中国全科医学 ›› 2023, Vol. 26 ›› Issue (08): 917-926.DOI: 10.12114/j.issn.1007-9572.2022.0592

所属专题: 心血管最新文章合集

• 论著 • 上一篇    下一篇

中国肥厚型心肌病患者血栓栓塞事件风险预测模型的构建研究

阮海燕1,2, 李丽英1, 张木馨1,3, 郑翼1, 何森1,*()   

  1. 1.610041 四川省成都市,四川大学华西医院心内科
    2.610200 四川省成都市双流区中医医院心内科
    3.610100 四川省成都市龙泉驿区第一人民医院心内科
  • 收稿日期:2022-04-12 修回日期:2022-10-10 出版日期:2023-03-15 发布日期:2022-11-24
  • 通讯作者: 何森

  • 作者贡献: 阮海燕负责数据清洗、统计学分析、绘制图表及论文起草等;李丽英负责数据收集、采集、清洗、检索文献等,参与研究过程的实施;张木馨负责数据收集、采集、清洗等,对研究指标及论文数据进行核查;郑翼提出研究思路,设计研究方案,提出研究命题等;何森提出研究思路,设计研究方案,提出研究命题,设计主要研究指标等,参与并负责研究所有过程,负责论文最终版本修订,对论文负责。
  • 基金资助:
    四川省科技计划资助项目(2022YFS0186); 国家自然科学基金资助项目(81600299)

Establishment of a Risk Prediction Model for Thrombotic Events in Chinese Patients with Hypertrophic Cardiomyopathy

RUAN Haiyan1,2, LI Liying1, ZHANG Muxin1,3, ZHENG Yi1, HE Sen1,*()   

  1. 1. Department of Cardiology, West China Hospital of Sichuan University, Chengdu 610041, China
    2. Department of Cardiology, Shuangliu District Hospital of Chinese Medicine, Chengdu 610200, China
    3. Department of Cardiology, the First People's Hospital of Longquanyi District, Chengdu 610100, China
  • Received:2022-04-12 Revised:2022-10-10 Published:2023-03-15 Online:2022-11-24
  • Contact: HE Sen

摘要: 背景 血栓栓塞(TE)事件是肥厚型心肌病(HCM)的重要并发症。目前针对HCM患者TE事件的风险预测,仅国外学者构建了两个模型:HCM Risk-CVA及French HCM score,然而,现有研究发现HCM Risk-CVA模型对于中国HCM患者的临床价值较为有限。 目的 本研究拟构建适合中国HCM患者的TE事件风险预测模型。 方法 本研究系回顾性队列研究,收集2010—2018年在四川大学华西医院就诊的537例HCM患者的病例资料。本研究通过电话随访或电子病历系统查询患者就诊记录,每6~12个月随访1次,直至出现终点事件或死亡或研究拟定的评估日期(2019-12-31),终点事件定义为复合性TE事件。采用单因素和多因素Cox回归分析构建风险预测模型,并使用自助重抽样的方法进行内部验证。 结果 537例患者中,24例患者有不同程度的数据缺失,最终纳入513例患者。中位随访时间为4.2(1.3,6.2)年,随访过程中42例(8.18%)发生TE事件,年发病率为2.10%〔95%CI(1.47%,2.73%)〕。根据多因素Cox回归模型构建TE事件风险预测模型,最终纳入年龄、既往TE事件、心房颤动及左心室射血分数(LVEF)(P<0.05)。根据回归系数进行权重后,分别赋值构建SAAE score评分模型,即:S=既往脑卒中(stroke)等TE事件,A=心房颤动(atrial fibrillation),A=年龄(age),E=左心室射血分数(left ventricular ejection fraction)。内部验证提示SAAE score在整体人群中区分TE事件的Harrell's C-指数为0.773〔95%CI(0.688,0.858)〕,校准斜率为1.006;同时,SAAE score在整体人群中对1、3、5年区分TE事件的Harrell's C-指数分别为0.790、0.799及0.735,校准能力较好。此外,SAAE score在合并/不合并心房颤动的人群中区分TE事件的Harrell's C-指数分别为0.669〔95%CI(0.548,0.791)〕及0.647〔95%CI(0.498,0.795)〕,校准能力也较好;同时,在此两类人群中,该模型对1、3、5年TE事件也具有一定价值的区分及校准能力。对于整体人群、合并/不合并心房颤动人群,根据SAAE score进行高危、中危及低危分层后,均可较好区分TE事件的发生。对于整体人群,SAAE score对于TE的区分优于HCM Risk-CVA模型(P=0.013)。临床决策曲线结果提示在不同预测时间点(1、3、5年),SAAE score的净获益均优于HCM Risk-CVA。 结论 本研究在中国HCM患者中构建了针对TE事件的风险预测模型,即SAAE score,该模型可较好地对HCM患者进行TE事件风险分层。

关键词: 心肌病,肥厚性, 血栓栓塞, 卒中, 心房颤动, 年龄, 射血分数, 评分模型

Abstract:

Background

Thrombotic events are a major complication of hypertrophic cardiomyopathy (HCM). There are only two available risk prediction models for thrombotic events, HCM Risk-CVA score and French HCM score developed by foreign scholars, yet the former one has been found to have limited predictive value in Chinese HCM patients.

Objective

To develop a risk prediction model for thrombotic events in Chinese patients with HCM.

Methods

A retrospective cohort study design was used. Five hundred and thirty-seven HCM patients who admitted to West China Hospital of Sichuan University from 2010 to 2018 were recruited. Post-discharge health status was collected by use of telephone follow-up or checking the treatment status recorded in the electronic medical record system once every 6 to 12 months until a composite thrombotic event (defined as the endpoint event) or death occurred or the determined thrombotic risk assessment day of this study (2019-12-31). Univariate and multvariate Cox regression analyses were applied to build a thrombotic risk prediction model, and its internal validation was tested in a resample using the bootstrapping technique.

Results

Due to data missing, 24 cases were excluded, and the other 513 cases were finally included. During a median follow-up of 4.2 years (IQR: 1.3-6.2 years), thrombotic events occurred in 42 cases (8.18%), with an annual morbidity rate of 2.10%〔95%CI (1.47%, 2.73%) 〕. By multivariate Cox regression analysis, age, prior thrombotic event and left ventricular ejection fraction (LVEF) were identified (P<0.05) and used for constructing the formula of SAAE score (S=prior stroke and other thrombotic events, A=atrial fibrillation, A=age, E=LVEF) for predicting thrombotic events after being weighted based on the regression coefficient. Internal validation suggested that SAAE score could discriminate thrombotic events in the whole population {Harrell's C-index=0.773〔95%CI (0.688, 0.858) 〕}, with a calibration slope of 1.006, and could well discriminate 1-year, 3-year and 5-year thrombotic events (Harrell's C-index=0.790, 0.799, 0.735), with a good calibration ability. In addition, the SAAE score also performed well in distinguishing thrombotic events in patients with or without atrial fibrillation {Harrell's C-index=0.669〔95%CI (0.548, 0.791) 〕, 0.647〔95%CI (0.498, 0.795) 〕}, with good calibration ability. Besides that, SAAE score could partially discriminate 1-year, 3-year and 5-year thrombotic events in these two groups, with certain calibration ability. For three groups (whole study population, patients with/without atrial fibrillation), SAAE score could discriminate the risk of thrombotic events (either low, moderate or high) excellently. For the whole study population, SAAE score was better than HCM Risk-CVA score in distinguishing thrombotic events (P=0.013). Decision curve analysis showed the net benefit of SAAE score was better than HCM Risk-CVA score at different prediction time points (1, 3 and 5 years) .

Conclusion

This thrombotic events risk prediction model developed by us for Chinese HCM patients, namely SAAE score, could well stratify the risk of thrombotic events.

Key words: Cardiomyopathy, hypertrophic, Thromboembolism, Stroke, Atrial fibrillation, Age, Ejection fraction, Score models