中国卒中杂志2023，Vol.18Issue(12)：1397-1404,8.DOI:10.3969/j.issn.1673-5765.2023.12.009
缺血性卒中患者院内复发风险预测模型开发与验证研究
Development and Validation of a Predictive Model for In-Hospital Recurrence Risk in Ischemic Stroke Patients
摘要
Abstract
Objective Develop a predictive model for in-hospital recurrence risk of ischemic stroke patients based on machine learning algorithms and externally validate it to provide insights for related research. Methods The development cohort was the China Stroke Center Alliance(CSCA)research cohort,and ischemic stroke patients in this cohort were randomly divided into training and internal validation sets in an 8∶2 ratio.The validation cohort was the Third China National Stroke Registry(CNSR-Ⅲ)research cohort.A list of candidate predictive factors was determined based on guidelines,literature,and data,followed by selection using least absolute shrinkage and selection operator(LASSO)regression.A predictive model for the in-hospital recurrence risk of ischemic stroke patients was developed using logistic regression and machine learning algorithms[random forest model,eXtreme gradient boosting(XGBoost)model,light gradient boosting machine(LightGBM)model].Model evaluation primarily focused on discrimination(C-statistic)and calibration(Brier score). Results The CSCA research cohort included 1 587 779 cases of ischemic stroke patients,with 99 085 cases of in-hospital recurrence(6.2%).The CNSR-Ⅲ research cohort included 14 146 cases of ischemic stroke patients,with 623 cases of in-hospital recurrence(4.4%).LASSO feature selection revealed that age,gender,stroke history,hypertension,diabetes,lipid metabolism disorders,atrial fibrillation,heart failure,coronary artery heart disease,peripheral vascular disease,LDL-C,fasting blood glucose,serum creatinine and in-hospital anticoagulation therapy were important predictive factors for predicting in-hospital recurrence of ischemic stroke patients.In internal validation,the discrimination of each model was around 0.75,with XGBoost model slightly outperforming other models(AUC 0.765,95%CI0.759-0.770),and the Brier scores for all models were around 0.05.In external validation,the predictive performance of all models was relatively low(AUC＜0.60),with Brier scores for all models less than 0.08. Conclusions In the limited context of the number and dimensions of predictive factors,the efficacy of logistic models and machine learning algorithms in predicting the recurrence risk of stroke was relatively low.Future exploration should involve more investigation into predictive factors and algorithm models.关键词
缺血性卒中/院内复发/预测模型/机器学习Key words
Ischemic stroke/In-hospital recurrence/Predictive model/Machine learning引用本文复制引用
陈思玎,姜英玉,王春娟,杨昕,李子孝,姜勇,王拥军,谷鸿秋..缺血性卒中患者院内复发风险预测模型开发与验证研究[J].中国卒中杂志,2023,18(12):1397-1404,8.基金项目
国家自然科学基金项目(72004146)北京市医院管理中心"青苗"人才计划(QML20210501)北京市医院管理中心"培育"人才计划(PX2021024) （72004146）
