Microbiology and predictors of mortality in haematological malignancy patients with gram-negative bacterial bloodstream infections

Microbiology and predictors of mortality in haematological malignancy patients with gram-negative bacterial bloodstream infections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbiology and predictors of mortality in haematological malignancy patients with gram-negative bacterial bloodstream infections Jing Zheng, Jinlian Li, Xuejun xu, Yuqing Li, Ya Guo, Jing Hu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4416357/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Bloodstream infection (BSI) in haematological malignancy patients caused by gram-negative bacteria (GNB) poses a clinical challenge, which is exacerbated by the increased dissemination of carbapenem-resistant GNB (CRGNB). In this study, we investigated the prevalence and factors for GNB BSI and mortality in this population. Methods: This retrospective study included haematological malignancy patients who developed GNB BSI between 2015 and 2023 at a tertiary teaching hospital in southern China. Risk factors for CRGNB BSI and mortality of GNB BSI were identified by using multivariate logistic analyses. The patients were randomly divided into training and validation cohorts at a ratio of 7:3 to establish the model of 30-day mortality. C-indices, calibration plots, and decision curve analyses were generated to evaluate the model. A nomogram of the model was established. Results : Among the 351 patients with GNB BSIs, acute myeloid leukaemia (51.3%) was the most common. Escherichia coli (28.8%) and Klebsiella pneumoniae (29.7%) were the most common pathogens of GNB BSI and CRGNB BSI, respectively. The risk factors for CRGNB BSI were chronic liver disease, previous exposure to carbapenems, a platelet count < 30×10 9 /l and an albumin concentration < 30 g/l before BSI. The model for 30-day mortality of GNB BSI included neutropenia and an albumin concentration < 30 g/l before BSI, as well as septic shock and mechanical ventilation after BSI. The C-indices were 0.942 and 0.931 in the training and validation cohorts, respectively. The calibration plots and decision curves indicated that the model had good performance. Conclusions : The identified factors allow for the stratification of patients at greatest risk for CRGNB BSI and poor prognosis for GNB BSI, which could help in facilitating timely effective intervention. haematological malignancy bloodstream infection gram-negative bacteria carbapenem resistance model mortality risk factor Figures Figure 1 Figure 2 Figure 3 Background Bacterial bloodstream infection (BSI) has become one of the most common complications in haematologic malignancy patients, with high morbidity and mortality despite advances in diagnosis, prophylaxis, and management [ 1 , 2 ]. In recent years, studies have shown that gram-negative bacteria (GNB) were the predominant causative pathogens of BSIs compared to gram-positive bacteria, accounting for 64.7–70.4% of bacterial BSIs [ 2 – 4 ]. GNB BSIs are associated with high rates of mortality and other poor outcomes, which further increase due to the dissemination of antibiotic-resistant strains, especially carbapenem-resistant gram-negative bacteria (CRGNB) [ 5 – 9 ], such as Acinetobacter baumannii , Klebsiella pneumoniae , Pseudomonas aeruginosa and Escherichia coli . This poses a considerable challenge to the management of BSI in these severely immunocompromised patients. In addition to increasing mortality, GNB BSIs also greatly increase medical bills and prolong hospital lengths of stay [ 10 , 11 ]. Timely empirical antibiotic treatment is crucial to these vulnerable patients with GNB BSIs. However, inappropriate empirical antibiotic treatment has become frequent due to the increasing incidence of antibiotic resistance worldwide, thus resulting in decreased effectiveness of treatment and the evolution of antibiotic-resistant strains by selective pressure from antibiotics, which may be responsible for increased mortality and morbidity [ 12 , 13 ]. Therefore, the ability to diagnose CRGNB BSIs early before the reporting of antibiotic resistance in positive cultures by microbiology laboratories and to properly treat them in a timely manner could substantially improve patient outcomes [ 14 – 16 ]. Furthermore, it is essential to identify the risk factors for mortality in patients with GNB BSIs and subsequently conduct early intervention and management [ 17 ]. Although previous studies have explored the risk factors for incidence and mortality in haematologic malignancy patients with BSIs [ 3 , 7 , 18 – 20 ], few studies have explored the risk factors for the prognosis of carbapenem-resistant organism infections in haematologic malignancy patients with GNB BSIs or for the prediction of mortality in haematologic malignancy patients with GNB BSIs. In this study, we retrospectively collected clinical characteristic data from patients with haematologic malignancies to analyse the distribution of GNB, identify the key factors associated with CRGNB when the identification of GNB in blood cultures had been completed, and establish a nomogram model for predicting 30-day mortality among haematologic malignancy patients with GNB BSIs. Based on this analysis, we can provide evidence for rationalizing the use of antibiotics and the management of haematologic malignancy patients with GNB BSIs. Methods Setting and patients This retrospective observational study was conducted at a 2500-bed tertiary teaching hospital in Guangzhou, South China. Haematologic malignancy patients (aged ≥ 18 years) with gram-negative bacteraemia who were hospitalized in the haematology and bone marrow transplantation departments (total of 100 beds) from 2015 to 2023 were included. Patients were identified from the records of the microbiology laboratory. Only the first isolate from the blood for each patient was included when multiple positive blood cultures with the same pathogen during the same hospital stay were identified. Patients were included more than once if they developed more than one episode of bacteraemia caused by a different gram-negative pathogen. Data, including demographic characteristics, underlying disease, medical and treatment history, and 30-day follow-up outcomes after BSI, were extracted from medical records. The study was approved by the ethics committee board of the hospital. Microbiological studies Species confirmation and antibiotic susceptibility testing were performed in the microbiology laboratory of the hospital by using the Vitek 2 automated system (bio-Mérieux, France) with the broth microdilution and disk diffusion methods. Antimicrobial susceptibilities were interpreted following the Clinical and Laboratory Standards Institute (CLSI) guidelines. Carbapenem resistance was defined as resistance to one or more carbapenem agents, such as meropenem, ertapenem, or imipenem [ 21 ]. The CRGNB strains included Escherichia coli (CREc), Klebsiella pneumoniae (CRKP), Enterobacter cloacae , Enterobacter aerogenes , Enterobacter species , Citrobacter fruendii , Proteus mirabilis , Proteus vulgaris , Morganella morganii , Serratia marrescens, Acinetobacter baumannii ( CRAB ) and Pseudomonas aeruginosa ( CRPA ) , etc. Definitions GBN BSI was defined as the isolation of gram-negative bacteria from the blood culture with or without infection symptoms (fever or hypothermia). The onset of BSI was considered as the date of collection of the first positive blood culture sample. A hospital-acquired BSI was defined as an infection that occurred 48 h after the patient’s admission; otherwise, it was defined as a community-acquired BSI. Septic shock was defined as a vasopressor requirement for maintaining a mean arterial pressure ≥ 65 mmHg or a serum lactate level ≥ 2 mmol/l [ 22 ]. Empiric antibiotic therapy was defined as one or more antibiotics that the patient received within 48 hours of being diagnosed with BSI (time to draw a positive blood culture specimen). Empiric antibiotic therapy was deemed appropriate if at least one of the utilized empiric antibiotics was a sensitive in vitro antibiotic sensitivity test; otherwise, it was considered to be inappropriate empiric antibiotic therapy. Exposure to prior antimicrobial treatment was defined as any treatment received for at least 48 h in the 30 days before BSI. Absolute neutrophil counts < 0.5×10 9 cells/L were defined as neutropenia. Stem cell transplants included both allogeneic and autologous transplants. Statistical analysis All of the statistical analyses were performed by using SPSS version 26.0 and R version 4.1.2. Continuous variables with a normal distribution are expressed as the mean ± standard deviation (SD) and were analysed by using a t test. Continuous variables with a nonnormal distribution are expressed as the median ± interquartile range (IQR) and were analysed by using the Mann‒Whitney U test. Categorical variables are reported as frequencies and were compared by using chi-square or Fisher’s exact tests. The patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3 to construct and validate the model of 30-day mortality. Multicollinearity was examined by checking the variance inflation factor on a multiple regression model with the same dependent and independent variables. Variables with two-tailed p < 0.10 in the univariate logistic regression analysis were included in the multivariate logistic regression analysis. Backwards stepwise multivariate logistic regression was used to investigate the independent risk factors for infection and mortality. The 30-day mortality probabilities were estimated by using the nomogram based on the results of multivariate logistic regression analysis in the training cohort. Model performance was assessed via discrimination, calibration and clinical usefulness. The C-index was used to evaluate the discrimination ability of the model. C-index values greater than 0.7 suggest a reasonable estimation. The Hosmer–Lemeshow goodness of fit test in multiple logistic regression was performed to assess the model calibration, and calibration curves were plotted. Decision curve analysis (DCA) was conducted to evaluate the net clinical benefit by quantifying the net benefits at different threshold probabilities. Results A total of 351 GNB isolates which obtained from the 307 haematologic malignancies patients with BSIs were included in this study. Of which, 180 (51.3%) episodes occurred in patients with acute myeloid leukaemia, 86 (24.5%) in patients with acute lymphocytic leukaemia, 62 (17.7%) in patients with non-Hodgkin lymphoma, and 23 (6.6%) in patients with other haematologic malignancies. The characteristics of the patients are described in Table 1 . Overall, the mean age was 47.6 years, and 219 (62.4%) of them were male. Septic shock was observed in 157 (44.7%) episodes, and 30-day crude mortality was observed in 86 (24.5%) episodes. Table 1 Demographic and clinical characteristics of the hematological malignancies patients with bloodstream infections in training and validation cohorts. No. (%) Variables Total (n = 351) Training cohort (n = 246) Validation cohort (n = 105) P Age, mean ± SD, years 47.6 ± 16.4 47.5 ± 17.0 47.9 ± 15.0 0.828 Sex, male 219 (62.4) 159 (64.6) 60 (57.1) 0.185 Co-morbid conditions Chronic pulmonary disease 29 (8.3) 20 (8.1) 9 (8.6) 0.891 Chronic liver disease 27 (7.7) 15 (6.1) 12 (11.4) 0.086 Diabetes mellitus 45 (12.8) 35 (14.2) 10 (9.5) 0.227 Chronic renal disease 31 (8.8) 23 (9.4) 8 (7.6) 0.601 Solid tumor 25 (7.1) 17 (6.9) 8 (7.6) 0.813 Nosocomial infection 258 (73.5) 179 (72.8) 79 (75.2) 0.631 CRGNB infection 91 (25.9) 59 (24.0) 32 (30.5) 0.204 Co-infection with any bacteria 54 (15.4) 39 (15.9) 15 (14.3) 0.709 Length of hospital stay b , median (IQR), days 18.0 (12.0, 23.0) 18.5 (12.0, 23.0) 18.0 (11.0, 23.0) 0.692 ICU stay b 32 (9.1) 20 (8.1) 12 (11.4) 0.326 Hematopoietic stem cell transplantation b 25 (7.1) 19 (7.7) 6 (5.7) 0.503 Surgical procedure b 7 (2.0) 6 (2.4) 1 (1.0) 0.620 Mechanical ventilation b 4 (1.1) 2 (0.8) 2 (1.9) 0.739 Intravascular catheter b 300 (85.5) 212 (86.2) 88 (83.8) 0.564 Urinary catheter b 25 (7.1) 17 (6.9) 8 (7.6) 0.813 Nasogastric tube b 7 (2.0) 4 (1.6) 3 (2.9) 0.735 Chemotherapy/Radiotherapy b 286 (81.5) 203 (82.5) 83 (79.1) 0.443 Corticoid therapy b 66 (18.8) 47 (19.1) 19 (18.1) 0.824 Use of immunosuppressors b 66 (18.8) 49 (19.9) 17 (16.2) 0.413 Exposure to antibiotics b () Carbapenems 158 (45) 107 (43.5) 51 (48.6) 0.381 Cephalosporins 6 (1.7) 5 (2.0) 1 (1.0) 0.791 Cephalomycins 15 (4.3) 11 (4.5) 4 (3.8) 1.000 Aminoglycosides 55 (15.7) 40 (16.3) 15 (14.3) 0.641 Tetracycline 50 (14.3) 32 (13.0) 18 (17.1) 0.310 Fluoroquinolone 29 (8.3) 18 (7.3) 11 (10.5) 0.325 Lactamases inhibitors 134 (38.2) 91 (37.0) 43 (41.0) 0.484 Colistin 14 (4.0) 13 (5.3) 1 (1.0) 0.109 Glycopeptides 66 (18.8) 42 (17.1) 24 (22.9) 0.204 Antifungals 163 (46.4) 110 (44.7) 53 (50.5) 0.322 Neutropenia b 245 (69.8) 168 (68.3) 77 (73.3) 0.346 Platelets < 30×10 9 /l b 172 (49.0) 120 (48.8) 52 (49.5) 0.899 Albumin < 30g/l b 55 (15.7) 39 (15.9) 16 (15.2) 0.885 Neutropenia a 275 (78.4) 194 (78.9) 81 (77.1) 0.720 Platelets < 30×10 9 /l a 231 (65.8) 166 (67.5) 65 (61.9) 0.313 Albumin < 30g/l a 136 (38.8) 93 (37.8) 43 (41.0) 0.579 Septic shock a 157 (44.7) 107 (43.5) 50 (47.6) 0.477 ICU stay a 66 (18.8) 46 (18.7) 20 (19.1) 0.939 Surgical procedure a 3 (0.9) 2 (0.8) 1 (1.0) 1.000 Mechanical ventilation a 43 (12.3) 30 (12.2) 13 (12.4) 0.961 Intravascular catheter a 317 (90.3) 220 (89.4) 97 (92.4) 0.392 Urinary catheter a 108 (30.8) 71 (28.9) 37 (35.2) 0.236 Nasogastric tube a 45 (12.8) 31 (12.6) 14 (13.3) 0.851 Chemotherapy/Radiotherapy a 66 (18.8) 45 (18.3) 21 (20.0) 0.708 Corticoid therapy a 33 (9.4) 22 (8.9) 11 (10.5) 0.652 Use of immunosuppressors a 157 (44.7) 109 (44.3) 48 (45.7) 0.808 Appropriate empiric antibiotic therapy a 329 (93.7) 230 (93.5) 99 (94.3) 0.780 30-day morality 86 (24.5) 60 (24.4) 26 (24.8) 0.941 SD: standard deviation; IQR: interquartile range; ICU: intensive care unit. a medical and treatment after bloodstream infection. b medical and treatment history 30 days prior to bloodstream infection. Distribution of bacterial species Among the 351 gram-negative pathogens identified from blood culture, a total of 17 different bacterial species were identified. Escherichia coli was the most common bacterium (28.8%, 101/351), followed by Klebsiella pneumoniae (26.2%, 92/351) and Pseudomonas aeruginosa (23.6%, 83/351); other bacteria were observed in fewer than 10% of the episodes (Fig. 1 ). As shown in Fig. 1 , 91 (25.9%) gram-negative isolates belonging to 12 different bacterial species were carbapenem resistant. CRKP was the most frequently isolated CRGNB (29.7%, 27/91), accounting for 29.3% (27/92) of the Klebsiella pneumoniae ; moreover, CRPA was the second most common CRGNB (25.3%, 23/91), accounting for 27.7% (23/83) of the Pseudomonas aeruginosa , followed by CREc (20.9%, 19/91) and CRAB (13.2%, 12/91), accounting for 18.8% (19/101) of the Escherichia coli and 54.5% (12/22) of the Acinetobacter baumannii , respectively. Other CRGNB were observed in ≤ 2 episodes. Factors associated with CRGNB BSI Among the haematologic malignancies patients with CRGNB BSIs, over half of the patients had acute myeloid leukaemia (58.2%, 53/91); additionally, 24.2% (22/91) had acute lymphocytic leukaemia, and 15.4% (14/91) had non-Hodgkin lymphoma. The incidence of 30-day mortality (50.6% vs. 15.4%; p < 0.001) and septic shock (64.8% vs. 37.7%; p < 0.001) were higher in haematologic malignancies patients with CRGNB BSIs than in those with carbapenem-susceptible gram-negative bacteria (CSGNB) BSIs. The characteristics of the patients with CRGNB BSIs and CSGNB BSIs are outlined in Table 2 . Univariate logistic regression analysis demonstrated that chronic liver disease, nosocomial infection, longer hospitalization, exposure to invasive procedures such as intravascular and urinary catheters, corticoid therapy, previous exposure to antimicrobial therapy (such as lactamase inhibitors, glycopeptides, carbapenems and tetracycline), neutropenia, an albumin concentration < 30 g/l and a platelet count < 30×10 9 /l before BSI were associated with carbapenem resistance in GNB isolates from patients with GNB BSIs ( p < 0.05). Multivariate logistic analysis demonstrated that patients with chronic liver disease (OR: 3.47, 95% CI 1.41–8.57, p = 0.007), a previous exposure to carbapenem therapy (OR: 2.62, 95% CI: 1.51–4.57, p = 0.001), a platelet count < 30×10 9 /l (OR: 1.85, 95% CI: 1.01–3.37, p = 0.046) and an albumin concentration < 30 g/l before BSI (OR: 2.44, 95% CI: 1.26–4.70, p = 0.008) were found to be independent risk factors for BSI caused by CRGNB when GNB BSI was performed (Table 2 ). Table 2 Univariate and multivariate analysis of risk factors for GNB BSI caused by CRGNB. No. (%) Univariate analysis Multivariate analysis Variables CSGNB (n = 260) CRGNB (n = 91) OR (95% CI) P OR (95% CI) P Age, mean ± SD, years 48.0 ± 16.9 46.4 ± 14.9 0.99 (0.98–1.01) 0.428 Sex, male 160 (61.5) 59 (64.8) 0.87 (0.53–1.43) 0.576 Co-morbid conditions Chronic pulmonary disease 20 (7.7) 9 (9.9) 1.32 (0.58–3.01) 0.512 Chronic liver diseases 15 (5.8) 12 (13.2) 2.48 (1.12–5.52) 0.022 3.47(1.41–8.57) 0.007 Diabetes mellitus 35 (13.5) 10 (11.0) 0.79 (0.38–1.67) 0.544 Chronic renal disease 22 (8.5) 9 (9.9) 1.19 (0.53–2.68) 0.679 Solid tumor 20 (7.7) 5 (5.5) 0.70 (0.25–1.92) 0.483 Nosocomial infection 180 (69.2) 78 (85.7) 2.67 (1.4–5.07) 0.003 1.38 (0.66–2.89) 0.386 Length of hospital stay b , median (IQR), days 17.0 (10.8, 22.3) 21.0 (15.5, 27.0) 1.07 (1.03–1.10) < 0.001 1.02 (0.98–1.06) 0.361 ICU stay b 20 (7.7) 12 (13.2) 1.82 (0.85–3.9) 0.117 Surgical procedure b 5 (1.9) 2 (2.2) 1.15 (0.22–6.02) 1.000 Mechanical ventilation b 1 (0.4) 3 (3.3) 8.83 (0.91–85.94) 0.093 9.42 (0.87-102.17) 0.065 Intravascular catheter b 216 (83.1) 84 (92.3) 2.44 (1.06–5.64) 0.032 2.23 (0.89–5.56) 0.085 Urinary catheter b 14 (5.4) 11 (12.1) 2.42 (1.05–5.54) 0.032 1.28 (0.47–3.53) 0.628 Nasogastric tube b 3 (1.2) 4 (4.4) 3.94 (0.86–17.96) 0.142 Chemotherapy/Radiotherapy b 214 (82.3) 72 (79.1) 0.81 (0.45–1.48) 0.501 Corticoid therapy b 41 (15.8) 25 (27.5) 2.02 (1.15–3.57) 0.014 1.76 (0.93–3.32) 0.081 Use of immunosuppressors b 48 (18.5) 18 (19.8) 1.09 (0.60–1.99) 0.782 Hematopoietic stem cell transplantation b 16 (6.2) 9 (9.9) 1.67 (0.71–3.93) 0.233 Exposure to antibiotics b Carbapenems 99 (38.1) 59 (64.8) 3.15 (1.91–5.19) < 0.001 2.62 (1.51–4.57) 0.001 Cephalosporins 5 (1.9) 1 (1.1) 0.57 (0.07–4.91) 0.958 Cephalomycins 14 (5.4) 1 (1.1) 0.20 (0.03–1.5) 0.150 Aminoglycosides 36 (13.9) 19 (20.9) 1.64 (0.89–3.04) 0.112 Tetracycline 25 (9.6) 25 (27.5) 3.56 (1.92–6.6) < 0.001 1.85 (0.93–3.68) 0.081 Fluoroquinolone 20 (7.7) 9 (9.9) 1.32 (0.58–3.01) 0.512 Lactamasesinhibitors 88 (33.9) 46 (50.6) 2.00 (1.23–3.24) 0.005 1.33 (0.75–2.35) 0.327 Colistin 10 (3.9) 4 (4.4) 1.15 (0.35–3.76) 1.000 Glycopeptides 41 (15.8) 25 (27.5) 2.02 (1.15–3.57) 0.014 0.85 (0.43–1.69) 0.647 Antifungals 113 (43.5) 50 (55.0) 1.59 (0.98–2.56) 0.059 0.96 (0.54–1.70) 0.884 Neutropenia b 168 (64.6) 77 (84.6) 3.01 (1.61–5.62) < 0.001 1.79 (0.84–3.81) 0.13 Platelets < 30×10 9 /l b 113 (43.5) 59 (64.8) 2.40(1.46–3.94) < 0.001 1.85 (1.01–3.37) 0.046 Albumin < 30g/l b 31 (11.9) 24 (26.4) 2.65 (1.46–4.81) 0.001 2.44 (1.26–4.70) 0.008 Septic shock 98 (37.7) 59 (64.8) - < 0.001 - - 30-day morality 40 (15.4) 46 (50.6) - < 0.001 - - CRGNB: carbapenem-resistant gram-negative bacteria; CSGNB: carbapenem-susceptible gram-negative bacteria; OR: odds ratio; CI: confidence interval; SD: standard deviation; IQR: interquartile range; ICU: intensive care unit. b medical and treatment history 30 days prior to bloodstream infection. Bold values suggest statistical signifcance ( p < 0.05). Establishment of a nomogram for predicting 30-day mortality after GNB BSI All of the patients were randomly divided into a training cohort (n = 253) and a validation cohort (n = 109) at a ratio of 7:3. The detailed characteristics of all of the patients are shown in Table 1 . All of the variables were balanced between the training and validation cohorts ( p ≥ 0.05). The parameters associated with 30-day mortality in the training cohort are described in Table 3 . Univariate analysis demonstrated that nosocomial infection, CRGNB infection, length of hospital stay before BSI < 14 days, previous exposure to antimicrobial therapy (such as carbapenems, tetracycline, glycopeptides, and antifungals), neutropenia, albumin < 30 g/l and platelets < 30×10 9 /l before and after BSI, septic shock, intensive care unit stay after BSI, and exposure to invasive procedures after BSI (such as mechanical ventilation, urinary catheter and nasogastric tube) were associated with 30-day mortality ( p < 0.05). After multivariate analysis, neutropenia before BSI (OR: 4.46, 95% CI: 1.37–22.89, p = 0.043), an albumin concentration < 30 g/l before BSI (OR: 7.21, 95% CI: 1.53–34.06, p = 0.013), septic shock (OR: 76.26, 95% CI: 13.59-427.93, p < 0.001) and mechanical ventilation after BSI (OR: 21.38, 95% CI: 3.55-128.77, p = 0.001) remained significantly associated with 30-day mortality after GNB BSI in patients with haematologic malignancies (Table 3 ). The results from the multivariate analysis were used to establish a visual nomogram for predicting 30-day mortality after GNB BSI in patients with haematologic malignancies (Fig. 2 ). Table 3 Univariate and multivariate logistic regression analyses of risk factors for 30-day mortality in the training cohort. Univariate analysis Multivariate analysis Characteristics OR (95% CI) P OR (95% CI) P Age ≥ 60 years 1.76 (0.95–3.28) 0.074 2.76 (0.90–8.46) 0.076 Sex, male 0.59 (0.31–1.12) 0.107 Co-morbid conditions Chronic pulmonary disease 2.23 (0.87–5.75) 0.097 1.29 (0.25–6.65) 0.762 Chronic liver disease 0.46 (0.10–2.10) 0.315 Diabetes mellitus 0.75 (0.31–1.8) 0.515 Chronic renal disease 0.85 (0.3–2.39) 0.756 Solid tumor 0.39 (0.09–1.77) 0.224 Nosocomial infection 2.21 (1.05–4.66) 0.038 3.06 (0.90-10.32) 0.072 CRGNB infection 5.41 (2.85–10.30) < 0.001 1.37 (0.38–5.24) 0.646 Co-infection with any bacteria 1.70 (0.81–3.57) 0.159 Length of hospital stay < 14 days b 0.48 (0.25–0.94) 0.032 3.34 (0.79–14.14) 0.101 ICU stay b 1.04 (0.36–2.98) 0.947 Mechanical ventilation b 3.14 (0.19–50.90) 0.422 Intravascular catheter b 2.03 (0.75–5.51) 0.164 Urinary catheter b 1.77 (0.62-5.00) 0.283 Nasogastric tube b 3.17 (0.44–23.01) 0.254 Chemotherapy/Radiotherapy b 1.08 (0.50–2.34) 0.849 Corticoid therapy b 1.41 (0.70–2.86) 0.340 Use of immunosuppressors b 1.31 (0.65–2.65) 0.447 Hematopoietic stem cell transplantation b 1.48 (0.54–4.08) 0.450 Exposure to antibiotics b Carbapenems 1.85 (1.03–3.33) 0.040 2.55 (0.54–12.14) 0.240 Cephalomycins 1.17 (0.3–4.56) 0.820 Aminoglycosides 1.22 (0.57–2.61) 0.617 Tetracycline 2.84 (1.31–6.14) 0.008 0.20 (0.04–1.11) 0.066 Fluoroquinolone 2.10 (0.78–5.69) 0.144 Lactamases inhibitors 1.30 (0.72–2.35) 0.389 Colistin 0.93 (0.25–3.48) 0.910 Glycopeptides 2.24 (1.11–4.53) 0.025 0.76 (0.17–3.45) 0.725 Antifungals 1.89 (1.05–3.41) 0.034 0.46 (0.13–1.60) 0.220 Neutropenia b 3.92 (1.76–8.74) 0.001 4.46 (1.37–22.89) 0.043 Platelets < 30×10 9 /l b 2.65 (1.44–4.87) 0.002 0.77 (0.19–3.10) 0.709 Albumin < 30g/l b 3.85 (1.88–7.87) < 0.001 7.21 (1.53–34.06) 0.013 Neutropenia a 3.72 (1.4.0-9.85) 0.008 0.65 (0.05–9.20) 0.748 Platelets < 30×10 9 /l a 4.11 (1.85–9.13) 0.001 3.31 (0.9-12.13) 0.071 Albumin < 30g/l a 3.39 (1.86–6.21) < 0.001 1.00(0.25–3.91) 0.996 Septic shock a 81.08 (19.09-344.45) < 0.001 76.26 (13.59-427.93) < 0.001 ICU stay a 6.35 (3.18–12.65) < 0.001 0.95 (0.07–13.44) 0.968 Mechanical ventilation a 49.91 (14.32-173.94) < 0.001 21.38 (3.55-128.77) 0.001 Intravascular catheter a 4.30 (0.98–18.76) 0.052 5.60 (0.43–73.50) 0.189 Urinary catheter a 12.63 (6.4-24.94) < 0.001 2.64 (0.90–7.73) 0.078 Nasogastric tube a 17.05 (6.83–42.58) < 0.001 1.02 (0.05–19.05) 0.991 Chemotherapy/Radiotherapy a 1 .00(0.47–2.13) 0.993 Corticoid therapy a 0.67 (0.22–2.05) 0.480 Use of immunosuppressors a 0.87 (0.48–1.56) 0.636 Appropriate empiric antibiotic therapy a 0.51 (0.18–1.47) 0.214 CRGNB: carbapenem-resistant gram-negative bacteria; ICU: intensive care unit; OR: odds ratio; CI: confidence interval. a medical and treatment after bloodstream infection. b medical and treatment history 30 days prior to bloodstream infection. The predictive model obtained in the training cohort had excellent discrimination ability, with the the C-indices were 0.942 (95% CI: 0.917–0.967) in the training cohort (Fig. 3 A) and 0.931 (95% CI: 0.888–0.974) in the validation cohort (Fig. 3 B). The calibration plots of the nomogram in both cohorts showed that the 30-day mortality after GNB BSI predicted by the model was consistent with the actual mortality (Fig. 3 C and Fig. 3 D). The Hosmer‒Lemeshow goodness of fit test showed a p value of 0.883 for the training cohort and 0.978 for the validation cohort, thus indicating that the model was well calibrated. The decision curves of the predictive model in both cohorts suggested that the model for predicting 30-day mortality after GNB BSI was more beneficial than all patients with BSIs or none with BSIs (Fig. 3 E and Fig. 3 F). Discussion Due to the weak immune system and the accumulation of various factors, such as repeated chemotherapy and long hospital stays, patients with haematologic malignancies suffer from greater morbidity and mortality from BSI than patients with other cancers [ 5 , 23 ]. In recent years, the expansion of GNB and antibiotic-resistant strains associated with significant mortality has elicited considerable concern because there are few possible therapeutic alternatives. It is essential to elucidate the pathogen distribution and identify the key factors that could help in assessing the risk of infection by antibiotic-resistant bacteria and mortality in haematologic malignancy patients, which can then be used to provide appropriate and immediate interventions (such as antibiotic administration) to improve patient survival. In this large-scale, single-centre study, the microbiological and clinical characteristics and outcomes of haematologic malignancy patients diagnosed with GNB BSI were recorded and analysed. The three most common GNB species were E. coli , Klebsiella pneumoniae and Pseudomonas aeruginosa , which together accounted for 78.6% of the GNBs causing BSI. This finding is consistent with the results of recent studies from other cities in China and other countries, such as Italy and Lebanon [ 3 , 4 , 24 ]; however, the proportion of E. coli (28.8%) in our study was much lower than that in Italy (52.7%) and Lebanon (45.6%), corresponding to a greater proportion of Klebsiella pneumoniae and Pseudomonas aeruginosa , thus suggesting that the prevalence of these bacterial species differs among global regions. It is necessary for the implementation of regular local surveillance of pathogen epidemiologic status and antibiotic susceptibility to evaluate antimicrobial strategies and adapt them to mitigate the effects of emerging pathogens. Carbapenems have been used as last-line antibiotics to treat infections caused by GNB, which are typically resistant to many first- and second-line antibiotics, such as cephalosporins and monobactam. Unfortunately, GNB rapidly developed resistance to carbapenems due to the selective pressures exerted by using this antibiotic family, and has now spread throughout the world. CRGNB strains usually include lactose nonfermenters such as CREc, CRKP and other Enterobacteriaceae , as well as nonfermenters such as CRAB and CRPA [ 25 ]. The impact of resistance may depend on the type of gram-negative bacteria, with resistance in Klebsiella pneumoniae and Pseudomonas aeruginosa having a greater impact than in Acinetobacter baumannii [ 7 ]. In this study, 25.9% of GNB were carbapenem resistant, which was similar to the findings of studies from Italy and Israel [ 4 , 7 ]. Not surprisingly, CRKP was the most frequently isolated CRGNB in this study, as it has been one of the most common pathogens causing BSIs that increasingly cause carbapenem resistance worldwide [ 26 ]. CRKP has been previously reported as being the bacterial species most frequently responsible for infections (mainly BSIs) in haematologic malignancies patients [ 24 , 27 , 28 ]. The number of CRPA-infected strains and the proportion of CRPA-infected strains among all Pseudomonas aeruginosa strains were lower than those of CRKP-infected strains in this study (but not much lower). In addition, CREc and CRAB were also observed in this study and have been reported in BSIs of haematologic malignancies patients in other studies [ 7 , 24 ]. Timely recognition of the most at-risk patients with CRGNB infection is critical for early appropriate empirical regimen selection within the first 24–48 h of infection and for source control of nosocomial dissemination, due to the fact that the standard empiric antibiotic therapies recommended for haematologic malignancy patients do not correspond to first-line CR-bacteria-targeted treatments, as well as the fact that CR-bacteria identification takes at least 48 h [ 15 ]. In the analysis of CRGNB BSI, we found that patients with underlying chronic liver disease were at an independent risk for infection BSI caused by CRGNB when GNB BSI was performed. This is due to the fact that the liver is essential for clearing bacteria and related toxins (such as endotoxin) from the bloodstream [ 29 ]. Liver dysfunction caused by chronic liver disease, such as alcoholic liver disease, is associated with a higher incidence of infections and a higher mortality rate due to sepsis [ 30 ]. Consistent with other studies [ 7 , 31 , 32 ], we also found that recent carbapenem treatment was significantly related to the occurrence of CRGNB bacteraemia. China is one of the main regions of the world with a high prevalence of antibiotic-resistant bacteria, and it is unavoidable that carbapenems are frequently used for empirical and targeted treatment, especially for patients in intensive care units and malignancy wards. Carbapenem treatment could eliminate the carbapenem-susceptible strains which resulted in carbapenem-resistant strains become the dominant population for infection. Albumin is synthesized by the liver and reflects liver function and nutritional status. Patients with low albumin concentrations are more likely to have lower immunity to resist bacterial infection. A previous study demonstrated that a low albumin concentration was a risk factor for BSI and CRGNB BSI among patients with haematological malignancies [ 7 ]. Consistently, our study demonstrated that an albumin concentration < 30 g/l was closely associated with a CRGNB BSI. Furthermore, a platelet count < 30×10 9 /l was also identified as being a risk factor significantly related to CRGNB BSI in our study, and which is rarely included in risk factor analysis of BSI in patients with haematological malignancies. Platelets are considered to be the sentinels of the bloodstream for rapid identification of microbial invasion by providing a comprehensive armamentarium of pathogen detection systems [ 31 ]. It is common knowledge that patients with haematological malignancies are immunocompromised because they receive chemotherapy. It could be hypothesized that a low concentration of albumin and a low platelet count combined with an immunocompromised state associated with a haematological malignancy may increase the risk for GNB BSI. The 30-day mortality of GNB BSIs was 24.5% in our study, whereas other studies have reported mortality rates ranging from 16.3–32.1% [ 24 , 32 , 33 ]. Similar to other studies [ 4 , 7 ], the 30-day mortality rate was much greater in CRGNB patients than in CSGNB patients. Several studies have reported of the risk factors for mortality in haematological malignancy patients with BSIs and GNBs; however, to our knowledge, there is no published clinical predictive model that allows us to accurately identify haematological malignancy patients at high risk of mortality within 30 days due to GNB BSIs. Herein, we established a multivariate regression model for predicting the 30-day mortality of haematological malignancy patients with GNB BSIs. Risk factors retained in the final model that were significantly and independently associated with 30-day mortality included septic shock, mechanical ventilation after BSI, neutropenia before BSI and an albumin concentration < 30 g/l before BSI. The model had satisfactory discrimination, consistency and clinical benefit based on the assessments of the C-indices, calibration plots and decision curves. Septic shock is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection and is associated with unacceptably high mortality [ 34 ]. According to numerous studies on bacteremia in patients with haematological malignancies [ 3 , 7 , 35 ], septic shock is an independent risk factor for mortality. Early screening of patients who exhibit low oxygen saturation, hypotension, or disturbance of consciousness, as well as timely intervention, including fluid resuscitation, vasopressors and appropriate antimicrobial therapy, are critical for removing the source of infection. In addition to septic shock, we also found that mechanical ventilation after BSI was strongly related to mortality. Few studies have included mechanical ventilation for potential risk factor analysis in haematological malignancy patients with BSI, although it has been widely identified as being a risk factor for mortality after BSI in other studies. Patients with bacteraemia requiring mechanical ventilation tend to have high APACHE and SOFA scores, longer lengths of hospital stay, multiple vascular lines, and greater risks of colonization and multiple infections. This emphasizes how crucial it is to account for serious underlying risk factors when evaluating prognosis in these patients. Neutropenia is a common adverse effect of chemotherapy, haematopoietic stem cell transplantation, and disease course in patients with haematological malignancies. Previous studies have suggested that neutropenia is an independent risk factor for BSI and has a significant impact on survival rates [ 5 , 27 , 36 ]. Neutrophils are essential for the acute inflammatory response and bacterial elimination. Neutropenia reduces the inflammatory response to developing infections, which promotes bacterial growth and invasion and renders patients vulnerable to recurrent infections. As life-threatening infections are more likely to occur in haematological malignancy patients with neutropenia, effective management of neutropenia is indispensable to maximize patient outcomes. Studies have demonstrated that an ALB concentration < 30 g/l is significantly related to 30-day mortality in haematological malignancy patients with BSIs [ 3 , 36 ]. In our study, an ALB concentration < 30 g/l was not only a dependent factor for CRGNB infection but also for GNB mortality. The monitoring of the albumin level and intervention by infusing albumin are important for preventing infection and improving the prognosis of haematological malignancy patients with GNB BSIs. Our study was limited by the single-centre retrospective nature of the study. There are likely clinical aspects that were not quantified or measured among haematological malignancy patients, and residual confounding factors cannot be excluded. Regional differences in the diagnosis, laboratory testing, therapeutic regimens and epidemiology of antibiotic-resistant isolates may result in different findings. Our predictive model should be optimized through further external validation and prospective studies. In conclusion, our study investigated the occurrence of GNB in haematological malignancy patients with BSIs and evaluated the demographic and clinical factors related to CRGNB BSI and GNB BSI-related mortality. CRGNB BSIs are frequently observed and are associated with poor prognosis. The risk of CRGNB BSI can be estimated when GNB BSI is confirmed by evaluating factors such as chronic liver disease, previous exposure to carbapenem therapy in one month, a platelet count < 30×10 9 /l and an albumin concentration < 30 g/l before BSI. Furthermore, neutropenia before BSI, an albumin concentration < 30 g/l before BSI, septic shock and mechanical ventilation after BSI were found to be independent risk factors for 30-day mortality in haematological malignancies patients with GNB BSIs. A nomogram with satisfactory predictive ability based on these factors was developed. It allows for patients to be stratified according to their risk of poor prognosis, and early effective management can be used to improve patient survival. Declarations Ethics approval and consent to participate The study protocol was approved by the ethics committee board of Zhujiang hospital. Informed consent was not obtained due to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was funded by the Medical Science and Technology Foundation of Guangdong Province [grant number A2024215]. Authors' contributions LW and JH conceived and designed the study. JZ, J-LL, X-JX and Y-QL participated in the data collection. LW and JZ analyzed the data. LW wrote the manuscript. JH revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Menzo S L, la Martire G, Ceccarelli G, et al. New Insight on Epidemiology and Management of Bacterial Bloodstream Infection in Patients with Hematological Malignancies[J]. Mediterr J Hematol Infect Dis, 2015,7:e2015044. Chen S, Lin K, Li Q, et al. A practical update on the epidemiology and risk factors for the emergence and mortality of bloodstream infections from real-world data of 3014 hematological malignancy patients receiving chemotherapy[J]. J Cancer, 2021,12:5494-5505. Wang J, Wang M, Zhao A, et al. Microbiology and prognostic prediction model of bloodstream infection in patients with hematological malignancies[J]. Front Cell Infect Microbiol, 2023,13:1167638. Haddad S, Jabbour J F, Hindy J R, et al. Bacterial bloodstream infections and patterns of resistance in patients with haematological malignancies at a tertiary centre in Lebanon over 10 years[J]. J Glob Antimicrob Resist, 2021,27:228-235. Lalaoui R, Javelle E, Bakour S, et al. Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies[J]. Front Microbiol, 2020,11:1422. Lan P, Lu Y, Chen Z, et al. Emergence of High-Level Cefiderocol Resistance in Carbapenem-Resistant Klebsiella pneumoniae from Bloodstream Infections in Patients with Hematologic Malignancies in China[J]. Microbiol Spectr, 2022,10:e8422. Andria N, Henig O, Kotler O, et al. Mortality burden related to infection with carbapenem-resistant Gram-negative bacteria among haematological cancer patients: a retrospective cohort study[J]. J Antimicrob Chemother, 2015,70:3146-3153. Trecarichi E M, Tumbarello M. Antimicrobial-resistant Gram-negative bacteria in febrile neutropenic patients with cancer: current epidemiology and clinical impact[J]. Curr Opin Infect Dis, 2014,27:200-210. Righi E, Peri A M, Harris P N, et al. Global prevalence of carbapenem resistance in neutropenic patients and association with mortality and carbapenem use: systematic review and meta-analysis[J]. J Antimicrob Chemother, 2017,72:668-677. Liao W C, Chung W S, Lo Y C, et al. Changing epidemiology and prognosis of nosocomial bloodstream infection: A single-center retrospective study in Taiwan[J]. J Microbiol Immunol Infect, 2022,55:1293-1300. Wisplinghoff H, Cornely O A, Moser S, et al. Outcomes of nosocomial bloodstream infections in adult neutropenic patients: a prospective cohort and matched case-control study[J]. Infect Control Hosp Epidemiol, 2003,24:905-911. Garcia-Vidal C, Cardozo-Espinola C, Puerta-Alcalde P, et al. Risk factors for mortality in patients with acute leukemia and bloodstream infections in the era of multiresistance[J]. PLoS One, 2018,13:e199531. Tumbarello M, Spanu T, Caira M, et al. Factors associated with mortality in bacteremic patients with hematologic malignancies[J]. Diagn Microbiol Infect Dis, 2009,64:320-326. Mokart D, Saillard C, Sannini A, et al. Neutropenic cancer patients with severe sepsis: need for antibiotics in the first hour[J]. Intensive Care Med, 2014,40:1173-1174. Averbuch D, Orasch C, Cordonnier C, et al. European guidelines for empirical antibacterial therapy for febrile neutropenic patients in the era of growing resistance: summary of the 2011 4th European Conference on Infections in Leukemia[J]. Haematologica, 2013,98:1826-1835. Zuckermann J, Moreira L B, Stoll P, et al. Compliance with a critical pathway for the management of febrile neutropenia and impact on clinical outcomes[J]. Ann Hematol, 2008,87:139-145. Timsit J F, Ruppe E, Barbier F, et al. Bloodstream infections in critically ill patients: an expert statement[J]. Intensive Care Med, 2020,46:266-284. Viasus D, Puerta-Alcalde P, Cardozo C, et al. Predictors of multidrug-resistant Pseudomonas aeruginosa in neutropenic patients with bloodstream infection[J]. Clin Microbiol Infect, 2020,26:345-350. Amanati A, Sajedianfard S, Khajeh S, et al. Bloodstream infections in adult patients with malignancy, epidemiology, microbiology, and risk factors associated with mortality and multi-drug resistance[J]. BMC Infect Dis, 2021,21:636. Chen X C, Xu J, Wu D P. Clinical Characteristics and Outcomes of Breakthrough Candidemia in 71 Hematologic Malignancy Patients and/or Allogeneic Hematopoietic Stem Cell Transplant Recipients: A Single-center Retrospective Study From China, 2011-2018[J]. Clin Infect Dis, 2020,71:S394-S399. Magiorakos A P, Srinivasan A, Carey R B, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance[J]. Clin Microbiol Infect, 2012,18:268-281. Deutschman C S, Singer M. Definitions for Sepsis and Septic Shock--Reply[J]. JAMA, 2016,316:458-459. Schelenz S, Nwaka D, Hunter P R. Longitudinal surveillance of bacteraemia in haematology and oncology patients at a UK cancer centre and the impact of ciprofloxacin use on antimicrobial resistance[J]. J Antimicrob Chemother, 2013,68:1431-1438. Trecarichi E M, Giuliano G, Cattaneo C, et al. Bloodstream infections due to Gram-negative bacteria in patients with hematologic malignancies: updated epidemiology and risk factors for multidrug-resistant strains in an Italian perspective survey[J]. Int J Antimicrob Agents, 2023,61:106806. Zahedi B A, Samadi K H, Ebrahimzadeh L H, et al. Dissemination of carbapenemases producing Gram negative bacteria in the Middle East[J]. Iran J Microbiol, 2015,7:226-246. Liu C, Dong N, Chan E, et al. Molecular epidemiology of carbapenem-resistant Klebsiella pneumoniae in China, 2016-20[J]. Lancet Infect Dis, 2022,22:167-168. Wang L, Wang Y, Fan X, et al. Prevalence of Resistant Gram-Negative Bacilli in Bloodstream Infection in Febrile Neutropenia Patients Undergoing Hematopoietic Stem Cell Transplantation: A Single Center Retrospective Cohort Study[J]. Medicine (Baltimore), 2015,94:e1931. Satlin M J, Cohen N, Ma K C, et al. Bacteremia due to carbapenem-resistant Enterobacteriaceae in neutropenic patients with hematologic malignancies[J]. J Infect, 2016,73:336-345. Strnad P, Tacke F, Koch A, et al. Liver - guardian, modifier and target of sepsis[J]. Nat Rev Gastroenterol Hepatol, 2017,14:55-66. Lu H. Inflammatory liver diseases and susceptibility to sepsis[J]. Clin Sci (Lond), 2024,138:435-487. McDonald B, Dunbar M. Platelets and Intravascular Immunity: Guardians of the Vascular Space During Bloodstream Infections and Sepsis[J]. Front Immunol, 2019,10:2400. Al-Otaibi F E, Bukhari E E, Badr M, et al. Prevalence and risk factors of Gram-negative bacilli causing blood stream infection in patients with malignancy[J]. Saudi Med J, 2016,37:979-984. Ayaz C M, Hazirolan G, Sancak B, et al. Factors Associated with Gram-Negative Bacteremia and Mortality in Neutropenic Patients with Hematologic Malignancies in a High-Resistance Setting[J]. Infect Dis Clin Microbiol, 2022,4:87-98. Cecconi M, Evans L, Levy M, et al. Sepsis and septic shock[J]. Lancet, 2018,392:75-87. Wang S, Song Y, Shi N, et al. Characteristics, Outcomes, and Clinical Indicators of Bloodstream Infections in Neutropenic Patients with Hematological Malignancies: A 7-Year Retrospective Study[J]. Infect Drug Resist, 2023,16:4471-4487. Tang Y, Cheng Q, Yang Q, et al. Prognostic factors and scoring model of hematological malignancies patients with bloodstream infections[J]. Infection, 2018,46:513-521. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4416357","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304554308,"identity":"7500ad24-7cb7-4082-940d-a2d9da790dae","order_by":0,"name":"Jing Zheng","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zheng","suffix":""},{"id":304554310,"identity":"420d4fcd-9128-4c35-ac7b-30cbdc495831","order_by":1,"name":"Jinlian Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinlian","middleName":"","lastName":"Li","suffix":""},{"id":304554312,"identity":"353f5fce-4b1a-4be4-8d9c-002aacf356e5","order_by":2,"name":"Xuejun xu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"xu","suffix":""},{"id":304554315,"identity":"27c11fdf-bdad-461a-b058-c776ff01eab0","order_by":3,"name":"Yuqing Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Li","suffix":""},{"id":304554318,"identity":"09ea63b0-7f79-4786-a04d-c932c84cb6f1","order_by":4,"name":"Ya Guo","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Guo","suffix":""},{"id":304554320,"identity":"dda1709a-47da-440e-9ae6-02ca54d5cf48","order_by":5,"name":"Jing Hu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Hu","suffix":""},{"id":304554322,"identity":"88fd6edc-45b4-4014-a45f-f25bf23ecc33","order_by":6,"name":"Ling Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLCCBwZAgr354IMPBjZyxGlJAGnhOZZsOKMgzZhILSBCwkdNmOfD4USCqg2Onz38IqHgjl2DBA8bs40BcwID++GjG/BqOZOXZpFg8Cy5Qbr32OMcA7Y8Bp60tBv4tJgdyDEzSDA4nMwgcy7dOMeAp5hBgscMv5bzb6BaJHLMpC0MJBIbCGq5kWP8AKjFDqyFwcCAsBb7G2/MgIF8OAEcyD0GCcZshPwi2Z9j/OHDn8P24Kj88ee/HD/74WN4tQABmwSQSNx/AMYloBwEmD+AHEiEwlEwCkbBKBipAADb1Ex/8D0mUgAAAABJRU5ErkJggg==","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-14 04:10:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4416357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4416357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57509301,"identity":"693f0c36-7a37-4b4e-81ef-892f9293a314","added_by":"auto","created_at":"2024-05-31 16:15:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79189,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of gram-negative pathogens in hematological malignancies patients with bloodstream infections (number).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4416357/v1/bfb3ed3626758ef7591e9b8e.png"},{"id":57509300,"identity":"3deb4ef5-a315-40fb-b804-14fef821ee83","added_by":"auto","created_at":"2024-05-31 16:15:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41018,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting 30-day mortality of hematological malignancies patients after bloodstream infections. (The “Yes” or “No” of each variable corresponded to the score on the “Points” axis, then the individual scores were added together to obtain the total score, the total score on the “Total points” axis corresponded to the dot of \u0026nbsp;“Diagnostic possibility” axis, which was the predicted probability of 30-day mortality).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4416357/v1/6eb5529344873e968545a5f8.png"},{"id":57509303,"identity":"7fbc4f74-5b27-4c11-b22f-9d66aeea95b1","added_by":"auto","created_at":"2024-05-31 16:15:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":960517,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve, calibration plots and decision curves analysis for the training cohort (A, C, E) and the validation cohort (B, D, F), respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4416357/v1/dfddce5cca835648e3edbd51.jpg"},{"id":60325770,"identity":"b9565593-7ae3-4a69-95bc-e5fc7e7bf309","added_by":"auto","created_at":"2024-07-15 15:11:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2048412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4416357/v1/4ee528b3-29cc-4edc-adb7-dd104d274df7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbiology and predictors of mortality in haematological malignancy patients with gram-negative bacterial bloodstream infections","fulltext":[{"header":"Background","content":"\u003cp\u003eBacterial bloodstream infection (BSI) has become one of the most common complications in haematologic malignancy patients, with high morbidity and mortality despite advances in diagnosis, prophylaxis, and management [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, studies have shown that gram-negative bacteria (GNB) were the predominant causative pathogens of BSIs compared to gram-positive bacteria, accounting for 64.7\u0026ndash;70.4% of bacterial BSIs [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. GNB BSIs are associated with high rates of mortality and other poor outcomes, which further increase due to the dissemination of antibiotic-resistant strains, especially carbapenem-resistant gram-negative bacteria (CRGNB) [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], such as \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e. This poses a considerable challenge to the management of BSI in these severely immunocompromised patients.\u003c/p\u003e \u003cp\u003eIn addition to increasing mortality, GNB BSIs also greatly increase medical bills and prolong hospital lengths of stay [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Timely empirical antibiotic treatment is crucial to these vulnerable patients with GNB BSIs. However, inappropriate empirical antibiotic treatment has become frequent due to the increasing incidence of antibiotic resistance worldwide, thus resulting in decreased effectiveness of treatment and the evolution of antibiotic-resistant strains by selective pressure from antibiotics, which may be responsible for increased mortality and morbidity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, the ability to diagnose CRGNB BSIs early before the reporting of antibiotic resistance in positive cultures by microbiology laboratories and to properly treat them in a timely manner could substantially improve patient outcomes [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, it is essential to identify the risk factors for mortality in patients with GNB BSIs and subsequently conduct early intervention and management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although previous studies have explored the risk factors for incidence and mortality in haematologic malignancy patients with BSIs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], few studies have explored the risk factors for the prognosis of carbapenem-resistant organism infections in haematologic malignancy patients with GNB BSIs or for the prediction of mortality in haematologic malignancy patients with GNB BSIs.\u003c/p\u003e \u003cp\u003eIn this study, we retrospectively collected clinical characteristic data from patients with haematologic malignancies to analyse the distribution of GNB, identify the key factors associated with CRGNB when the identification of GNB in blood cultures had been completed, and establish a nomogram model for predicting 30-day mortality among haematologic malignancy patients with GNB BSIs. Based on this analysis, we can provide evidence for rationalizing the use of antibiotics and the management of haematologic malignancy patients with GNB BSIs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting and patients\u003c/h2\u003e \u003cp\u003eThis retrospective observational study was conducted at a 2500-bed tertiary teaching hospital in Guangzhou, South China. Haematologic malignancy patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) with gram-negative bacteraemia who were hospitalized in the haematology and bone marrow transplantation departments (total of 100 beds) from 2015 to 2023 were included. Patients were identified from the records of the microbiology laboratory. Only the first isolate from the blood for each patient was included when multiple positive blood cultures with the same pathogen during the same hospital stay were identified. Patients were included more than once if they developed more than one episode of bacteraemia caused by a different gram-negative pathogen. Data, including demographic characteristics, underlying disease, medical and treatment history, and 30-day follow-up outcomes after BSI, were extracted from medical records. The study was approved by the ethics committee board of the hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiological studies\u003c/h2\u003e \u003cp\u003eSpecies confirmation and antibiotic susceptibility testing were performed in the microbiology laboratory of the hospital by using the Vitek 2 automated system (bio-M\u0026eacute;rieux, France) with the broth microdilution and disk diffusion methods. Antimicrobial susceptibilities were interpreted following the Clinical and Laboratory Standards Institute (CLSI) guidelines. Carbapenem resistance was defined as resistance to one or more carbapenem agents, such as meropenem, ertapenem, or imipenem [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The CRGNB strains included \u003cem\u003eEscherichia coli\u003c/em\u003e (CREc), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (CRKP), \u003cem\u003eEnterobacter cloacae\u003c/em\u003e, \u003cem\u003eEnterobacter aerogenes\u003c/em\u003e, \u003cem\u003eEnterobacter species\u003c/em\u003e, \u003cem\u003eCitrobacter fruendii\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003eProteus vulgaris\u003c/em\u003e, \u003cem\u003eMorganella morganii\u003c/em\u003e, \u003cem\u003eSerratia marrescens, Acinetobacter baumannii (\u003c/em\u003eCRAB\u003cem\u003e) and Pseudomonas aeruginosa (\u003c/em\u003eCRPA\u003cem\u003e)\u003c/em\u003e, etc.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eGBN BSI was defined as the isolation of gram-negative bacteria from the blood culture with or without infection symptoms (fever or hypothermia). The onset of BSI was considered as the date of collection of the first positive blood culture sample. A hospital-acquired BSI was defined as an infection that occurred 48 h after the patient\u0026rsquo;s admission; otherwise, it was defined as a community-acquired BSI. Septic shock was defined as a vasopressor requirement for maintaining a mean arterial pressure\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg or a serum lactate level\u0026thinsp;\u0026ge;\u0026thinsp;2 mmol/l [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Empiric antibiotic therapy was defined as one or more antibiotics that the patient received within 48 hours of being diagnosed with BSI (time to draw a positive blood culture specimen). Empiric antibiotic therapy was deemed appropriate if at least one of the utilized empiric antibiotics was a sensitive in vitro antibiotic sensitivity test; otherwise, it was considered to be inappropriate empiric antibiotic therapy. Exposure to prior antimicrobial treatment was defined as any treatment received for at least 48 h in the 30 days before BSI. Absolute neutrophil counts\u0026thinsp;\u0026lt;\u0026thinsp;0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e cells/L were defined as neutropenia. Stem cell transplants included both allogeneic and autologous transplants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll of the statistical analyses were performed by using SPSS version 26.0 and R version 4.1.2. Continuous variables with a normal distribution are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and were analysed by using a t test. Continuous variables with a nonnormal distribution are expressed as the median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range (IQR) and were analysed by using the Mann‒Whitney U test. Categorical variables are reported as frequencies and were compared by using chi-square or Fisher\u0026rsquo;s exact tests. The patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3 to construct and validate the model of 30-day mortality. Multicollinearity was examined by checking the variance inflation factor on a multiple regression model with the same dependent and independent variables. Variables with two-tailed \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate logistic regression analysis were included in the multivariate logistic regression analysis. Backwards stepwise multivariate logistic regression was used to investigate the independent risk factors for infection and mortality. The 30-day mortality probabilities were estimated by using the nomogram based on the results of multivariate logistic regression analysis in the training cohort.\u003c/p\u003e \u003cp\u003eModel performance was assessed via discrimination, calibration and clinical usefulness. The C-index was used to evaluate the discrimination ability of the model. C-index values greater than 0.7 suggest a reasonable estimation. The Hosmer\u0026ndash;Lemeshow goodness of fit test in multiple logistic regression was performed to assess the model calibration, and calibration curves were plotted. Decision curve analysis (DCA) was conducted to evaluate the net clinical benefit by quantifying the net benefits at different threshold probabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 351 GNB isolates which obtained from the 307 haematologic malignancies patients with BSIs were included in this study. Of which, 180 (51.3%) episodes occurred in patients with acute myeloid leukaemia, 86 (24.5%) in patients with acute lymphocytic leukaemia, 62 (17.7%) in patients with non-Hodgkin lymphoma, and 23 (6.6%) in patients with other haematologic malignancies. The characteristics of the patients are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, the mean age was 47.6 years, and 219 (62.4%) of them were male. Septic shock was observed in 157 (44.7%) episodes, and 30-day crude mortality was observed in 86 (24.5%) episodes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the hematological malignancies patients with bloodstream infections in training and validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation cohort \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-morbid conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNosocomial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRGNB infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-infection with any bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay\u003csup\u003eb\u003c/sup\u003e, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0 (12.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5 (12.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0 (11.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematopoietic stem cell transplantation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravascular catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasogastric tube\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticoid therapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of immunosuppressors\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to antibiotics\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e()\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalomycins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycosides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetracycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluoroquinolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactamases inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColistin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycopeptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;30g/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;30g/l\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravascular catheter\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary catheter\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasogastric tube\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticoid therapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of immunosuppressors\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppropriate empiric antibiotic therapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day morality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSD: standard deviation; IQR: interquartile range; ICU: intensive care unit.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e medical and treatment after bloodstream infection.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e medical and treatment history 30 days prior to bloodstream infection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of bacterial species\u003c/h2\u003e \u003cp\u003eAmong the 351 gram-negative pathogens identified from blood culture, a total of 17 different bacterial species were identified. \u003cem\u003eEscherichia coli\u003c/em\u003e was the most common bacterium (28.8%, 101/351), followed by \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (26.2%, 92/351) and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (23.6%, 83/351); other bacteria were observed in fewer than 10% of the episodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 91 (25.9%) gram-negative isolates belonging to 12 different bacterial species were carbapenem resistant. CRKP was the most frequently isolated CRGNB (29.7%, 27/91), accounting for 29.3% (27/92) of the \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e; moreover, CRPA was the second most common CRGNB (25.3%, 23/91), accounting for 27.7% (23/83) of the \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, followed by CREc (20.9%, 19/91) and CRAB (13.2%, 12/91), accounting for 18.8% (19/101) of the \u003cem\u003eEscherichia coli\u003c/em\u003e and 54.5% (12/22) of the \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, respectively. Other CRGNB were observed in \u0026le;\u0026thinsp;2 episodes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with CRGNB BSI\u003c/h2\u003e \u003cp\u003eAmong the haematologic malignancies patients with CRGNB BSIs, over half of the patients had acute myeloid leukaemia (58.2%, 53/91); additionally, 24.2% (22/91) had acute lymphocytic leukaemia, and 15.4% (14/91) had non-Hodgkin lymphoma. The incidence of 30-day mortality (50.6% vs. 15.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and septic shock (64.8% vs. 37.7%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher in haematologic malignancies patients with CRGNB BSIs than in those with carbapenem-susceptible gram-negative bacteria (CSGNB) BSIs. The characteristics of the patients with CRGNB BSIs and CSGNB BSIs are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis demonstrated that chronic liver disease, nosocomial infection, longer hospitalization, exposure to invasive procedures such as intravascular and urinary catheters, corticoid therapy, previous exposure to antimicrobial therapy (such as lactamase inhibitors, glycopeptides, carbapenems and tetracycline), neutropenia, an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l and a platelet count\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l before BSI were associated with carbapenem resistance in GNB isolates from patients with GNB BSIs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic analysis demonstrated that patients with chronic liver disease (OR: 3.47, 95% CI 1.41\u0026ndash;8.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), a previous exposure to carbapenem therapy (OR: 2.62, 95% CI: 1.51\u0026ndash;4.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), a platelet count\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l (OR: 1.85, 95% CI: 1.01\u0026ndash;3.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) and an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l before BSI (OR: 2.44, 95% CI: 1.26\u0026ndash;4.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) were found to be independent risk factors for BSI caused by CRGNB when GNB BSI was performed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of risk factors for GNB BSI caused by CRGNB.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSGNB\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRGNB\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.53\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-morbid conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (0.58\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48 (1.12\u0026ndash;5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.47(1.41\u0026ndash;8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.38\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.53\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.25\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNosocomial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67 (1.4\u0026ndash;5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38 (0.66\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay\u003csup\u003eb\u003c/sup\u003e, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.0 (10.8, 22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (15.5, 27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.98\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (0.85\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.22\u0026ndash;6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.83 (0.91\u0026ndash;85.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.42 (0.87-102.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravascular catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 (1.06\u0026ndash;5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.23 (0.89\u0026ndash;5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42 (1.05\u0026ndash;5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28 (0.47\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasogastric tube\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.94 (0.86\u0026ndash;17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.45\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticoid therapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02 (1.15\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 (0.93\u0026ndash;3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of immunosuppressors\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.60\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematopoietic stem cell transplantation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 (0.71\u0026ndash;3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to antibiotics\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15 (1.91\u0026ndash;5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.62 (1.51\u0026ndash;4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57 (0.07\u0026ndash;4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalomycins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.03\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycosides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 (0.89\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetracycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56 (1.92\u0026ndash;6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85 (0.93\u0026ndash;3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluoroquinolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (0.58\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactamasesinhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.23\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33 (0.75\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColistin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.35\u0026ndash;3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycopeptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02 (1.15\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.43\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (0.98\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.54\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01 (1.61\u0026ndash;5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79 (0.84\u0026ndash;3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40(1.46\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85 (1.01\u0026ndash;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;30g/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65 (1.46\u0026ndash;4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44 (1.26\u0026ndash;4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day morality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCRGNB: carbapenem-resistant gram-negative bacteria; CSGNB: carbapenem-susceptible gram-negative bacteria; OR: odds ratio; CI: confidence interval; SD: standard deviation; IQR: interquartile range; ICU: intensive care unit.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e medical and treatment history 30 days prior to bloodstream infection.\u003c/p\u003e \u003cp\u003eBold values suggest statistical signifcance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of a nomogram for predicting 30-day mortality after GNB BSI\u003c/h2\u003e \u003cp\u003eAll of the patients were randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;253) and a validation cohort (n\u0026thinsp;=\u0026thinsp;109) at a ratio of 7:3. The detailed characteristics of all of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All of the variables were balanced between the training and validation cohorts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05). The parameters associated with 30-day mortality in the training cohort are described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUnivariate analysis demonstrated that nosocomial infection, CRGNB infection, length of hospital stay before BSI\u0026thinsp;\u0026lt;\u0026thinsp;14 days, previous exposure to antimicrobial therapy (such as carbapenems, tetracycline, glycopeptides, and antifungals), neutropenia, albumin\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l and platelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l before and after BSI, septic shock, intensive care unit stay after BSI, and exposure to invasive procedures after BSI (such as mechanical ventilation, urinary catheter and nasogastric tube) were associated with 30-day mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After multivariate analysis, neutropenia before BSI (OR: 4.46, 95% CI: 1.37\u0026ndash;22.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l before BSI (OR: 7.21, 95% CI: 1.53\u0026ndash;34.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), septic shock (OR: 76.26, 95% CI: 13.59-427.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mechanical ventilation after BSI (OR: 21.38, 95% CI: 3.55-128.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) remained significantly associated with 30-day mortality after GNB BSI in patients with haematologic malignancies (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results from the multivariate analysis were used to establish a visual nomogram for predicting 30-day mortality after GNB BSI in patients with haematologic malignancies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses of risk factors for 30-day mortality in the training cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76 (0.95\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76 (0.90\u0026ndash;8.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.31\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-morbid conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 (0.87\u0026ndash;5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29 (0.25\u0026ndash;6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.10\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.31\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.3\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.09\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNosocomial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 (1.05\u0026ndash;4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06 (0.90-10.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRGNB infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.41 (2.85\u0026ndash;10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.38\u0026ndash;5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-infection with any bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (0.81\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay\u0026thinsp;\u0026lt;\u0026thinsp;14 days\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.25\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.34 (0.79\u0026ndash;14.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.36\u0026ndash;2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14 (0.19\u0026ndash;50.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravascular catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03 (0.75\u0026ndash;5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary catheter\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 (0.62-5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasogastric tube\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.17 (0.44\u0026ndash;23.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.50\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticoid therapy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (0.70\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of immunosuppressors\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.65\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematopoietic stem cell transplantation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48 (0.54\u0026ndash;4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to antibiotics\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.03\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.55 (0.54\u0026ndash;12.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalomycins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.3\u0026ndash;4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycosides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.57\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetracycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84 (1.31\u0026ndash;6.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.04\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluoroquinolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10 (0.78\u0026ndash;5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactamases inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.72\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColistin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.25\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycopeptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24 (1.11\u0026ndash;4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.17\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 (1.05\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46 (0.13\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92 (1.76\u0026ndash;8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46 (1.37\u0026ndash;22.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65 (1.44\u0026ndash;4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.19\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;30g/l\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85 (1.88\u0026ndash;7.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21 (1.53\u0026ndash;34.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.72 (1.4.0-9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.05\u0026ndash;9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.11 (1.85\u0026ndash;9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 (0.9-12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;30g/l\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.39 (1.86\u0026ndash;6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.25\u0026ndash;3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.08 (19.09-344.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.26 (13.59-427.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.35 (3.18\u0026ndash;12.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.07\u0026ndash;13.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.91 (14.32-173.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.38 (3.55-128.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravascular catheter\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30 (0.98\u0026ndash;18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.60 (0.43\u0026ndash;73.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary catheter\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.63 (6.4-24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64 (0.90\u0026ndash;7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasogastric tube\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.05 (6.83\u0026ndash;42.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.05\u0026ndash;19.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy/Radiotherapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 .00(0.47\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticoid therapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.22\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of immunosuppressors\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.48\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppropriate empiric antibiotic therapy\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.18\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCRGNB: carbapenem-resistant gram-negative bacteria; ICU: intensive care unit; OR: odds ratio; CI: confidence interval.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e medical and treatment after bloodstream infection.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e medical and treatment history 30 days prior to bloodstream infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predictive model obtained in the training cohort had excellent discrimination ability, with the the C-indices were 0.942 (95% CI: 0.917\u0026ndash;0.967) in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and 0.931 (95% CI: 0.888\u0026ndash;0.974) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The calibration plots of the nomogram in both cohorts showed that the 30-day mortality after GNB BSI predicted by the model was consistent with the actual mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The Hosmer‒Lemeshow goodness of fit test showed a \u003cem\u003ep\u003c/em\u003e value of 0.883 for the training cohort and 0.978 for the validation cohort, thus indicating that the model was well calibrated. The decision curves of the predictive model in both cohorts suggested that the model for predicting 30-day mortality after GNB BSI was more beneficial than all patients with BSIs or none with BSIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the weak immune system and the accumulation of various factors, such as repeated chemotherapy and long hospital stays, patients with haematologic malignancies suffer from greater morbidity and mortality from BSI than patients with other cancers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In recent years, the expansion of GNB and antibiotic-resistant strains associated with significant mortality has elicited considerable concern because there are few possible therapeutic alternatives. It is essential to elucidate the pathogen distribution and identify the key factors that could help in assessing the risk of infection by antibiotic-resistant bacteria and mortality in haematologic malignancy patients, which can then be used to provide appropriate and immediate interventions (such as antibiotic administration) to improve patient survival.\u003c/p\u003e \u003cp\u003eIn this large-scale, single-centre study, the microbiological and clinical characteristics and outcomes of haematologic malignancy patients diagnosed with GNB BSI were recorded and analysed. The three most common GNB species were \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, which together accounted for 78.6% of the GNBs causing BSI. This finding is consistent with the results of recent studies from other cities in China and other countries, such as Italy and Lebanon [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; however, the proportion of \u003cem\u003eE. coli\u003c/em\u003e (28.8%) in our study was much lower than that in Italy (52.7%) and Lebanon (45.6%), corresponding to a greater proportion of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, thus suggesting that the prevalence of these bacterial species differs among global regions. It is necessary for the implementation of regular local surveillance of pathogen epidemiologic status and antibiotic susceptibility to evaluate antimicrobial strategies and adapt them to mitigate the effects of emerging pathogens.\u003c/p\u003e \u003cp\u003eCarbapenems have been used as last-line antibiotics to treat infections caused by GNB, which are typically resistant to many first- and second-line antibiotics, such as cephalosporins and monobactam. Unfortunately, GNB rapidly developed resistance to carbapenems due to the selective pressures exerted by using this antibiotic family, and has now spread throughout the world. CRGNB strains usually include lactose nonfermenters such as CREc, CRKP and other \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, as well as nonfermenters such as CRAB and CRPA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The impact of resistance may depend on the type of gram-negative bacteria, with resistance in \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e having a greater impact than in \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this study, 25.9% of GNB were carbapenem resistant, which was similar to the findings of studies from Italy and Israel [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Not surprisingly, CRKP was the most frequently isolated CRGNB in this study, as it has been one of the most common pathogens causing BSIs that increasingly cause carbapenem resistance worldwide [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. CRKP has been previously reported as being the bacterial species most frequently responsible for infections (mainly BSIs) in haematologic malignancies patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The number of CRPA-infected strains and the proportion of CRPA-infected strains among all \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e strains were lower than those of CRKP-infected strains in this study (but not much lower). In addition, CREc and CRAB were also observed in this study and have been reported in BSIs of haematologic malignancies patients in other studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTimely recognition of the most at-risk patients with CRGNB infection is critical for early appropriate empirical regimen selection within the first 24\u0026ndash;48 h of infection and for source control of nosocomial dissemination, due to the fact that the standard empiric antibiotic therapies recommended for haematologic malignancy patients do not correspond to first-line CR-bacteria-targeted treatments, as well as the fact that CR-bacteria identification takes at least 48 h [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the analysis of CRGNB BSI, we found that patients with underlying chronic liver disease were at an independent risk for infection BSI caused by CRGNB when GNB BSI was performed. This is due to the fact that the liver is essential for clearing bacteria and related toxins (such as endotoxin) from the bloodstream [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Liver dysfunction caused by chronic liver disease, such as alcoholic liver disease, is associated with a higher incidence of infections and a higher mortality rate due to sepsis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consistent with other studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we also found that recent carbapenem treatment was significantly related to the occurrence of CRGNB bacteraemia. China is one of the main regions of the world with a high prevalence of antibiotic-resistant bacteria, and it is unavoidable that carbapenems are frequently used for empirical and targeted treatment, especially for patients in intensive care units and malignancy wards. Carbapenem treatment could eliminate the carbapenem-susceptible strains which resulted in carbapenem-resistant strains become the dominant population for infection.\u003c/p\u003e \u003cp\u003eAlbumin is synthesized by the liver and reflects liver function and nutritional status. Patients with low albumin concentrations are more likely to have lower immunity to resist bacterial infection. A previous study demonstrated that a low albumin concentration was a risk factor for BSI and CRGNB BSI among patients with haematological malignancies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consistently, our study demonstrated that an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l was closely associated with a CRGNB BSI. Furthermore, a platelet count\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l was also identified as being a risk factor significantly related to CRGNB BSI in our study, and which is rarely included in risk factor analysis of BSI in patients with haematological malignancies. Platelets are considered to be the sentinels of the bloodstream for rapid identification of microbial invasion by providing a comprehensive armamentarium of pathogen detection systems [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is common knowledge that patients with haematological malignancies are immunocompromised because they receive chemotherapy. It could be hypothesized that a low concentration of albumin and a low platelet count combined with an immunocompromised state associated with a haematological malignancy may increase the risk for GNB BSI.\u003c/p\u003e \u003cp\u003eThe 30-day mortality of GNB BSIs was 24.5% in our study, whereas other studies have reported mortality rates ranging from 16.3\u0026ndash;32.1% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similar to other studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the 30-day mortality rate was much greater in CRGNB patients than in CSGNB patients. Several studies have reported of the risk factors for mortality in haematological malignancy patients with BSIs and GNBs; however, to our knowledge, there is no published clinical predictive model that allows us to accurately identify haematological malignancy patients at high risk of mortality within 30 days due to GNB BSIs. Herein, we established a multivariate regression model for predicting the 30-day mortality of haematological malignancy patients with GNB BSIs. Risk factors retained in the final model that were significantly and independently associated with 30-day mortality included septic shock, mechanical ventilation after BSI, neutropenia before BSI and an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l before BSI. The model had satisfactory discrimination, consistency and clinical benefit based on the assessments of the C-indices, calibration plots and decision curves.\u003c/p\u003e \u003cp\u003eSeptic shock is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection and is associated with unacceptably high mortality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. According to numerous studies on bacteremia in patients with haematological malignancies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], septic shock is an independent risk factor for mortality. Early screening of patients who exhibit low oxygen saturation, hypotension, or disturbance of consciousness, as well as timely intervention, including fluid resuscitation, vasopressors and appropriate antimicrobial therapy, are critical for removing the source of infection. In addition to septic shock, we also found that mechanical ventilation after BSI was strongly related to mortality. Few studies have included mechanical ventilation for potential risk factor analysis in haematological malignancy patients with BSI, although it has been widely identified as being a risk factor for mortality after BSI in other studies. Patients with bacteraemia requiring mechanical ventilation tend to have high APACHE and SOFA scores, longer lengths of hospital stay, multiple vascular lines, and greater risks of colonization and multiple infections. This emphasizes how crucial it is to account for serious underlying risk factors when evaluating prognosis in these patients.\u003c/p\u003e \u003cp\u003eNeutropenia is a common adverse effect of chemotherapy, haematopoietic stem cell transplantation, and disease course in patients with haematological malignancies. Previous studies have suggested that neutropenia is an independent risk factor for BSI and has a significant impact on survival rates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Neutrophils are essential for the acute inflammatory response and bacterial elimination. Neutropenia reduces the inflammatory response to developing infections, which promotes bacterial growth and invasion and renders patients vulnerable to recurrent infections. As life-threatening infections are more likely to occur in haematological malignancy patients with neutropenia, effective management of neutropenia is indispensable to maximize patient outcomes. Studies have demonstrated that an ALB concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l is significantly related to 30-day mortality in haematological malignancy patients with BSIs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our study, an ALB concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l was not only a dependent factor for CRGNB infection but also for GNB mortality. The monitoring of the albumin level and intervention by infusing albumin are important for preventing infection and improving the prognosis of haematological malignancy patients with GNB BSIs.\u003c/p\u003e \u003cp\u003eOur study was limited by the single-centre retrospective nature of the study. There are likely clinical aspects that were not quantified or measured among haematological malignancy patients, and residual confounding factors cannot be excluded. Regional differences in the diagnosis, laboratory testing, therapeutic regimens and epidemiology of antibiotic-resistant isolates may result in different findings. Our predictive model should be optimized through further external validation and prospective studies.\u003c/p\u003e \u003cp\u003eIn conclusion, our study investigated the occurrence of GNB in haematological malignancy patients with BSIs and evaluated the demographic and clinical factors related to CRGNB BSI and GNB BSI-related mortality. CRGNB BSIs are frequently observed and are associated with poor prognosis. The risk of CRGNB BSI can be estimated when GNB BSI is confirmed by evaluating factors such as chronic liver disease, previous exposure to carbapenem therapy in one month, a platelet count\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026times;10\u003csup\u003e9\u003c/sup\u003e/l and an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l before BSI. Furthermore, neutropenia before BSI, an albumin concentration\u0026thinsp;\u0026lt;\u0026thinsp;30 g/l before BSI, septic shock and mechanical ventilation after BSI were found to be independent risk factors for 30-day mortality in haematological malignancies patients with GNB BSIs. A nomogram with satisfactory predictive ability based on these factors was developed. It allows for patients to be stratified according to their risk of poor prognosis, and early effective management can be used to improve patient survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the\u0026nbsp;ethics committee board of Zhujiang hospital. Informed consent was not obtained due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Medical Science and Technology Foundation of Guangdong Province [grant number A2024215].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLW and JH conceived and designed the study. JZ, J-LL, X-JX and Y-QL participated in the data collection. LW and JZ analyzed the data. LW wrote the manuscript. JH revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMenzo S L, la Martire G, Ceccarelli G, et al. New Insight on Epidemiology and Management of Bacterial Bloodstream Infection in Patients with Hematological Malignancies[J]. Mediterr J Hematol Infect Dis, 2015,7:e2015044.\u003c/li\u003e\n\u003cli\u003eChen S, Lin K, Li Q, et al. A practical update on the epidemiology and risk factors for the emergence and mortality of bloodstream infections from real-world data of 3014 hematological malignancy patients receiving chemotherapy[J]. J Cancer, 2021,12:5494-5505.\u003c/li\u003e\n\u003cli\u003eWang J, Wang M, Zhao A, et al. Microbiology and prognostic prediction model of bloodstream infection in patients with hematological malignancies[J]. Front Cell Infect Microbiol, 2023,13:1167638.\u003c/li\u003e\n\u003cli\u003eHaddad S, Jabbour J F, Hindy J R, et al. Bacterial bloodstream infections and patterns of resistance in patients with haematological malignancies at a tertiary centre in Lebanon over 10 years[J]. J Glob Antimicrob Resist, 2021,27:228-235.\u003c/li\u003e\n\u003cli\u003eLalaoui R, Javelle E, Bakour S, et al. Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies[J]. Front Microbiol, 2020,11:1422.\u003c/li\u003e\n\u003cli\u003eLan P, Lu Y, Chen Z, et al. Emergence of High-Level Cefiderocol Resistance in Carbapenem-Resistant Klebsiella pneumoniae from Bloodstream Infections in Patients with Hematologic Malignancies in China[J]. Microbiol Spectr, 2022,10:e8422.\u003c/li\u003e\n\u003cli\u003eAndria N, Henig O, Kotler O, et al. Mortality burden related to infection with carbapenem-resistant Gram-negative bacteria among haematological cancer patients: a retrospective cohort study[J]. J Antimicrob Chemother, 2015,70:3146-3153.\u003c/li\u003e\n\u003cli\u003eTrecarichi E M, Tumbarello M. Antimicrobial-resistant Gram-negative bacteria in febrile neutropenic patients with cancer: current epidemiology and clinical impact[J]. Curr Opin Infect Dis, 2014,27:200-210.\u003c/li\u003e\n\u003cli\u003eRighi E, Peri A M, Harris P N, et al. Global prevalence of carbapenem resistance in neutropenic patients and association with mortality and carbapenem use: systematic review and meta-analysis[J]. J Antimicrob Chemother, 2017,72:668-677.\u003c/li\u003e\n\u003cli\u003eLiao W C, Chung W S, Lo Y C, et al. Changing epidemiology and prognosis of nosocomial bloodstream infection: A single-center retrospective study in Taiwan[J]. J Microbiol Immunol Infect, 2022,55:1293-1300.\u003c/li\u003e\n\u003cli\u003eWisplinghoff H, Cornely O A, Moser S, et al. Outcomes of nosocomial bloodstream infections in adult neutropenic patients: a prospective cohort and matched case-control study[J]. Infect Control Hosp Epidemiol, 2003,24:905-911.\u003c/li\u003e\n\u003cli\u003eGarcia-Vidal C, Cardozo-Espinola C, Puerta-Alcalde P, et al. Risk factors for mortality in patients with acute leukemia and bloodstream infections in the era of multiresistance[J]. PLoS One, 2018,13:e199531.\u003c/li\u003e\n\u003cli\u003eTumbarello M, Spanu T, Caira M, et al. Factors associated with mortality in bacteremic patients with hematologic malignancies[J]. Diagn Microbiol Infect Dis, 2009,64:320-326.\u003c/li\u003e\n\u003cli\u003eMokart D, Saillard C, Sannini A, et al. Neutropenic cancer patients with severe sepsis: need for antibiotics in the first hour[J]. Intensive Care Med, 2014,40:1173-1174.\u003c/li\u003e\n\u003cli\u003eAverbuch D, Orasch C, Cordonnier C, et al. European guidelines for empirical antibacterial therapy for febrile neutropenic patients in the era of growing resistance: summary of the 2011 4th European Conference on Infections in Leukemia[J]. Haematologica, 2013,98:1826-1835.\u003c/li\u003e\n\u003cli\u003eZuckermann J, Moreira L B, Stoll P, et al. Compliance with a critical pathway for the management of febrile neutropenia and impact on clinical outcomes[J]. Ann Hematol, 2008,87:139-145.\u003c/li\u003e\n\u003cli\u003eTimsit J F, Ruppe E, Barbier F, et al. Bloodstream infections in critically ill patients: an expert statement[J]. Intensive Care Med, 2020,46:266-284.\u003c/li\u003e\n\u003cli\u003eViasus D, Puerta-Alcalde P, Cardozo C, et al. Predictors of multidrug-resistant Pseudomonas aeruginosa in neutropenic patients with bloodstream infection[J]. Clin Microbiol Infect, 2020,26:345-350.\u003c/li\u003e\n\u003cli\u003eAmanati A, Sajedianfard S, Khajeh S, et al. Bloodstream infections in adult patients with malignancy, epidemiology, microbiology, and risk factors associated with mortality and multi-drug resistance[J]. BMC Infect Dis, 2021,21:636.\u003c/li\u003e\n\u003cli\u003eChen X C, Xu J, Wu D P. Clinical Characteristics and Outcomes of Breakthrough Candidemia in 71 Hematologic Malignancy Patients and/or Allogeneic Hematopoietic Stem Cell Transplant Recipients: A Single-center Retrospective Study From China, 2011-2018[J]. Clin Infect Dis, 2020,71:S394-S399.\u003c/li\u003e\n\u003cli\u003eMagiorakos A P, Srinivasan A, Carey R B, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance[J]. Clin Microbiol Infect, 2012,18:268-281.\u003c/li\u003e\n\u003cli\u003eDeutschman C S, Singer M. Definitions for Sepsis and Septic Shock--Reply[J]. JAMA, 2016,316:458-459.\u003c/li\u003e\n\u003cli\u003eSchelenz S, Nwaka D, Hunter P R. Longitudinal surveillance of bacteraemia in haematology and oncology patients at a UK cancer centre and the impact of ciprofloxacin use on antimicrobial resistance[J]. J Antimicrob Chemother, 2013,68:1431-1438.\u003c/li\u003e\n\u003cli\u003eTrecarichi E M, Giuliano G, Cattaneo C, et al. Bloodstream infections due to Gram-negative bacteria in patients with hematologic malignancies: updated epidemiology and risk factors for multidrug-resistant strains in an Italian perspective survey[J]. Int J Antimicrob Agents, 2023,61:106806.\u003c/li\u003e\n\u003cli\u003eZahedi B A, Samadi K H, Ebrahimzadeh L H, et al. Dissemination of carbapenemases producing Gram negative bacteria in the Middle East[J]. Iran J Microbiol, 2015,7:226-246.\u003c/li\u003e\n\u003cli\u003eLiu C, Dong N, Chan E, et al. Molecular epidemiology of carbapenem-resistant Klebsiella pneumoniae in China, 2016-20[J]. Lancet Infect Dis, 2022,22:167-168.\u003c/li\u003e\n\u003cli\u003eWang L, Wang Y, Fan X, et al. Prevalence of Resistant Gram-Negative Bacilli in Bloodstream Infection in Febrile Neutropenia Patients Undergoing Hematopoietic Stem Cell Transplantation: A Single Center Retrospective Cohort Study[J]. Medicine (Baltimore), 2015,94:e1931.\u003c/li\u003e\n\u003cli\u003eSatlin M J, Cohen N, Ma K C, et al. Bacteremia due to carbapenem-resistant Enterobacteriaceae in neutropenic patients with hematologic malignancies[J]. J Infect, 2016,73:336-345.\u003c/li\u003e\n\u003cli\u003eStrnad P, Tacke F, Koch A, et al. Liver - guardian, modifier and target of sepsis[J]. Nat Rev Gastroenterol Hepatol, 2017,14:55-66.\u003c/li\u003e\n\u003cli\u003eLu H. Inflammatory liver diseases and susceptibility to sepsis[J]. Clin Sci (Lond), 2024,138:435-487.\u003c/li\u003e\n\u003cli\u003eMcDonald B, Dunbar M. Platelets and Intravascular Immunity: Guardians of the Vascular Space During Bloodstream Infections and Sepsis[J]. Front Immunol, 2019,10:2400.\u003c/li\u003e\n\u003cli\u003eAl-Otaibi F E, Bukhari E E, Badr M, et al. Prevalence and risk factors of Gram-negative bacilli causing blood stream infection in patients with malignancy[J]. Saudi Med J, 2016,37:979-984.\u003c/li\u003e\n\u003cli\u003eAyaz C M, Hazirolan G, Sancak B, et al. Factors Associated with Gram-Negative Bacteremia and Mortality in Neutropenic Patients with Hematologic Malignancies in a High-Resistance Setting[J]. Infect Dis Clin Microbiol, 2022,4:87-98.\u003c/li\u003e\n\u003cli\u003eCecconi M, Evans L, Levy M, et al. Sepsis and septic shock[J]. Lancet, 2018,392:75-87.\u003c/li\u003e\n\u003cli\u003eWang S, Song Y, Shi N, et al. Characteristics, Outcomes, and Clinical Indicators of Bloodstream Infections in Neutropenic Patients with Hematological Malignancies: A 7-Year Retrospective Study[J]. Infect Drug Resist, 2023,16:4471-4487.\u003c/li\u003e\n\u003cli\u003eTang Y, Cheng Q, Yang Q, et al. Prognostic factors and scoring model of hematological malignancies patients with bloodstream infections[J]. Infection, 2018,46:513-521.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"haematological malignancy, bloodstream infection, gram-negative bacteria, carbapenem resistance, model, mortality, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-4416357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4416357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Bloodstream infection (BSI) in haematological malignancy patients\u003cstrong\u003e \u003c/strong\u003ecaused by gram-negative bacteria (GNB) poses a clinical challenge, which is exacerbated by the increased dissemination of carbapenem-resistant GNB (CRGNB). In this study, we investigated the prevalence and factors for GNB BSI and mortality in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective study included haematological malignancy patients who developed GNB BSI between 2015 and 2023 at a tertiary teaching hospital in southern China. Risk factors for CRGNB BSI and mortality of GNB BSI were identified by using multivariate logistic analyses. The patients were randomly divided into training and validation cohorts at a ratio of 7:3 to establish the model of 30-day mortality. C-indices, calibration plots, and decision curve analyses were generated to evaluate the model. A nomogram of the model was established.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among the 351 patients with GNB BSIs, acute myeloid leukaemia (51.3%) was the most common. \u003cem\u003eEscherichia coli\u003c/em\u003e (28.8%) and \u003cem\u003eKlebsiella pneumoniae \u003c/em\u003e(29.7%) were the most common pathogens of GNB BSI and CRGNB BSI, respectively. The risk factors for CRGNB BSI were chronic liver disease, previous exposure to carbapenems, a platelet count \u0026lt; 30×10\u003csup\u003e9\u003c/sup\u003e/l and an albumin concentration \u0026lt; 30 g/l before BSI. The model for 30-day mortality of GNB BSI included neutropenia and an albumin concentration \u0026lt; 30 g/l before BSI, as well as septic shock and mechanical ventilation after BSI. The C-indices were 0.942 and 0.931 in the training and validation cohorts, respectively. The calibration plots and decision curves indicated that the model had good performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The identified factors allow for the stratification of patients at greatest risk for CRGNB BSI and poor prognosis for GNB BSI, which could help in facilitating timely effective intervention.\u003c/p\u003e","manuscriptTitle":"Microbiology and predictors of mortality in haematological malignancy patients with gram-negative bacterial bloodstream infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 16:14:59","doi":"10.21203/rs.3.rs-4416357/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf64f3d8-0ddc-482a-b7a2-13a22b807330","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T06:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 16:14:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4416357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4416357","identity":"rs-4416357","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}