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

Chicco, Davide; Jurman, Giuseppe

doi:10.1038/s41598-020-73558-3

Cited by 41 publications

(36 citation statements)

References 106 publications

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“…To recap, four studies applied machine learning to minimal electronic health records to diagnose sepsis or predict survival of patients [10,11,21,22], while six other studies applied them to complete electronic health records for the same goals [12-14, 16, 17, 19]. The study of Burdick and colleagues [15] even reported an observed decreased in the mortality at the hospital where the computational intelligence methods were applied to recognize sepsis.…”

Section: Introductionmentioning

confidence: 99%

Data analytics and clinical feature ranking of medical records of patients with sepsis

Chicco

Oneto

2021

BioData Mining

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Background Sepsis is a life-threatening clinical condition that happens when the patient’s body has an excessive reaction to an infection, and should be treated in one hour. Due to the urgency of sepsis, doctors and physicians often do not have enough time to perform laboratory tests and analyses to help them forecast the consequences of the sepsis episode. In this context, machine learning can provide a fast computational prediction of sepsis severity, patient survival, and sequential organ failure by just analyzing the electronic health records of the patients. Also, machine learning can be employed to understand which features in the medical records are more predictive of sepsis severity, of patient survival, and of sequential organ failure in a fast and non-invasive way. Dataset and methods In this study, we analyzed a dataset of electronic health records of 364 patients collected between 2014 and 2016. The medical record of each patient has 29 clinical features, and includes a binary value for survival, a binary value for septic shock, and a numerical value for the sequential organ failure assessment (SOFA) score. We disjointly utilized each of these three factors as an independent target, and employed several machine learning methods to predict it (binary classifiers for survival and septic shock, and regression analysis for the SOFA score). Afterwards, we used a data mining approach to identify the most important dataset features in relation to each of the three targets separately, and compared these results with the results achieved through a standard biostatistics approach. Results and conclusions Our results showed that machine learning can be employed efficiently to predict septic shock, SOFA score, and survival of patients diagnoses with sepsis, from their electronic health records data. And regarding clinical feature ranking, our results showed that Random Forests feature selection identified several unexpected symptoms and clinical components as relevant for septic shock, SOFA score, and survival. These discoveries can help doctors and physicians in understanding and predicting septic shock. We made the analyzed dataset and our developed software code publicly available online.

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Section: Introductionmentioning

confidence: 99%

Data analytics and clinical feature ranking of medical records of patients with sepsis

Chicco

Oneto

2021

BioData Mining

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“…age and comorbidities) factors which predict mortality in combination with the SOFA score in patients hospitalized for a restricted list of potentially severe infections ( pneumoniae , necrotizing dermohypodermitis, acute pyelonephritis, acute peritonitis, endocarditis, central venous catheter infection, meningitis, and acute osteoarthritis). Recently, other authors have shown the predictive value of age for sepsis-related mortality [ 69 – 71 ], but none demonstrated an equivalence between “modifiable” and “non-modifiable” factors using a scoring-point system of mortality prediction. Thus, it is interesting that in terms of mortality prediction and in this specific population, a lactatemia of ≥2 mmol/L is barely equivalent to an age of ≥40 years or a Charlson's comorbidity score of ≥2 ( Table 3 ).…”

Section: Discussionmentioning

confidence: 99%

Assessment of Metabolic Dysfunction in Sepsis in a Retrospective Single-Centre Cohort

Goutay

Perche

Toussaint

et al. 2021

Critical Care Research and Practice

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Objective. Our primary aim was to assess selected metabolic dysfunction parameters, both independently and as a complement to the SOFA score, as predictors of short-term mortality in patients with infection admitted to the intensive care unit (ICU). Methods. We retrospectively enrolled all consecutive adult patients admitted to the eight ICUs of Lille University Hospital, between January 2015 and September 2016, with suspected or confirmed infection. We selected seven routinely measured biological and clinical parameters of metabolic dysfunction (maximal arterial lactatemia, minimal and maximal temperature, minimal and maximal glycaemia, cholesterolemia, and triglyceridemia), in addition to age and the Charlson’s comorbidity score. All parameters and SOFA scores were recorded within 24 h of admission. Results. We included 956 patients with infection, among which 295 (30.9%) died within 90 days. Among the seven metabolic parameters investigated, only maximal lactatemia was associated with higher risk of 90-day hospital mortality in SOFA-adjusted analyses (SOFA-adjusted OR, 1.17; 95%CI, 1.10 to 1.25; p < 0.001 ). Age and the Charlson’s comorbidity score were also statistically associated with a poor prognosis in SOFA-adjusted analyses. We were thus able to develop a metabolic failure, age, and comorbidity assessment (MACA) score based on scales of lactatemia, age, and the Charlson’s score, intended for use in combination with the SOFA score. Conclusions. The maximal lactatemia level within 24 h of ICU admission is the best predictor of short-term mortality among seven measures of metabolic dysfunction. Our combined “SOFA + MACA” score could facilitate early detection of patients likely to develop severe infections. Its accuracy requires further evaluation.

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“…P <0.05 was considered significant. We used Matthews correlation coefficient (MCC) to evaluate the predictive model, with MCC worst value=– and best value=+1 [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Combining Blood-Based Biomarkers to Predict Mortality of Sepsis at Arrival at the Emergency Department

Xie

Lin

et al. 2020

Med Sci Monit

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Background Our aim was to determine a useful combination of blood biomarkers that can predict 28-day mortality of sepsis upon arrival at the Emergency Department (ED). Material/Methods Based on Sepsis-3.0, 90 sepsis patients were enrolled and divided into survivor and nonsurvivor groups with day 28 as the study end point. After comparing the demographic data and clinical characteristics of patients, we evaluated the predictive validity of a combination of markers including interleukin-6 (IL-6), procalcitonin (PCT), and lactate at arrival at the ED. Independent risk factors were found by using univariate and multivariate logistic regression analyses, and the prognostic value of markers was determined by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results There were 67 (74.4%) survivors and 23 (25.6%) nonsurvivors. The levels of IL-6 (survivors vs nonsurvivors: median 205.30 vs 3499.00 pg/mL, P =0.012) and lactate (survivors vs nonsurvivors: median 2.37 vs 5.77 mmol/L, P =0.003) were significantly lower in survivor group compared with the nonsurvivor group. Markers including IL-6, PCT, lactate, and neutrophil-to-white blood cell ratio (NWR) were independent risk factors in predicting 28-day mortality due to sepsis. The combination of these 4 markers provided the best predictive performance for 28-day mortality of patients with sepsis, on arrival at the ED (AUC of 0.823, 95% confidence interval [CI] 0.723–0.924), and its accuracy, specificity, and sensitivity were 74.4% (95% CI 64.0–82.8%), 91% (95% CI 80.9–96.3%), and 65% (95% CI 42.8–82.8%), respectively. Conclusions The combination of IL-6, PCT, lactate, and NWR measurements is a potential predictor of 28-day mortality for patients with sepsis, at arrival at the ED. Further research is needed to confirm our findings.

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

Cited by 41 publications

References 106 publications

Data analytics and clinical feature ranking of medical records of patients with sepsis

Data analytics and clinical feature ranking of medical records of patients with sepsis

Assessment of Metabolic Dysfunction in Sepsis in a Retrospective Single-Centre Cohort

Combining Blood-Based Biomarkers to Predict Mortality of Sepsis at Arrival at the Emergency Department

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