Serum albumin is a strong predictor of sepsis outcome in elderly patients

Arnau-Barrés, Isabel; Güerri‐Fernández, Robert; Luque, Sònia; Sorlí, Luisa; Vázquez, Olga; Miralles, Ramón

doi:10.1007/s10096-019-03478-2

Cited by 90 publications

(71 citation statements)

References 18 publications

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“…Our research suggests that both lower maximum and minimum albumin indicate an increased hospital mortality. This result is also supported by several previous studies [ 28 – 30 ]. As the most common protein in the human body, serum albumin has important physiological functions such as maintaining plasma osmotic pressure, buffering function and binding capacity.…”

Section: Discussionsupporting

confidence: 92%

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

et al. 2021

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Background Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. Results Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. Conclusions The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.

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Section: Discussionsupporting

confidence: 92%

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

et al. 2021

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“…Serum albumin level is a common indicator for assessing a patient’s nutritional status, organ function, and comorbidity. The inflammatory state resulting from bacterial infection, which leads to the production of IL-1, TNF, and other cell mediators, can interfere with liver albumin synthesis, resulting in hypoalbuminemia [30]. There is currently a lack of literature on the relationship between mortality and serum albumin level in patients with E. coli infections.…”

Section: Discussionmentioning

confidence: 99%

Risk factors and outcomes in non-transplant patients with extended-spectrum beta-lactamase-producing Escherichia coli bacteremia: a retrospective study from 2013 to 2016

Xiao

Yang

Zhou

et al. 2019

Antimicrob Resist Infect Control

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Background Escherichia coli is one of the most common strains of extended-spectrum β-lactam (ESBL)-producing bacteria, and the prevention and treatment of ESBL-producing E. coli infections is an ongoing challenge. The clinical characteristics and outcomes of ESBL-producing E. coli bacteremia in non-transplant patients remain to be elucidated. Methods This retrospective study included 491 non-transplant patients with E. coli bloodstream infections (BSIs) from January 2013 to December 2016 and was conducted to investigate the risk factors, clinical features, and outcomes of these infections. Results Of the 491 E. coli BSI patients, 57.6% suffered from infections with ESBL-producing strains. A multivariate analysis showed that urinary tract infection, prior use of cephalosporin, and treatment with β-lactam-β-lactamase inhibitor (BLBLI) combination antibiotics were independent risk factors for the development of ESBL-producing E. coli BSIs. The overall mortality rate in E. coli BSI patients was 14.46%, and there was no significant difference in the 28 day mortality rate between ESBL-producing E. coli and non-ESBL-producing E. coli BSI patients (14.8% vs. 14.0%, respectively; P = 0.953). Similarly, there was no difference between the community-acquired infection group and the nosocomial infection group. Hepatobiliary disease, carbapenem exposure, high APACHE II score, and hypoproteinemia were independent risk factors for death in E. coli BSI patients. Multivariate analysis showed that hypoproteinemia and severe disease were independent risk factors for death from ESBL -producing E. coli BSIs. Furthermore, there was no significant difference in the 28 day mortality between patients with ESBL -producing E. coli BSIs treated with carbapenem monotherapy versus those treated with BLBLI combination antibiotics (12.8% vs. 17.9%, respectively; P = 0.384). Conclusions Prior use of cephalosporin or BLBLI combination antibiotics increased the risk ratio for ESBL -producing E. coli infection. Hypoproteinemia and severe disease are independent risk factors for death in patients with E. coli BSIs. There was no significant difference in the 28 day prognosis of patients with ESBL-producing E. coli and those with non-ESBL-producing E. coli BSIs. These data do not support the conclusion that carbapenems might be more effective than BLBLI antibiotics for treatment of patients with BSIs cau...

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“…This means that lower albumin levels correlate with severe systemic inflammation and organ failure [14]. Moreover, several studies demonstrated that low albumin levels correlated with adverse clinical outcomes [11, 15].…”

Section: Introductionmentioning

confidence: 99%

Increased neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with severe sepsis or septic shock

Gong

Cheng

et al. 2020

Epidemiol. Infect.

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There has been no study exploring the prognostic values of neutrophil percentage-to-albumin ratio (NPAR). We hypothesised that NPAR is a novel marker of inflammation and is associated with all-cause mortality in patients with severe sepsis or septic shock. Patient data were extracted from the MIMIC-III V1.4 database. Only the data for the first intensive care unit (ICU) admission of each patient were used and baseline data were extracted within 24 h after ICU admission. The clinical endpoints were 30-, 90- and 365-day all-cause mortality in critically ill patients with severe sepsis or septic shock. Cox proportional hazards models and subgroup analyses were used to determine the relationship between NPAR and these clinical endpoints. A total of 2166 patients were eligible for this analysis. In multivariate analysis, after adjustments for age, ethnicity and gender, higher NPAR was associated with increased risk of 30-, 90- and 365-day all-cause mortality in critically ill patients with severe sepsis or septic shock. Furthermore, after adjusting for more confounding factors, higher NPAR remained a significant predictor of all-cause mortality (tertile 3 vs. tertile 1: HR, 95% CI: 1.29, 1.04–1.61; 1.41, 1.16–1.72; 1.44, 1.21–1.71). A similar trend was observed in NPAR levels stratified by quartiles. Higher NPAR was associated with increased risk of all-cause mortality in critically ill patients with severe sepsis or septic shock.

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Serum albumin is a strong predictor of sepsis outcome in elderly patients

Abstract: Serum albumin is a strong predictor of sepsis outcome in elderly patients

Cited by 90 publications

References 18 publications

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

Risk factors and outcomes in non-transplant patients with extended-spectrum beta-lactamase-producing Escherichia coli bacteremia: a retrospective study from 2013 to 2016

Increased neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with severe sepsis or septic shock

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