitus and chronic heart failure Corticosteroid intake 2.05 1.09–3.84 0.024 compared to the less advanced disease state. Stroke 2.97 2.12–4.17 < 0.0001 There is a great difference between susceptibility to an infection and susceptibility to inflammation. Many studies Dementia 2.18 1.51–3.15 < 0.0001 have shown the role of type 2 diabetes mellitus, solid tumor Patients with community- and hematologic malignancies, liver cirrhosis, atrial fibrilla- acquired pneumonia tion and coronary heart disease for susceptibility to Coronary heart disease 1.87 1.30–2.69 0.001 infections [6–10]. In this study, we try to define which Dementia 2.20 1.53–3.17 < 0.0001 comorbidities elicit progression to organ dysfunction once Patients with intraabdominal an infection has started. Of course this cannot be done with infections comparison of infected patients with healthy controls. Type 2 diabetes mellitus 1.84 1.16–2.93 0.010 Instead we compared various non-serious with serious Chronic renal disease 2.67 1.12–6.35 0.026 community-acquired infections admitted to the ED. Our Non-metastatic solid tumor 3.03 1.79–5.13 < 0.0001 findings agree at someaspects anddisagreeat someother malignancy aspects with the current ideas about comorbidities that Atrial fibrillation 2.23 1.11–4.48 0.024 define risk for severity. A typical example is the case of Dementia 3.69 1.87–7.25 < 0.0001 CAP. Severity of CAP is defined by the pneumonia severity index (PSI) in which the history of five disorders i.e. Liver cirrhosis 4.59 1.20–17.51 0.025 neoplastic disease, congestive heart failure, cerebrovascular Patients with primary bacteremia disease, renal disease and liver disease are taken into consideration . Our analysis shows that among these Dementia 3.87 1.65–9.12 0.002 five comorbidities only chronic heart disease leads the Only variables remaining significant after the final step of logistic forward conditional regression analysis are included development of organ dysfunction. Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 7 of 9 Fig. 5 Modulation of the risk for death after 28 days in relation to the underlying infection and the number of comorbidities. Each line represents the odds ratios and confidence intervals (CI) for death in the presence of one or at least two comorbidities, as defined for each infection in Table 2. P values represent comparisons with patients without any comorbidity. The p-values of comparisons between odds ratio for one moborditiy and for at least two comorbidities are: *0.00002; 0.0029. **could not be calculated because one value was zero The impact of diabetes mellitus type 2 on the final both short- and long-term outcomes was found and this outcome of patients with sepsis is a matter of debate. A was accompanied with lack of differences in the levels of comparison of the mortality of 241 diabetic patients and circulating biomarkers for inflammation, coagulation 863 non-diabetic patients with sepsis was done in the and endothelial activation. The same lack of effect on prospective cohort of the Molecular Diagnosis and Risk clinical outcomes and concentrations of biomarkers was Stratification of Sepsis (MARS) project of two large aca- found after adjustment for treatment with insulin and demic centers in the Netherlands . No difference in metformin . This finding corroborates our results on Fig. 6 Modulation of the risk for 28-day mortality in relation to the underlying infection and the Charlson’s comorbidity index (CCI). Each line represents the odds ratios and confidence intervals (CI) for death of each individual infection when CCI is more than 2 compared to CCI ≤2. P-values are compared by the Tarone’s test. AP: acute pyelonephritis; BSI: primary bacteremia; CAP: community-acquired pneumonia; IAI: intraabdominal infection Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 8 of 9 the lack of effect of type 2 diabetes mellitus as a risk Abbreviations AP: Acute pyelonephritis; APACHE: Acute physiology and chronic health factor for 28-day mortality in AP and CAP. The impact evaluation; BSI: Primary bacteremia; CAP: Community-acquired pneumonia; of type 2 diabetes on the final outcome of CAP was also CCI: Charlson’s co-morbidity index; COPD: Chronic obstructive pulmonary studied in two big cohorts, the GenIMS of 1895 subjects disorder; ED: Emergency department; HSSG: Hellenic sepsis study group; IAI: Intraabdominal infections; MARS: Molecular Diagnosis and Risk with CAP and the Health ABC of 1645 subjects. Mortal- Stratification of Sepsis; PSI: Pneumonia severity index; ROC: Receiver Operator ity was greater among patients with diabetes than Characteristics; SIRS: Systemic inflammatory response syndrome; without diabetes . At first reading, this finding is SOFA: Sequential organ failure assessment opposite to the lack of association between type 2 Funding diabetes and mortality from CAP described in our study. The study was funded by the Hellenic Institute for the Study of Sepsis. However, diabetic patients of the GenIMS and Health Availability of data and materials ABC cohorts had greater risk for death by cardiovascular Data are available upon request by the corresponding author. events . This is partly compatible with our finding for coronary heart disease as an independent risk factor Authors’ contributions DS participated in subjects’ enrolment, drafted the manuscript and gave for death in CAP. approval to the final version for submission. VK, VV, IMK, AP, AS, KEK, KA, MB, Sepsis is a multifactorial process and staging is KT and MC participated in subjects’ enrolment, reviewed the manuscript for necessary to provide personalized treatment targeting intellectual content and gave approval to the final version for submission. EJGB designed the study, analyzed the data, drafted the manuscript and the needs of each patient. This concept has been gave approval to the final version for submission. introduced may years ago where the PIRO system was introduced. The acronym of PIRO stands for pre- Ethics approval and consent to participate disposition through comorbidities, infection, response The protocol was approved by the following Ethics Committees: of the host and organ dysfunction . Our analysis Ethics Committee of “Alexandra” General Hospital of Athens showed for the first time an additive interaction Ethics Committee of 251 Air Force General Hospital of Athens between comorbidities and IAIs that increased the Ethics Committee of ATTIKON University General Hospital of likelihood for sepsis and unfavorable outcome far Athens Ethics Committee of Asklipieion General Hospital of Voula, more than the other types of infection. The data Territory of Athens make clear that the PIRO system should separately Ethics Committee of “Center for Trauma Resuscitation- KAT” stage the significance of the six comorbidities affect- General Hospital of Athens Ethics Committee of “Evangelismos” General Hospital of Athens ing outcome in IAI. Ethics Committee of “Evgenideio” Hospital of Athens Ethics committee of “Hippokrateio” General Hospital of Athens Conclusion Ethics committee of “Hygeia” General Hospital of Athens Ethics Committee of “G. Gennimatas” General Hospital of Athens The results of our study generate the need to consider Ethics Committee of “Laikon” General Hospital of Athens development of sepsis and organ dysfunction after an Ethics Committee of “Konstantopouleio-Aghia Olga” General infection an individualized process. Comorbidities play a Hospital of Athens Ethics Committee of “Korgialeneion-Benakion” General Hospital of major role in this process. However, the comorbidities Athens which facilitate progression into organ dysfunction vary Ethics Committee of “Sismanogleion” General Hospital of Athens according to the underlying infection. Among all type of Ethics Committee of “Sotiria” Athens General Hospital Ethics Committee of “Thriasio” Elefsis General Hospital Territory of infections, IAIs act additively with the comorbidities of Athens the host to potentiate the likelihood for sepsis and the Ethics Committee of “Aghios Dimitrios” General Hospital of risk for unfavorable outcome at an extent much greater Thessaloniki Ethics Committee of “G. Gennimatas” General Hospital of than the other infections. Thessaloniki Ethics Committee of “Aghios Pavlos” General Hospital of Thessaloniki Additional files Ethics Committee of “Theagenio” Hospital of Thessaloniki Ethics Committee of “Metaxa” Hospital of Piraeus Ethics Committee of “Tzaneio” General Hospital of Piraeus Additional file 1: Table S1. Comparison of comorbidities between Ethics committee of University General Hospital of Alexandroupolis patients with infection and sepsis developing in the field of acute Ethics Committee of General Hospital of Argos pyelonephritis. (DOCX 21 kb) Ethics Committee of General Hospital of Arta Additional file 2: Table S2. Comparison of comorbidities between Ethics committee of General Hospital of Chios patients with infection and sepsis developing in the field of community- Ethics Committee of University General Hospital of Ioannina acquired pneumonia. (DOCX 21 kb) Ethics Committee of General Hospital of Karditsa Additional file 3: Table S3. Comparison of comorbidities between Ethics Committee of General Hospital of Korinthos patients with infection and sepsis developing in the field of intraabdominal Ethics Committee of General Hospital of Lamia infections. (DOCX 20 kb) Ethics Committee of University General Hospital of Larisa Ethics Committee of General Hospital of Nicosia Additional file 4: Table S4. Comparison of comorbidities between Ethics committee of General Hospital of Nafplion patients with infection and sepsis developing in the field of primary Ethics Committee of University General Hospital of Patras bacteremia. (DOCX 21 kb) Ethics Committee of General Hospital of Ptolemaida Sinapidis et al. BMC Infectious Diseases (2018) 18:242 Page 9 of 9 Ethics Committee of General Hospital of Sparti Giannikopoulos G, Alexiou Z, Voloudakis N, Koutsoukou A. Individualized Ethics Committee of General Hospital of Trikala significance of the −251 a/T single nucleotide polymorphism of interleukin- Ethics Committee of General Hospital of Zakynthos 8 in severe infections. Eur J Clin Microbiol Infect Dis. 2016;35(4):563–70. 5. Giamarellos-Bourboulis EJ, Tsaganos T, Tsangaris I, Lada M, Routsi C, Sinapidis D, Koupetori M, Bristianou M, Adamis G, Mandragos K, Dalekos GN, Kritselis I, Written informed consent was provided by the patients or by a legal Giannikopoulos G, Koutelidakis I, Pavlaki M, Antoniadou E, Vlachogiannis G, representative in case of patients unable to consent. Koulouras V, Prekates A, Dimopoulos G, Koutsoukou A, Pnevmatikos I, Ioakeimidou A, Kotanidou A, Orfanos SE, Armaganidis A, Gogos C. Validation of Competing interests the new Sepsis-3 definitions: proposal for improvement in early risk EJ Giamarellos-Bourboulis has received honoraria (paid to the University of identification. Clin Microbiol Infect. 2017;23(2):104–9. Athens) from AbbVie, Biotest, Brahms GmbH, and The Medicines Company; 6. Trevelin SC, Carlos D, Beretta M, da Silva JS, Cunha FQ. Diabetes mellitus he has received compensation as a consultant for Astellas Greece (paid to and Sepsis: a challenging association. Shock. 2017;47(3):276–87. the University of Athens); and has received independent educational grants 7. Kim Y, Wie SH, Chang UI, Kim J, Ki M, Cho YK, Lim SK, Lee JS, Kwon KT, Lee (paid to the University of Athens) from AbbVie, Biotest, and Sanofi. He is H, Cheong HJ, Park DW, Ryu SY, Chung MH, Pai H. Comparison of the funded by the FrameWork 7 program HemoSpec and by the Horizon 2020 clinical characteristics of diabetic and non-diabetic women with program European Sepsis Academy (granted to the University of Athens). community-acquired acute pyelonephritis: a multicenter study. J Inf Secur. None of the other authors has any other type of financial conflict to disclose. 2014;69(3):244–51. The other authors declare that they have no competing interests. 8. Garcia-Vidal C, Ardanuy C, Gudiol C, Cuervo G, Calatayud L, Bodro M, Duarte R, Fernandez-Sevilla A, Antonio M, Linares J, Carratala J. Clinical and microbiological epidemiology of Streptococcus pneumoniae bacteremia in Publisher’sNote cancer patients. J Inf Secur. 2012;65(6):521–7. Springer Nature remains neutral with regard to jurisdictional claims in 9. Gustot T, Felleiter P, Pickkers P, Sakr Y, Rello J, Velissaris D, Pierrakos C, published maps and institutional affiliations. Taccone FS, Sevcik P, Moreno C, Vincent JL. Impact of infection on the prognosis of critically ill cirrhotic patients: results from a large worldwide Author details study. Liver Int. 2014;34(10):1496–503. Department of Therapeutics, National and Kapodistrian University of Athens, 10. Zhu J, Zhang X, Shi G, Yi K, Tan X. Atrial fibrillation is an independent risk Medical School, Athens, Greece. 1st Department of Internal Medicine, factor for hospital-acquired pneumonia. PLoS One. 2015;10(7):e0131782. “G.Gennimatas” Athens General Hospital, Athens, Greece. 1st Department of 11. 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BMC Infectious Diseases – Springer Journals
Published: May 29, 2018
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