解放军医学杂志2024，Vol.49Issue(6)：629-635,7.DOI:10.11855/j.issn.0577-7402.1273.2023.0427
基于生命体征时序数据的创伤致死性大出血伤情动态预测模型开发及验证
Development and validation of dynamic prediction models using vital signs time series data for fatal massive hemorrhage in trauma
摘要
Abstract
Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.关键词
创伤/大出血/机器学习/辅助诊断Key words
wounds and injuries/massive hemorrhage/machine learning/assistant diagnosis分类
医药卫生引用本文复制引用
郭程娱,龚明慧,沈翘楚,韩辉,王若琳,张红亮,王俊康,李春平,黎檀实..基于生命体征时序数据的创伤致死性大出血伤情动态预测模型开发及验证[J].解放军医学杂志,2024,49(6):629-635,7.基金项目
This work was supported by the National Key Research and Development Program of China(2020YFC1512702) 国家重点研发计划(2020YFC1512702) （2020YFC1512702）
