Sepsis mortality score for the prediction of mortality in septic patients

Shukeri, Wan Fadzlina Wan Muhd; Ralib, Azrina; Abdulah, Nor Zamzila; Mat-Nor, Mohd Basri

doi:10.1016/j.jcrc.2017.09.009

Cited by 34 publications

(28 citation statements)

References 20 publications

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“…On the basis of clinical score and laboratory data, the current study was done on samples collected at time 0, 2,6,12,24,36,48,72,168, and 240 hours after infusion, to concentrate the analysis in samples that had the most prominent clinical or laboratory changes and to include two additional samples (168 and 240 hours) to assess possible long term effects on PON-1 activity.…”

Section: Methodsmentioning

confidence: 99%

Serial measurements of Paraoxonase-1 (PON-1) activity in horses with experimentally induced endotoxemia

Scavone

Sgorbini

Borges

et al. 2020

Preprint

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Background: Paraoxonase-1 (PON-1) is an antioxidant enzyme, whose activity decreases during the acute phase response in many species. Little is known about PON-1 and its role as a negative acute phase protein during septic inflammation in horses, but promising findings about its utility in diagnosing SIRS and predicting the outcome in diseased horses, were recently highlighted. The objective of the study was to investigate the behaviour of PON-1 in horses after experimentally induced endotoxemia. To this aim, PON-1 activity was measured on 66 plasma samples collected from six clinically healthy mares, previously included in another study, before and at multiple time points between 12 and 240 hours after intravenous infusion of Escherichia coli O55:B5 lipopolysaccharide (LPS). Results: compared with baseline values, a progressive transient decrease of PON-1 activity was observed starting from 24 hours post-infusion, with lowest values observed between 3 to 7 days post-infusion, followed by a normalisation to pre-infusion levels the tenth day. Conclusions: the results of this study suggest that measurement and monitoring of PON-1 activity might be useful to evaluate progression and recovery from endotoxemia in horses. Further studies in horses with naturally occurring sepsis are warranted.

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Section: Methodsmentioning

confidence: 99%

Serial measurements of Paraoxonase-1 (PON-1) activity in horses with experimentally induced endotoxemia

Scavone

Sgorbini

Borges

et al. 2020

Preprint

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“…Among these datasets, we point out the Surviving Sepsis Campaign initiative 77 , 78 (albeit not fully publicly released), the Medical Information Mart for Intensive Care database (MIMIC-III) 79 and the electronic Intensive Care Unit (eICU) Collaborative Research Database 80 , which stand our for their completeness and integrity. Additionally, it is worth mentioning some notable studies aimed at identifying a restricted number of sepsis survival predicting features 81 , 82 : for instance, six predictors by Mao and colleagues 83 , 84 , five main predictive features by Shukeri et al 85 , and three blood biomarkers by Dolin and coauthors 86 .…”

Section: Introductionmentioning

confidence: 99%

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Chicco

Jurman

2020

Sci Rep

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Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.

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“…Hence, the first studies emerged that report machine learning-based mortality prediction models using data from patients with sepsis presenting to the ED [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Doorn

Borggreve

Schalkwijk

et al. 2020

Preprint

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IntroductionPatients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.MethodsA single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n=1,244) and validation dataset (n=100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.ResultsA number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82).ConclusionMachine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

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Sepsis mortality score for the prediction of mortality in septic patients

Cited by 34 publications

References 20 publications

Serial measurements of Paraoxonase-1 (PON-1) activity in horses with experimentally induced endotoxemia

Serial measurements of Paraoxonase-1 (PON-1) activity in horses with experimentally induced endotoxemia

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

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