基于血栓弹力图的后循环脑梗死患者病情进展风险预测模型构建与验证  

Construction and Validation of a Risk Prediction Model for Disease Progression of Posterior Circulation Cerebral Infarction Patients Based on Thromboelastography

作　　者：张重生 谢娟 王前友 ZHANG Chongsheng;XIE Juan;WANG Qianyou(Department of Neurology,Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences,Shanghai 202150,China)

机构地区：[1]上海健康医学院附属崇明医院神经内科,上海202150

出　　处：《临床误诊误治》2024年第19期55-61,共7页Clinical Misdiagnosis & Mistherapy

基　　金：上海市崇明区“可持续发展科技创新行动计划”项目(CKY2023-12)。

摘　　要：目的基于血栓弹力图构建后循环脑梗死患者病情进展风险预测模型,旨在为临床实践提供最佳预测工具。方法选取2021年5月至2023年4月120例后循环脑梗死患者作为建模人群,另选取同期80例后循环脑梗死患者作为验证人群,统计患者病情进展情况、临床资料及血栓弹力图参数。采用LASSO初筛后循环脑梗死患者疾病进展的特征变量,Logistic回归分析疾病进展的影响因素;采用R软件构建列线图预测模型,并分别采用受试者工作特征曲线、临床决策曲线、校准曲线评价该模型区分度、有效性及准确度。结果LASSO回归分析显示,当惩罚系数λ=0.122时模型性能优良且影响因素最少,最终筛选出6个预测变量为心房颤动史、入院时美国国立卫生院卒中量表(NIHSS)评分及血栓弹力图参数反应时间、凝固时间、最大振幅、凝固角。Logistic回归分析显示,入院时NIHSS评分、心房颤动史及血栓弹力图参数反应时间、凝固时间、最大振幅、凝固角均为后循环脑梗死患者疾病进展的影响因素(P<0.01)。根据上述影响因素建立后循环脑梗死患者疾病进展风险列线图预测模型;在建模与验证人群中,该列线图模型曲线下面积分别为0.875(95%CI:0.813,0.937)、0.914(95%CI:0.851,0.976),提示具有良好的区分度;校准曲线显示,该列线图模型在建模与验证人群中预测值与实际观察结果高度相关,提示准确度良好;临床决策曲线显示,在建模与验证人群中,该列线图模型净获益值较好,提示预测效能良好。结论心房颤动史、入院时NIHSS评分及血栓弹力图参数反应时间、凝固时间、最大振幅、凝固角均为后循环脑梗死患者病情进展的影响因素,基于上述影响因素构建列线图模型,具有良好风险预测效能。Objective To construct a risk prediction model for the progression of posterior circulation cerebral infarction(PCCI)patients based on thromboelastogram,aiming to provide the optimal predictive tool for clinical practice.Methods A total of 120 patients with PCCI from May 2021 to April 2023 were selected as the modeling population,and another 80 patients with PCCI treated during the same period were selected as the validation population.The disease progression,clinical data and thromboelastic parameters of the patients were analyzed,and the characteristic variables of disease progression of patients with PCCI were preliminarily screened by LASSO.Logistic regression was used to analyze the influencing factors of disease progression.R software was used to construct the nomogram prediction model,and the differentiation,validity and accuracy of the model were evaluated by receiver operating characteristic(ROC)curve,clinical decision curve and calibration curve,respectively.Results LASSO regression analysis showed that when the penalty coefficientλ=0.122,the model had good performance with the least influencing factors.Six predictive variables were finally selected,including the history of atrial fibrillation,the score of the National Institutes of Health Stroke Scale(NIHSS)at admission,the reaction time of thromboelastic parameters,the coagulation time,the maximum amplitude,and the coagulation angle.Logistic regression analysis showed that NIHSS score,history of atrial fibrillation,reaction time of thromboelastic parameters,coagulation time,the maximum amplitude and coagulation angle were all influencing factors for disease progression in patients with PCCI(P<0.01).According to the above influencing factors,the risk prediction model of disease progression in patients with PCCI was established.In the modeling and verification population,the area under the ROC curve(AUC)of the nomogram model was 0.875(95%CI:0.813,0.937)and 0.914(95%CI:0.851,0.976),respectively,indicating that the model had good differentiation.The cali

关 键 词：后循环脑梗死 血栓弹力图 病情进展 列线图 预测模型 心房颤动 NIHSS评分 ROC曲线 

分 类 号：R743.33[医药卫生—神经病学与精神病学]