[1]向杰 张伟 马亚哲 黄从新.心房颤动患者发生脑梗死的风险预测模型[J].心血管病学进展,2020,(12):1315-1323.[doi:10.16806/j.cnki.issn.1004-3934.2020.12.022]
　XIANG Jie,ZHANG wei,MA Yazhe,et al.Risk Prediction Model for Cerebral Infarction in Patients with Atrial Fibrillation[J].Advances in Cardiovascular Diseases,2020,(12):1315-1323.[doi:10.16806/j.cnki.issn.1004-3934.2020.12.022]
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心房颤动患者发生脑梗死的风险预测模型()
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《心血管病学进展》[ISSN:51-1187/R/CN:1004-3934]

卷:
期数:
2020年12期
页码:
1315-1323
栏目:
综述
出版日期:
2020-12-25

文章信息/Info

Title:
Risk Prediction Model for Cerebral Infarction in Patients with Atrial Fibrillation
作者:
向杰 张伟 马亚哲 黄从新
(武汉大学人民医院心血管研究所,湖北 武汉 430060)
Author(s):
XIANG Jie; ZHANG wei; MA Yazhe; HUANG Congxin
(Institute of Cardiovascular Research,Renmin Hospital of Wuhan University,Wuhan 430060,Hubei,China)
关键词:
心房颤动; 脑梗死; 风险预测模型; 列线图
Keywords:
Atrial fibrillationCerebral infarction; Risk prediction model; Nomogram
DOI:
10.16806/j.cnki.issn.1004-3934.2020.12.022
摘要:
目的 通过病例对照研究来开发和内部验证心房颤动患者发生脑梗死的风险模型图。方法 收集2019年6月2019年12月于武汉大学人民医院心内科住院治疗的268心房颤动患者（其中56有脑梗死）的临床资料，基于这些患者的52项临床数据开发一种预测模型。以LASSO回归模型用于优化脑梗死发生风险模型的特征选择。应用多变量logistic回归分析来建立预测模型，该模型结合了在LASSO回归模型中选择的特征并使用C指数，校正图和决策曲线分析评估了预测模型的区别，校准和临床实用性同时使用自举验证评估内部验证。结果 预测列线图中包含的预测变量包括血小板抑制剂、甘油三酯、直接胆红素、纤维蛋白、左房内径、抗凝剂、国际标准化比值、低密度脂蛋白、高血压、脑钠肽、高密度脂蛋白射血分数。该模型显示出良好的辨别能力和良好的校准，其C指数为0.778 765 2，在内部验证中仍可达到0.718 117 5的C指数值。决策曲线分析表明，在将脑梗死发生可能性阈值确定为1％的情况下进行干预时，脑梗死风险列线图对临床是有用的。结论 这种新颖的预测模型结合患者的基本临床数据，可以方便临床工作中对房颤患者发生脑梗死的风险预测，从而为患者制定更优的治疗方案。
Abstract:
ObjectiveTo develop and internally validate a risk model for cerebral infarction in patients with atrial fibrillation. Methods Data of 268 patients with atrial fibrillation admitted to department of cardiology in Renmin Hospital of Wuhan University from June 2019 to December 2019 were collected,of whom 56 cases had cerebral infarction. A prediction model based on 52 clinical data of these patients was developed. LASSO regression model was used to optimize the feature selection of risk model for cerebral infarction and multivariate logistic regression analysis to build prediction model,which combined the feature selected in LASSO regression model. Differences between prediction models,the calibration and clinical practicality were evaluated by C index,correction chart and decision curve analysis. Meanwhile,internal verification was assessed with bootstrap verification. Results The predictive variables included in prediction nomogram consist of platelet inhibitors,triglyceride,direct bilirubin,fibrin,left atrium inner diameter,anticoagulant,international standardized ratio,low density lipoprotein,hypertension,brain natriuretic peptide,high density lipoprotein and left ventricular ejection fraction. The model showed a good discriminating and calibrating power with C index of 0.778 765 2,which could still reach 0.718 117 5 in internal verification. Results of decision curve analysis showed when interventions were carried out at 1% threshold of morbidity for cerebral infarction,it was clinically useful to utilize risk histogram of cerebral infarction. Conclusion This novel prediction model integrating basic clinical data of patients facilitates risk predictions for cerebral infarction in patients with atrial fibrillation,which may help to formulate better therapeutic plans for patients.

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备注/Memo

备注/Memo:
通讯作者：黄从新，E-mail：huangcongxin@vip.com
收稿日期：2020-07-14
更新日期/Last Update: 2021-02-22