Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

作者全名："Zhang, Yan; Xu, Weiwei; Yang, Ping; Zhang, An"

作者地址："[Zhang, Yan; Yang, Ping; Zhang, An] Chongqing Med Univ, Affiliated Hosp 2, Dept Crit Care Med, Chongqing 400010, Peoples R China; [Xu, Weiwei] Chongqing Med Univ, Affiliated Hosp 2, Dept Endocrine & Metab Dis, Chongqing 400010, Peoples R China"

通信作者："Yang, P; Zhang, A (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Crit Care Med, Chongqing 400010, Peoples R China."

来源：BMC MEDICAL INFORMATICS AND DECISION MAKING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001123744600001

JCR分区：Q2

影响因子：3.3

年份：2023

卷号：23

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Mortality; Machine learning; Systematic review; Sepsis; Meta-analysis

摘要："Background and objectivesSepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points.MethodsPubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction.ResultsFifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools.ConclusionMachine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units."

基金机构：Emergency Research Project for Novel Coronavirus Pneumonia Prevention and Control of Chongqing Health Commission [2020NCPZX04]

基金资助正文："This study was supported by Emergency Research Project for Novel Coronavirus Pneumonia Prevention and Control of Chongqing Health Commission [No. 2020NCPZX04]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript"