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Erschienen in: BMC Infectious Diseases 1/2022

Open Access 01.12.2022 | Research

Factors associated with in-hospital mortality in adult sepsis with Escherichia coli infection

verfasst von: Kun Song, Cuirong Guo, Zhao Zeng, Changluo Li, Ning Ding

Erschienen in: BMC Infectious Diseases | Ausgabe 1/2022

Abstract

Background

Escherichia coli (E. coli) is an important pathogen in sepsis. This study aimed to explore the factors which were associated with in-hospital mortality in adult sepsis with E. coli infection based on a public database.

Methods

All sepsis patients with E. coli infection in MIMIC-III were included in this study. Clinical characteristics between the survivor and non-survivor groups were analyzed. Factors associated with in-hospital mortality were identified by multivariate logistic regression.

Results

A total of 199 patients were eventually included and divided into two groups: a survivor group (n = 167) and a non-survivor group (n = 32). RDW and HCT were identified as the factors with clinical outcomes. The area under the ROC curve (AUC) were 0.633 and 0.579, respectively. When combined RDW and HCT for predicting in-hospital mortality, the AUC was 0.772, which was significantly superior to SOFA and APACHEII scores.

Conclusion

RDW and HCT were identified as factors associated with in-hospital mortality in adult sepsis patients with E. coli infection. Our findings will be of help in early and effective evaluation of clinical outcomes in those patients.
Hinweise
Kun Song, Cuirong Guo and Zhao Zeng contributed equally to this article

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
E. coli
Escherichia coli
SOFA
Sequential organ failure assessment
APACHE
Acute physiology and chronic health evaluation
LOS
Length of stay
ICU
Intensive care unit
CAD
Coronary artery disease
SBP
Systolic blood pressure
DBP
Diastolic blood pressure
HR
Heart rate
RR
Respiratory rate
WBC
White blood cells
PLT
Platelet
RDW
Red cell volume distribution width
PT
Prothrombin time
TT
Thrombin time
ALT
Alanine aminotransferase
AST
Aspartate aminotransferase
HCT
Hematocrit
MCV
Mean corpuscular volume
LOS
Length of stay
ICU
Intensive care unit
IQR
Interquartile ranges
CI
Confidential interval
OR
Odds ratio
AUC
Area under the curve
ROC
Receiver-operator characteristic

Introduction

Sepsis has been defined as a dysregulated host immune response to infections, leading to a life-threatening organ dysfunction [1]. Escherichia coli (E. coli) as one major kind of gram-negative bacilli may cause intra-abdominal infections, urinary tract infections, and sepsis [2]. An early-onset neonatal sepsis research with 235 cases showed that the most frequent pathogen was E. coli (86 [36.6%]) with higher incidence of mortality [3]. In China, a recent study clarified that E. coli infection accounted for nearly 30% in neonatal sepsis with more than a 10% death rate [4]. The immature immune systems of neonates may lead to a higher mortality in E. coli infection. Hence, early identification of those sepsis patients with poor prognosis was significant.
However, for E. coli infection, most previous studies focused on neonatal sepsis and few studies have been done for investigating the clinical characteristics of adult patients. Moreover, little has been known about the predictive values of different laboratory variables in adult sepsis with E. coli infection. Therefore, in our study, we aimed to explore the factors which were associated with in-hospital mortality in adult sepsis with E. coli infection based on a public database.

Methods

Patients

All sepsis patients with E. coli infection in MIMIC-III were included in this study. MIMIC-III database as an US-based critical care public database includes data linked with 53,423 adult patients (aged 16 years or above) from 2001 to 2012 and 7870 neonates from 2001 to 2008 admitted to a intensive care unit (ICU) [5]. Data including vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay and survival data were comprehensively recorded. The following tables in MIMIC III dataset were utilized in our study: ADMISSIONS, CHARTEVENTS, D_ICD DIAGNOSIS, D_ITEMS, D_LABIEVENTS, DIAGNOSIS_ICD, ICUSTAYS, LABEVENTS, NOTEEVENTS, PATIENTS, INPUTEVENTS_CV, INPUTEVENTS _MV and OUTPUTEVENTS [5].

Study population

All patients with a diagnosis relevant to sepsis with E. coli infection in the database were initially screened. The diagnosis of sepsis with E. coli infection in the database was confirmed by the lab findings when the pathogen culture in blood was positive in E. coli. Only the data of each patient in the first admission were utilized in this study. Exclusion criteria included as follows: patients with missing > 5% individual data and age less than 18.

Data extraction

Data extraction was performed by using structure query language (SQL). The data of demographic characteristics, clinical variables, laboratory variables and scoring systems were extracted for further analysis. The baseline characteristics used were those recorded within 24 h after admission. When one variable was recorded at a different time compared to the initial 24 h, the first one was enrolled in the study. Demographic characteristics included age, gender, marital status, ethnicity, ICU department, admission type, and comorbidities (renal disease, coronary artery disease (CAD), diabetes, hypertension). Clinical and laboratory variables included systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), white blood cells (WBC), neutrophils, lymphocytes, basophils, platelet (PLT), red cell volume distribution width (RDW), hematocrit (HCT), glucose, prothrombin time (PT), thrombin time (TT), albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), mean corpuscular volume (MCV), total bilirubin, creatinine, lactate, total calcium and anion gap. Clinical outcomes including length of stay (LOS) in ICU and in-hospital mortality and scoring systems including sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation (APACHEII) were also extracted.

Statistical analysis

Characteristics are expressed as mean ± standard deviation or median (IQR) for continuous variables and a percentage or frequency for categorical variables. Continuous variables were compared using Student’s t-test (normal distribution) or Mann–Whitney U-test (Skewed distribution), and categorical variables were compared using Fisher’s exact test or Chi-square analysis. Stepwise logistic regression for variables selection in multivariable logistic regression was performed. Variables with P < 0.2 which were compared between the survivor and non-survivor groups were further enrolled in multivariable logistic regression. Then, factors associated with in-hospital mortality was identified by multivariate logistic regression. Finally, the receiver-operator characteristic (ROC) analysis of different factors for predicting in-hospital mortality were performed. The cut-off values of variables were confirmed by the Youden Index (sensitivity + specificity-1). The value of each variable with the maximum Youden Index was the cut-off value.
SPSS software (version 26) was implemented for statistical analysis. Two-sided P values < 0.05 were considered statistically significant.

Results

General characteristics of the patients

At first, 5403 sepsis patients were included. Then, based on the infection of different pathogens, 210 sepsis patients with E. coli infection were enrolled in this research. According to the exclusion criteria, 11 patients were excluded and a total of 199 patients were included and divided into a survivor group (n = 167) and a non-survivor group (n = 32) (Fig. 1). General characteristics of the cohort were elucidated in Table 1. The median age was 69.52 and males accounted for 45.22% in total. Most of the patients were hospitalized in MICU (83.42%) and emergency admission was the most common admission type (96.98%). The top four comorbidities were as follows: hypertension (45.73%), CAD (18.09%), diabetes (4.52%) and renal disease (4.02%). The median scores of APACHEII and SOFA were 14 and 3, respectively.
Table 1
General characteristics of the patients
Variables
 
 Number of patients (n)
199
 Age (years)
69.52 (61.01–82.35)
Gender (n, %)
 
 Male
90 (45.22%)
 Female
109 (54.78%)
Marital status (n, %)
 
 Divorced
14 (7.03%)
 Married
83 (41.72%)
 Single
58 (29.14%)
 Widow
33 (16.58%)
 Others
11 (5.53%)
Ethnicity (n, %)
 
 Asian
14 (7.04%)
 White
142 (71.36%)
 Black/American
14 (7.03%)
 Hispanic/Latino
4 (2.01%)
 Others
25 (12.56%)
Department (n, %)
 
 CCU
12 (6.03%)
 MICU
166 (83.42%)
 SICU
12 (6.03%)
 TICU
5 (2.51%)
 CSRU
4 (2.01%)
Admission type (n, %)
 
 Elective
4 (2.01%)
 Urgent
2 (1.01%)
 Emergency
193 (96.98%)
Comorbidities (n, %)
 
 Renal disease
8 (4.02%)
 CAD
36 (18.09%)
 Diabetes
9 (4.52%)
 Hypertension
91 (45.73%)
Scoring system
 
 APACHEII
14 (12–17)
 SOFA
3 (1–4)
Clinical outcomes
 
 LOS in ICU (days)
3.70 (1.96–8.97)
 LOS in hospital (days)
8 (5–17)
 In-hospital mortality (n, %)
32 (16.08%)
SOFA sequential organ failure assessment, APACHE acute physiology and chronic health evaluation, LOS length of stay, ICU intensive care unit, CAD coronary artery disease
The median days of LOS in ICU and in hospital were 3.7 and 8, respectively. In-hospital mortality was 16.08%.

Comparison of variables between survivor and non-survivor groups

Different variables in survivor and non-survivor groups were compared and analyzed in Table 2. The median age of the survivor and non-survivor groups were 69.96 and 68.32 (P = 0.573). Gender showed no significant difference (P = 0.171). In vital signs, no significant differences showed in DBP (P = 0.414), SBP (P = 0.138) and RR (P = 0.068), while HR was significantly higher in the non-survivor group (P = 0.043). Comparison of comorbidities including renal disease (P = 0.778), CAD (P = 0.916), hypertension (P = 0.149) and diabetes (P = 0.806) demonstrated no significant differences between the two groups. In laboratory characteristics, PLT (P = 0.551), AST (P = 0.863), MCV (P = 0.278) glucose (P = 0.475), ALT (P = 0.789), TT (P = 0.733), hematocrit (P = 0.060), PT (P = 0.935), anion gap (P = 0.273), lymphocytes (P = 0.590), WBC (P = 0.479), lactate (P = 0.078), albumin (P = 0.369), creatinine (P = 0.728), total bilirubin (P = 0.176) and calcium (P = 0.854) didn’t have any significant differences between the two groups. Neutrophils (P = 0.015), RDW (P = 0.026) and basophils (P = 0.021) showed significant differences. There was no significant difference in the scores of APACHEII (P = 0.585) and SOFA (P = 0.357). In the non-survivor group, the days of LOS in ICU (P < 0.001) and hospital (P = 0.032) were longer.
Table 2
Comparison of variables between survivor and non-survivor groups
Variables
Survivor (n = 167)
Non-survivor (n = 32)
P-value
Age (years)
69.96 ± 14.80
68.32 ± 16.04
0.573
Gender (n, %)
   
 Male
72 (43.11%)
18 (56.25%)
0.171
 Female
95 (56.89%)
14 (43.75%)
Vital signs
   
 DBP (mmHg)
59.00 (48.50, 71.50)
63.00 (52.00, 69.00)
0.414
 SBP (mmHg)
110.83 ± 24.586
117.84 ± 23.489
0.138
 HR (beats/min)
97.46 ± 19.739
105.22 ± 19.511
0.043
 RR (beats/min)
20.00 (16.00, 25.00)
22.50 (20, 28.75)
0.068
Comorbidities (n,%)
   
 Renal disease
7 (4.19%)
1 (3.12%)
0.778
 CAD
30 (17.96%)
6 (18.75%)
0.916
 Diabetes
6 (3.59%)
3 (9.37%)
0.149
 Hypertension
77 (46.11%)
14 (43.75%)
0.806
Laboratory characteristics
  
 PLT (*109/L)
189.00 (132.50, 295.00)
198.00 (125.00, 310.00)
0.551
 AST (IU/L)
47.00 (25.50, 90.00)
48.00 (26.00, 92.00)
0.863
MCV (fL)
91.00 (88.00, 96.00)
91 (87.50, 95.00)
0.278
 Glucose (mg/dL)
121.00 (103.50, 163.50)
138.00 (116.00, 166.00)
0.475
 ALT (IU/L)
33.00 (19.50, 85.00)
38.00 (17.00, 54.00)
0.789
 Neutrophils (%)
78.50 (66.50, 82.50)
82.00 (75.00, 89.90)
0.015
 TT (s)
30.40 (26.35, 35.40)
32.70 (27.70, 39.50)
0.733
 HCT (%)
34.16 ± 5.41
36.43 ± 8.28
0.060
 PT (s)
14.40 (13.15, 17.30)
14.90 (12.80, 19.40)
0.935
 Anion Gap (mmol/L)
17.00 (13.00, 20.00)
17.00 (13.00, 21.00)
0.273
 RDW (%)
14.87 ± 1.85
15.70 ± 2.18
0.026
 Lymphocytes (%)
6.00 (2.95, 12.00)
8.00 (5.00, 15.00)
0.590
 WBC (*109/L)
12.20 (6.30, 19.75)
13.50 (3.70, 18.30)
0.479
 Lactate (mmol/L)
2.40 (1.45, 3.45)
2.90 (1.60, 4.50)
0.078
 Albumin (g/dL)
3.00 (2.50, 3.50)
2.90 (2.50, 3.40)
0.369
 Creatinine (mg/dL)
1.30 (1.00, 2.150
1.40 (0.90, 2.50)
0.728
 Total bilirubin (mg/dL)
0.80 (0.40, 1.60)
1.10 (0.50, 3.70)
0.176
 Total calcium (mg/dL)
8.02 ± 0.964
7.99 ± 1.383
0.854
 Basophils (%)
0.00 (0.00, 0.20)
0.00 (0.00, 0.10)
0.021
Scoring system (IQR)
   
 APACHEII
14.00 (12.00, 18.00)
15.50 (10.50, 17.00)
0.585
 SOFA
2.00 (1.00, 4.00)
3.00 (2.00, 4.75)
0.357
Clinical outcomes (days)
   
 LOS in ICU
3.22 (1.87, 7.82)
11.01 (4.66, 17.75)
 < 0.001
 LOS in hospital
8.00 (5.00, 15.00)
13.50 (6.25, 35.50)
0.032
SBP systolic blood pressure, DBP diastolic blood pressure, HR heart rate, RR respiratory rate, CAD coronary artery disease, WBC white blood cells, PLT platelet, RDW red cell volume distribution width, PT prothrombin time, TT thrombin time, ALT alanine aminotransferase, AST aspartate aminotransferase, HCT hematocrit, MCV mean corpuscular volume, SOFA sequential organ failure assessment, APACHE acute physiology and chronic health evaluation, LOS length of stay, ICU intensive care unit, IQR interquartile ranges

Factors associated with in-hospital mortality in multivariable analysis

Variables including gender (male), SBP, HR, RR, diabetes, neutrophils, HCT, RDW, lactate, total bilirubin and basophils were enrolled in multivariable analysis (Table 3). Two factors associated with in-hospital mortality were identified: HCT (P = 0.007, Odds Ratio (OR) = 1.116, 95%CI = 1.030–1.209) and RDW (P = 0.002, OR = 1.435, 95%CI = 1.140–1.806).
Table 3
Factors associated with in-hospital mortality in multivariable analysis
Variables
B
SE
Wald
P value
OR
95% CI for OR
Lower
Upper
Male
0.406
0.464
0.764
0.382
1.500
0.604
3.727
SBP
0.008
0.010
0.654
0.419
1.008
0.989
1.027
HR
0.004
0.013
0.087
0.768
1.004
0.978
1.031
RR
0.060
0.040
2.249
0.134
1.062
0.982
1.149
Diabetes
1.527
0.942
2.625
0.105
4.603
0.726
29.174
Neutrophils
 − 0.023
0.011
4.017
0.055
0.978
0.956
1.018
HCT
0.110
0.041
7.227
0.007
1.116
1.030
1.209
RDW
0.361
0.117
9.459
0.002
1.435
1.140
1.806
Lactate
0.024
0.124
0.036
0.849
1.024
0.804
1.305
Total Bilirubin
0.059
0.052
1.321
0.250
1.061
0.959
1.174
Basophils
 − 0.672
0.814
0.682
0.409
0.511
0.104
2.517
CI confidential interval, OR odds ratio, RDW red cell volume distribution width, SBP systolic blood pressure, HR heart rate, RR respiratory rate, HCT hematocrit

Predictive performances of factors and scoring systems

In Table 4 and Fig. 2, different predictive performances of HCT, RDW and scoring systems including SOFA and APAHEII were demonstrated. The cut-off values of RDW and HCT were 15.45% and 38.4%, respectively. The area under the ROC curve (AUC) of RDW and HCT were 0.633 and 0.579, respectively. When combined RDW and HCT for predicting in-hospital mortality, the AUC was 0.772, which was significantly superior to SOFA and APACHEII scores.
Table 4
Predictive performances of RDW, HCT and scoring systems
Variables
AUC
95%CI
Cut-off value
Sensitivity (95%CI)
Specificity (95%CI)
RDW (%)
0.633
0.518–0.748
15.45
0.625 (0.437–0.783)
0.686 (0.611–0.756)
HCT (%)
0.579
0.451–0.706
38.4
0.438 (0.268–0.621)
0.801 (0.732–0.858)
RDW + HCT
0.772
0.687–0.858
SOFA
0.550
0.441–0.658
3
0.613 (0.407–0.757)
0.487 (0.425–0.580)
APACHEII
0.539
0.427–0.651
14
0.613 (0.408–0.758)
0.573 (0.502–0.655)
AUC area under the ROC curve, CI confidential interval, SOFA sequential organ failure assessment, APACHE acute physiology and chronic health evaluation, RDW red cell volume distribution width, HCT hematocrit

Discussion

In our retrospective study, RDW and HCT were identified as factors associated with in-hospital mortality in adult sepsis patients with E. coli infection. To the best of our knowledge, this was the first study to explore the association of the factors with clinical prognosis in adult sepsis with E. coli infection based on MIMIC-III public database.
RDW as a parameter which could measure the range of variation of red blood cell size has been proved to be a common and inexpensive biomarker in critical illness [6]. Elevated RDW levels implicated higher variation in size, which has been usually applied for differentiation in anemia due to nutritional deficiency [7].
There is an accumulation of studies that have explored the association between RDW and clinical outcomes in sepsis. Recently, two modified and simple scores both including RDW have been proved to be useful tools for predicting short-term outcomes in sepsis or septic shock [8, 9]. One study focusing on neonatal sepsis elucidated that RDW to platelet ratio as a useful systemic inflammatory marker could be an indicator for sepsis occurrence in early stage [10]. In adult sepsis patients, the combination of three parameters including RDW, platelet distribution width and the neutrophil‑lymphocyte count ratio which were easily acquired from whole blood cell count analysis had a good diagnostic performance [11]. A nomogram including RDW provided a relatively accurate prediction for the early identification of septic patients at high risk of mortality in the emergency department [12]. One meta-analysis including 17,961 sepsis patients from 11 studies demonstrated that RDW was a significantly useful predictor of mortality in sepsis and patients with elevated RDW were more likely to have higher mortality [13].
Studies on RDW in different cohorts had different cut-off values. In a study with a total of 103 patients with community-acquired intra-abdominal sepsis, RDW ≥ 16 had an AUC of 0.867 for predicting in-hospital mortality [14]. Another study with 1046 patients concluded that for 30-day mortality and early clinical deterioration, an optimal cut-off value of RDW were 12.95 and 14.48, respectively [15]. One recent study on sepsis patients demonstrated that Youden Index was maximum (37%) at RDW value 14.75, which was good at predicting mortality within 28-days of emergency admission [16]. In our study, the best threshold value of RDW for predicting in-hospital mortality was 15.45.
The underlying mechanisms as to why increased RDW was associated with adverse prognosis in sepsis remained largely unknown, but several explanations have been illuminated in some studies. First, elevated inflammatory markers due to systemic inflammation response in sepsis may affect the erythrocytes maturation and lead to the migration of reticulocytes into the peripheral circulation, thereby resulting in RDW being elevated [17]. Second, reactive erythropoiesis was stimulated under oxidative stress which was one of the pathophysiologic entities of sepsis. Then, large immature red cells with poor oxygen-binding capacity were released, causing an increase in the RDW [18]. Third, sepsis can interrupt the iron steady state, trigger bone marrow suppression, and downregulate the expression of the erythropoietin receptor, which all contribute and cause more production of ineffective red blood cell and RDW increased [19].
In our research, HCT was another factor which was associated with in-hospital mortality in sepsis. One recent study based on machine learning for early detection of late-onset neonatal sepsis showed that HCT was one of top three predictive variables [20]. Another study in Brazil found that as a predictor of mortality risk in the sepsis, the level of HCT decreased with worse outcomes [21]. However, a positive relationship between HCT and mortality was found in our study, which was not consistent with some previous studies [22, 23]. The differences could be partly explained by two reasons. First, sepsis patients with poor outcomes were more likely to be suffering from hypovolemia due to increased capillary permeability [24], which resulted in higher levels of HCT. Second, the general characteristics of sepsis patients in different studies were not the same.
Limitations should also be clarified in our study. First, the study was on the basis of a publicly single-center database in US. While applying to other nations, concerns regarding the generalizability of the conclusions and the confounding bias caused by the missing data should be considered. Second, the new definition of Sepsis-3 was not included in this study because the patients in MIMIC-III were enrolled before 2012, which may lead to some limitations in applying our results. Third, RDW is always related to the underlying condition, especially chronic anemia, while anemia is one of the most common complications in patients with sepsis in the ICU [25]. Sepsis-related anemia can be caused by some factors including fluid loading-related hemodilution, iatrogenic blood loss, and inflammation-associated abnormalities in erythropoiesis [26, 27]. Due to lack of some data in MIMIC-III, the anemia which involved past medical history or caused by sepsis couldn’t be defined clearly. Further research should be done for exploring the differences between sepsis with anemia and without anemia in order to validate our results. Fourth, samples in our study were relatively small and subgroups were not divided for further analysis. Due to lack of some data in MIMIC-III, not all the variables which may affect the association between RDW and prognosis were enrolled. Hence, more samples with more variables and multiple centers should be explored for validating our results.

Conclusion

RDW and HCT were identified as factors associated with in-hospital mortality in adult sepsis with E. coli infection. Our findings will be of help in early and effective evaluation of clinical outcomes in those patients. Therefore, the measurements of RDW and HCT should be considered for prognostic assessment of adult sepsis with E. coli infection.

Acknowledgements

None.

Declarations

This study was conducted in accordance with Good Clinical Practice (Declaration of Helsinki 2002). MIMIC-III was an anonymized public database. To apply for access to the database, we passed the Protecting Human Research Participants exam (No. 32900964). The project was approved by the institutional review boards of the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC) and was given a waiver of informed consent.
Not applicable.

Competing interests

None.
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Literatur
1.
Zurück zum Zitat Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Intensive Care Med. 2017;43(3):304–77. CrossRef Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Intensive Care Med. 2017;43(3):304–77. CrossRef
2.
Zurück zum Zitat Ku NS, Kim HW, Oh HJ, Kim YC, Kim MH, Song JE, Oh DH, Ahn JY, Kim SB, Jeong SJ, et al. Red blood cell distribution width is an independent predictor of mortality in patients with gram-negative bacteremia. Shock. 2012;38(2):123–7. CrossRef Ku NS, Kim HW, Oh HJ, Kim YC, Kim MH, Song JE, Oh DH, Ahn JY, Kim SB, Jeong SJ, et al. Red blood cell distribution width is an independent predictor of mortality in patients with gram-negative bacteremia. Shock. 2012;38(2):123–7. CrossRef
3.
Zurück zum Zitat Stoll BJ, Puopolo KM, Hansen NI, Sanchez PJ, Bell EF, Carlo WA, Cotten CM, D’Angio CT, Kazzi SNJ, Poindexter BB, et al. Early-Onset Neonatal Sepsis 2015 to 2017, the Rise of Escherichia coli, and the Need for Novel Prevention Strategies. JAMA Pediatr. 2020;174(7):e200593. CrossRef Stoll BJ, Puopolo KM, Hansen NI, Sanchez PJ, Bell EF, Carlo WA, Cotten CM, D’Angio CT, Kazzi SNJ, Poindexter BB, et al. Early-Onset Neonatal Sepsis 2015 to 2017, the Rise of Escherichia coli, and the Need for Novel Prevention Strategies. JAMA Pediatr. 2020;174(7):e200593. CrossRef
4.
Zurück zum Zitat Zhu M, Jin Y, Duan Y, He M, Lin Z, Lin J. Multi-Drug Resistant Escherichia coli causing early-onset neonatal sepsis - a single center experience from China. Infect Drug Resist. 2019;12:3695–702. CrossRef Zhu M, Jin Y, Duan Y, He M, Lin Z, Lin J. Multi-Drug Resistant Escherichia coli causing early-onset neonatal sepsis - a single center experience from China. Infect Drug Resist. 2019;12:3695–702. CrossRef
5.
Zurück zum Zitat Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng ML, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific Data. 2016;3:89. CrossRef Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng ML, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific Data. 2016;3:89. CrossRef
6.
Zurück zum Zitat Chi T, Zhao Q, Wang P. Risk factors for acute coronary syndrome in upper gastrointestinal bleeding patients. Gastroenterol Res Pract. 2021;2021:8816805. CrossRef Chi T, Zhao Q, Wang P. Risk factors for acute coronary syndrome in upper gastrointestinal bleeding patients. Gastroenterol Res Pract. 2021;2021:8816805. CrossRef
7.
Zurück zum Zitat Hoffmann J, Urrechaga E. Role of RDW in mathematical formulas aiding the differential diagnosis of microcytic anemia. Scand J Clin Lab Invest. 2020;80(6):464–9. CrossRef Hoffmann J, Urrechaga E. Role of RDW in mathematical formulas aiding the differential diagnosis of microcytic anemia. Scand J Clin Lab Invest. 2020;80(6):464–9. CrossRef
8.
Zurück zum Zitat Kim YC, Song JE, Kim EJ, Choi H, Jeong WY, Jung IY, Jeong SJ, Ku NS, Choi JY, Song YG, et al. A simple scoring system using the red blood cell distribution width, delta neutrophil index, and platelet count to predict mortality in patients with severe sepsis and septic shock. J Intensive Care Med. 2019;34(2):133–9. CrossRef Kim YC, Song JE, Kim EJ, Choi H, Jeong WY, Jung IY, Jeong SJ, Ku NS, Choi JY, Song YG, et al. A simple scoring system using the red blood cell distribution width, delta neutrophil index, and platelet count to predict mortality in patients with severe sepsis and septic shock. J Intensive Care Med. 2019;34(2):133–9. CrossRef
9.
Zurück zum Zitat Kim J, Lee Y, Cho Y, Sohn Y, Hyun J, Ahn S, Lee W, Seong H, Kim J, Ahn J, et al. A modified simple scoring system using the red blood cell distribution width, delta neutrophil index, and mean platelet volume-to-platelet count to predict 28-day mortality in patients with sepsis. J Intensive Care Medicine. 2020;88506662:0933245. Kim J, Lee Y, Cho Y, Sohn Y, Hyun J, Ahn S, Lee W, Seong H, Kim J, Ahn J, et al. A modified simple scoring system using the red blood cell distribution width, delta neutrophil index, and mean platelet volume-to-platelet count to predict 28-day mortality in patients with sepsis. J Intensive Care Medicine. 2020;88506662:0933245.
10.
Zurück zum Zitat Karabulut B, Arcagok BC. New diagnostic possibilities for early onset neonatal sepsis: red cell distribution width to platelet ratio. Fetal Pediatr Pathol. 2020;39(4):297–306. CrossRef Karabulut B, Arcagok BC. New diagnostic possibilities for early onset neonatal sepsis: red cell distribution width to platelet ratio. Fetal Pediatr Pathol. 2020;39(4):297–306. CrossRef
11.
Zurück zum Zitat Zhang HB, Chen J, Lan QF, Ma XJ, Zhang SY. Diagnostic values of red cell distribution width, platelet distribution width and neutrophil-lymphocyte count ratio for sepsis. Exp Ther Med. 2016;12(4):2215–9. CrossRef Zhang HB, Chen J, Lan QF, Ma XJ, Zhang SY. Diagnostic values of red cell distribution width, platelet distribution width and neutrophil-lymphocyte count ratio for sepsis. Exp Ther Med. 2016;12(4):2215–9. CrossRef
12.
Zurück zum Zitat Zhao C, Wei Y, Chen D, Jin J, Chen H. Prognostic value of an inflammatory biomarker-based clinical algorithm in septic patients in the emergency department: An observational study. Int Immunopharmacol. 2020;80:106145. CrossRef Zhao C, Wei Y, Chen D, Jin J, Chen H. Prognostic value of an inflammatory biomarker-based clinical algorithm in septic patients in the emergency department: An observational study. Int Immunopharmacol. 2020;80:106145. CrossRef
13.
Zurück zum Zitat Zhang L, Yu C, Guo K, Huang C, Mo L. Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis. BMC Immunol. 2020;21(1):40. CrossRef Zhang L, Yu C, Guo K, Huang C, Mo L. Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis. BMC Immunol. 2020;21(1):40. CrossRef
14.
Zurück zum Zitat Ozdogan HK, Karateke F, Ozyazici S, Ozdogan M, Ozaltun P, Kuvvetli A, Gokler C, Ersoy Z. The predictive value of red cell distribution width levels on mortality in intensive care patients with community-acquired intra-abdominal sepsis. Ulus Travma Acil Cerrahi Derg. 2015;21(5):352–7. PubMed Ozdogan HK, Karateke F, Ozyazici S, Ozdogan M, Ozaltun P, Kuvvetli A, Gokler C, Ersoy Z. The predictive value of red cell distribution width levels on mortality in intensive care patients with community-acquired intra-abdominal sepsis. Ulus Travma Acil Cerrahi Derg. 2015;21(5):352–7. PubMed
15.
Zurück zum Zitat Uffen JW, Oomen P, de Regt M, Oosterheert JJ, Kaasjager K. The prognostic value of red blood cell distribution width in patients with suspected infection in the emergency department. BMC Emerg Med. 2019;19(1):76. CrossRef Uffen JW, Oomen P, de Regt M, Oosterheert JJ, Kaasjager K. The prognostic value of red blood cell distribution width in patients with suspected infection in the emergency department. BMC Emerg Med. 2019;19(1):76. CrossRef
16.
Zurück zum Zitat Ghimire R, Shakya YM, Shrestha TM, Neupane RP. The utility of red cell distribution width to predict mortality of septic patients in a tertiary hospital of Nepal. BMC Emerg Med. 2020;20(1):43. CrossRef Ghimire R, Shakya YM, Shrestha TM, Neupane RP. The utility of red cell distribution width to predict mortality of septic patients in a tertiary hospital of Nepal. BMC Emerg Med. 2020;20(1):43. CrossRef
17.
Zurück zum Zitat Scharte M, Fink MP. Red blood cell physiology in critical illness. Crit Care Med. 2003;31(12 Suppl):S651-657. CrossRef Scharte M, Fink MP. Red blood cell physiology in critical illness. Crit Care Med. 2003;31(12 Suppl):S651-657. CrossRef
18.
Zurück zum Zitat Kim S, Lee K, Kim I, Jung S, Kim MJ. Red cell distribution width and early mortality in elderly patients with severe sepsis and septic shock. Clin Exp Emerg Med. 2015;2(3):155–61. CrossRef Kim S, Lee K, Kim I, Jung S, Kim MJ. Red cell distribution width and early mortality in elderly patients with severe sepsis and septic shock. Clin Exp Emerg Med. 2015;2(3):155–61. CrossRef
19.
Zurück zum Zitat Yčas J, Horrow J, Horne B. Persistent increase in red cell size distribution width after acute diseases: A biomarker of hypoxemia? Clin Chim Acta. 2015;448:107–17. CrossRef Yčas J, Horrow J, Horne B. Persistent increase in red cell size distribution width after acute diseases: A biomarker of hypoxemia? Clin Chim Acta. 2015;448:107–17. CrossRef
20.
Zurück zum Zitat Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R, Chen Y, Romano-Keeler J, Nian H, Weitkamp JH. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc. 2014;21(2):326–36. CrossRef Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R, Chen Y, Romano-Keeler J, Nian H, Weitkamp JH. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc. 2014;21(2):326–36. CrossRef
21.
Zurück zum Zitat Juncal VR, Neto LAD, Camelier AA, Messeder OHC, Farias A. Clinical impact of sepsis at admission to the ICU of a private hospital in Salvador Brazil. J Bras Pneumol. 2011;37(1):85–92. CrossRef Juncal VR, Neto LAD, Camelier AA, Messeder OHC, Farias A. Clinical impact of sepsis at admission to the ICU of a private hospital in Salvador Brazil. J Bras Pneumol. 2011;37(1):85–92. CrossRef
22.
Zurück zum Zitat Flanagan L, Choi C, Lemdani M, Shah A, Parray A, Sukyte-Raube D, Fang C, Baredes S, Eloy J. Complication risk in ventral skull base surgery based on preoperative hematocrit. Laryngoscope. 2021;9:8. Flanagan L, Choi C, Lemdani M, Shah A, Parray A, Sukyte-Raube D, Fang C, Baredes S, Eloy J. Complication risk in ventral skull base surgery based on preoperative hematocrit. Laryngoscope. 2021;9:8.
23.
Zurück zum Zitat Zulastri M, Hafidz M, Ismail M, Zuhdi A. Hematocrit change as a predictor of readmission for decompensated heart failure: a retrospective single centre study. Rev Cardiovasc Med. 2021;22(2):505–12. CrossRef Zulastri M, Hafidz M, Ismail M, Zuhdi A. Hematocrit change as a predictor of readmission for decompensated heart failure: a retrospective single centre study. Rev Cardiovasc Med. 2021;22(2):505–12. CrossRef
24.
Zurück zum Zitat Turcato G, Zaboli A, Ciccariello L, Pfeifer N. Estimated plasma volume status (ePVS) could be an easy-to-use clinical tool to determine the risk of sepsis or death in patients with fever. J Crit Care. 2020;58:106–12. CrossRef Turcato G, Zaboli A, Ciccariello L, Pfeifer N. Estimated plasma volume status (ePVS) could be an easy-to-use clinical tool to determine the risk of sepsis or death in patients with fever. J Crit Care. 2020;58:106–12. CrossRef
25.
Zurück zum Zitat Jansma G, de Lange F, Kingma W, Vellinga N, Koopmans M, Kuiper M, Boerma E. “Sepsis-related anemia” is absent at hospital presentation; a retrospective cohort analysis. BMC Anesthesiol. 2015;15:55. CrossRef Jansma G, de Lange F, Kingma W, Vellinga N, Koopmans M, Kuiper M, Boerma E. “Sepsis-related anemia” is absent at hospital presentation; a retrospective cohort analysis. BMC Anesthesiol. 2015;15:55. CrossRef
26.
Zurück zum Zitat Jiang Y, Jiang FQ, Kong F, An MM, Jin BB, Cao D, Gong P. Inflammatory anemia-associated parameters are related to 28-day mortality in patients with sepsis admitted to the ICU: a preliminary observational study. Ann Intensive Care. 2019;9(1):67. CrossRef Jiang Y, Jiang FQ, Kong F, An MM, Jin BB, Cao D, Gong P. Inflammatory anemia-associated parameters are related to 28-day mortality in patients with sepsis admitted to the ICU: a preliminary observational study. Ann Intensive Care. 2019;9(1):67. CrossRef
27.
Zurück zum Zitat Elshinawy M, Kamal M, Nazir H, Khater D, Hassan R, Elkinany H, Wali Y. Sepsis-related anemia in a pediatric intensive care unit: transfusion-associated outcomes. Transfusion. 2020;60(Suppl 1):S4–9. PubMed Elshinawy M, Kamal M, Nazir H, Khater D, Hassan R, Elkinany H, Wali Y. Sepsis-related anemia in a pediatric intensive care unit: transfusion-associated outcomes. Transfusion. 2020;60(Suppl 1):S4–9. PubMed
Metadaten
Titel
Factors associated with in-hospital mortality in adult sepsis with Escherichia coli infection
verfasst von
Kun Song
Cuirong Guo
Zhao Zeng
Changluo Li
Ning Ding
Publikationsdatum
01.12.2022
Verlag
BioMed Central
Erschienen in
BMC Infectious Diseases / Ausgabe 1/2022
Elektronische ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07201-z

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