A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis
A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis
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DOI：
10.1016/j.medin.2018.07.016
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发表时间：
2020-04-01
影响因子：
3
通讯作者：
Duitama-Munoz, J. F.
中科院分区：
文献类型：
--
作者：
Garcia-Gallo, J. E.;Fonseca-Ruiz, N. J.;Duitama-Munoz, J. F.
简介：脓毒症与高死亡率相关，其严重程度必须迅速评估。使用的疾病严重程度评分预期适用于所有患者人群，并通常评价住院死亡率。然而，脓毒症患者在出院后仍有死亡的危险。目的：建立一个预测诊断为脓毒症的危重患者1年死亡率的模型。患者：来自重症监护医学信息集市的5650例脓毒症患者的数据（MIMIC-III）数据库进行了评估，随机分为如下：70%的培训和30%的验证。设计：一个回顾性的登记为基础的队列研究进行。根据入院后24 h的临床信息，建立基于随机梯度提升（SGB）方法的1年死亡率预测模型。使用最小绝对收缩和选择算子（LASSO）和SGB变量重要性方法解决变量选择问题。结果：验证子集中的AUROC为0.8039（95%可信区间（CI）：[0.8033 - 0.80451]）。该模型超过了传统的严重程度的疾病分数在同一subset.Conclusion获得的预测性能：使用组装算法，如SGB，用于生成一个定制的模型脓毒症产生更准确的1年死亡率预测比传统的评分系统，如SAPS II，SOFA或OASIS。(C)2018 Elsevier Espana，S.L.U. y SEMICYUC。All rights reserved.
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 24 h 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 0.80451) 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. (C) 2018 Elsevier Espana, S.L.U. y SEMICYUC. All rights reserved.