J E García-Gallo1, N J Fonseca-Ruiz2, L A Celi3, J F Duitama-Muñoz4. 1. Engineering and Software Investigation Group, Universidad de Antioquia UdeA, Medellín, Colombia. Electronic address: jesteban.garcia@udea.edu.co. 2. Critical and Intensive Care, Medellín Clinic, Medellín, Colombia; Critical and Intensive Care Program, CES University, Medellín, Colombia. 3. Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, USA. 4. Engineering and Software Investigation Group, Universidad de Antioquia UdeA, Medellín, Colombia.
Abstract
INTRODUCTION: Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are intended to be applicable to all patient populations, and generally evaluate in-hospital mortality. However, patients with sepsis continue to be at risk of death after hospital discharge. OBJECTIVE: To develop a model for predicting 1-year mortality in critical patients diagnosed with sepsis. PATIENTS: The data corresponding to 5650 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC-III) database were evaluated, randomly divided as follows: 70% for training and 30% for validation. DESIGN: A retrospective register-based cohort study was carried out. The clinical information of the first 24h after admission was used to develop a 1-year mortality prediction model based on Stochastic Gradient Boosting (SGB) methodology. Variable selection was addressed using Least Absolute Shrinkage and Selection Operator (LASSO) and SGB variable importance methodologies. The predictive power was evaluated using the area under the ROC curve (AUROC). RESULTS: An AUROC of 0.8039 (95% confidence interval (CI): [0.8033 0.8045]) was obtained in the validation subset. The model exceeded the predictive performances obtained with traditional severity of disease scores in the same subset. CONCLUSION: The use of assembly algorithms, such as SGB, for the generation of a customized model for sepsis yields more accurate 1-year mortality prediction than the traditional scoring systems such as SAPS II, SOFA or OASIS.
INTRODUCTION:Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are intended to be applicable to all patient populations, and generally evaluate in-hospital mortality. However, patients with sepsis continue to be at risk of death after hospital discharge. OBJECTIVE: To develop a model for predicting 1-year mortality in critical patients diagnosed with sepsis. PATIENTS: The data corresponding to 5650 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC-III) database were evaluated, randomly divided as follows: 70% for training and 30% for validation. DESIGN: A retrospective register-based cohort study was carried out. The clinical information of the first 24h after admission was used to develop a 1-year mortality prediction model based on Stochastic Gradient Boosting (SGB) methodology. Variable selection was addressed using Least Absolute Shrinkage and Selection Operator (LASSO) and SGB variable importance methodologies. The predictive power was evaluated using the area under the ROC curve (AUROC). RESULTS: An AUROC of 0.8039 (95% confidence interval (CI): [0.8033 0.8045]) was obtained in the validation subset. The model exceeded the predictive performances obtained with traditional severity of disease scores in the same subset. CONCLUSION: The use of assembly algorithms, such as SGB, for the generation of a customized model for sepsis yields more accurate 1-year mortality prediction than the traditional scoring systems such as SAPS II, SOFA or OASIS.
Keywords:
Intensive care unit; Least Absolute Shrinkage and Selection Operator; Least absolute shrinkage and selection operator; Predicción de pronóstico; Prognosis prediction; Sepsis; Stochastic Gradient Boosting; Stochastic gradient boosting; Unidad de Cuidados Intensivos
Authors: Yue Yu; Chi Peng; Zhiyuan Zhang; Kejia Shen; Yufeng Zhang; Jian Xiao; Wang Xi; Pei Wang; Jin Rao; Zhichao Jin; Zhinong Wang Journal: Front Cardiovasc Med Date: 2022-05-03
Authors: Yibing Zhu; Jin Zhang; Guowei Wang; Renqi Yao; Chao Ren; Ge Chen; Xin Jin; Junyang Guo; Shi Liu; Hua Zheng; Yan Chen; Qianqian Guo; Lin Li; Bin Du; Xiuming Xi; Wei Li; Huibin Huang; Yang Li; Qian Yu Journal: Front Med (Lausanne) Date: 2021-07-01