基于不同人群心源性休克风险预测模型的研究现状

Research status of risk prediction model for cardiogenic shock based on different patient population

心源性休克(CS)并发症多、病死率高,利用CS预后评分模型对患者进行准确的风险分层,早期识别高危患者,对优化分类诊治措施、改善患者结局有重要意义。近年来,新的风险预测模型和机器学习(ML)智能分析的应用较传统模型提高了对CS患者的风险分类和预后评估能力,尤其临床变量结合生物标志物的模型对CS风险预测有更高的准确性。本文对基于不同患者群体CS风险预测模型及ML模型的应用与研究现状进行综述。

Cardiogenic shock(CS)is characterized by complex complications and high mortality.Using the prognostic scoring model for CS to stratify the patients accurately and early identify the high-risk patients,which is of great significance to optimize the classified diagnosis and treatment measures and improve the outcome of patients.In recent years,new risk prediction models and machine learning(ML)intelligent analysis have improved the ability of risk classification and prognosis evaluation to CS patients compared with the traditional models,especially a model combining clinical variables with biomarkers has higher accuracy for predicting the CS risk.This paper reviewed the application and research status of CS risk prediction model and ML model based on different patient population.