An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission

Background and Objective: Patients who survive sepsis in the intensive care unit (ICU) (sepsis survivors) have an increased risk of long-term mortality and ICU readmission. We aim to identify the risk factors for in-hospital mortality in sepsis survivors with later ICU readmission and visualize the quantitative relationship between the individual risk factors and mortality by applying machine learning (ML) algorithm.; Methods: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III) database for sepsis and non-sepsis ICU survivors who were later readmitted to the ICU. The data on the first day of ICU readmission and the in-hospital mortality was combined for the ML algorithm modeling and the SHapley Additive exPlanations (SHAP) value of the correlation between the risk factors and the outcome.; Results: Among the 2970 enrolled patients, in-hospital mortality during ICU readmission was significantly higher in sepsis survivors (n = 2228) than nonsepsis survivors (n = 742) (50.4% versus 30.7%, P
Source: Current Awareness Service for Health (CASH) - Category: Consumer Health News Source Type: news