稳定型冠心病患者心脏康复的风险分层模型

doi:10.3969/j.issn.1006-9771.2021.07.014

《中国康复理论与实践》 ›› 2021, Vol. 27 ›› Issue (7): 819-828.doi: 10.3969/j.issn.1006-9771.2021.07.014

稳定型冠心病患者心脏康复的风险分层模型

郑志昌¹,袁玮²,林伟¹,刘杰¹,王晓荣¹,杨威¹,于海涛¹,薛淞¹,王亚敏¹,唐丽¹,王国栋¹()

1.中国康复研究中心北京博爱医院心脏内科,北京市 100068
2.首都医科大学附属北京友谊医院呼吸内科,北京市 100050

收稿日期:2020-07-03 修回日期:2020-10-26 出版日期:2021-07-25 发布日期:2021-07-28
通讯作者: 王国栋 E-mail:lukewang1972@sohu.com
作者简介:郑志昌(1980-),男,汉族,河南洛阳市人,硕士,主治医师,主要研究方向：冠心病、心脏康复。|王国栋(1972-),男,汉族,山东诸城市人,博士,副主任医师,主要研究方向：冠心病、心脏康复。
基金资助:
中国康复研究中心科研项目(2019ZX-24)

A Risk Prediction Model for Cardiac Rehabilitation in Patients with Stable Coronary Artery Disease

ZHENG Zhi-chang¹,YUAN Wei²,LIN Wei¹,LIU Jie¹,WANG Xiao-rong¹,YANG Wei¹,YU Hai-tao¹,XUE Song¹,WANG Ya-min¹,TANG Li¹,WANG Guo-dong¹()

1. Department of Cardiovascular Medicine, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing 100068, China
2. Department of Respiratory Medicine, Beijing Friendship Hospital Affiliated of Capital Medical University, Beijing 100050, China

Received:2020-07-03 Revised:2020-10-26 Published:2021-07-25 Online:2021-07-28
Contact: WANG Guo-dong E-mail:lukewang1972@sohu.com
Supported by:
China Rehabilitation Research Center Project(2019ZX-24)

摘要/Abstract

摘要：

目的通过基于心肺运动试验(CPET)的测试数据和患者的一般临床资料,对稳定型冠心病患者进行心脏康复风险分层,区分出心脏康复的低危和高危患者。

方法连续纳入2014年12月至2018年12月本院冠心病数据库中冠脉造影术前行CPET检查的稳定型冠心病患者114例。使用LASSO回归进行变量筛选;使用Logistic回归建立评估模型,使用R软件的RMS包绘制评估模型的列线图;通过R软件的ROCR包绘制受试者工作特征曲线,计算曲线下面积(AUC)。

结果根据LASSO回归分析,确定7个预测因素：冠脉造影结果、最大二氧化碳通气当量(EqCO₂max)、淋巴细胞计数、空腹血糖水平、心肌酶阳性、血同型半胱氨酸和血尿素氮水平。结合临床经验及权重分析,最终纳入冠脉造影结果、EqCO₂max、淋巴细胞计数和空腹血糖水平4个因素进行Logistic回归建模;模型的AUC值为0.875,对结局事件有良好预测能力。

结论 EqCO₂max和淋巴细胞计数为稳定型冠心病患者心脏康复风险分层的主要预测因素,可用于识别稳定型冠心病心脏康复的高危患者;基于CPET和实验室检查建立的稳定型冠心病患者心脏康复风险分层评估模型,可以为稳定型冠心病患者心脏康复的风险评估提供帮助。

关键词: 冠心病, 心肺运动测试, 心脏康复

Abstract:

Objective To create a prediction model that could be used to stratify the risk of cardiac rehabilitation in patients with stable coronary artery disease by using test data based on cardiopulmonary exercise testing (CPET) and general clinical data.

Methods A total of 114 patients with stable coronary artery disease were consecutively enrolled from the Cardiology Coronary Artery Disease Database of our hospital from December, 2014 to December, 2018, all the patients underwent CPET before coronary angiography. LASSO was used for feature selection. A nomogram was formulated based on the results of multivariate Logistic regression analysis using the RMS package of R. The predictive power was assessed with Receiver Operating Characteristic Curve.

Results Seven predictors were identified based on LASSO: coronary angiography results, the maximum value of ventilatory equivalent for carbon dioxide (EqCO₂max), lymphocyte count, fasting blood glucose levels, cardiac muscle enzyme positivity, blood homocysteine and blood urea nitrogen levels. Combined with clinical experience and weighting analysis, the final four factors were included for Logistic regression modeling: coronary angiography results, EqCO₂max, lymphocyte count and fasting blood glucose levels. The area under the curve was 0.875 for the model.

Conclusion EqCO₂max and lymphocyte count are key predictors for stable coronary heart disease and can be used to identify patients at high risk for cardiac rehabilitation. A risk stratification model based on CPET and laboratory tests can be used to assess risk stratification for cardiac rehabilitation in patients with stable coronary artery disease.

Key words: coronary artery disease, Cardiopulmonary Exercise Test, cardiac rehabilitation

中图分类号:

R541.4

郑志昌,袁玮,林伟,刘杰,王晓荣,杨威,于海涛,薛淞,王亚敏,唐丽,王国栋. 稳定型冠心病患者心脏康复的风险分层模型[J]. 《中国康复理论与实践》, 2021, 27(7): 819-828.

ZHENG Zhi-chang,YUAN Wei,LIN Wei,LIU Jie,WANG Xiao-rong,YANG Wei,YU Hai-tao,XUE Song,WANG Ya-min,TANG Li,WANG Guo-dong. A Risk Prediction Model for Cardiac Rehabilitation in Patients with Stable Coronary Artery Disease[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(7): 819-828.

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项目	风险分层		t/χ²值	P值
项目	低危组(n = 100)	高危组(n = 14)	t/χ²值	P值
年龄(岁)	59.490±7.543	60.210±8.210	-0.333	0.740
性别(n)			5.363	0.021
男	53	12
女	47	2
体质量指数(kg/m²)	26.288±3.384	25.179±1.882	1.197	0.234
高血压病史(n)	65	12	2.404	0.121
糖尿病史(n)	35	4	0.225	0.635
高血脂病史(n)	66	11	0.885	0.347
卒中病史(n)	4	1	0.289	0.591
吸烟(n)			6.805	0.078
不吸烟	58	5
吸烟、已戒	17	1
吸烟、未戒	25	8
饮酒(n)			6.325	0.097
不饮酒	64	6
饮酒、已戒	10	0
饮酒、未戒	26	8

实验室检查	风险分层		t/Z/χ²值	P值
实验室检查	低危组(n = 100)	高危组(n = 14)	t/Z/χ²值	P值
白细胞(×10⁹ /L)	6.410±1.615	7.00(4.750, 8.000)	-0.457	0.648
中性粒细胞(×10⁹ /L)	4.155±1.398	4.941±3.075	-1.639	0.104
淋巴细胞(×10⁹ /L)	1.811±0.607	1.496±0.409	1.871	0.020
红细胞(×10¹² /L)	4.770±1.214	4.50(4.000, 5.000)	-0.852	0.394
血小板(×10⁹ /L)	223.390±50.789	215.710±58.950	0.519	0.605
总胆固醇(mmol/L)	4.130±0.895	4.290±1.267	-0.577	0.565
甘油三酯(mmol/L)	1.630±1.253	1.400(1.000, 2.000)	-0.216	0.829
低密度脂蛋白胆固醇(mmol/L)	2.460±0.731	2.570±0.852	-0.524	0.602
高密度脂蛋白胆固醇(mmol/L)	1.120±0.409	1.000(0.900, 1.100)	-0.346	0.729
空腹血糖(mmol/L)	5.970±1.766	7.210±3.401	-2.153	0.033
超敏C反应蛋白 (mg/dL)	0.530±0.429	0.200(0.100, 0.625)	-0.757	0.449
同型半胱氨酸 (μmol/L)	12.480±4.631	16.710±12.003	-2.484	0.014
尿素氮(mmol/L)	5.320±1.377	5.860±1.956	-1.293	0.199
肌酐(μmol/L)	68.410±16.261	72.790±28.110	-0.850	0.397
尿酸(μmol/L)	331.540±100.749	345.140±119.413	-0.462	0.645
CK-MB阳性(n)	0	3	15.001	0.006
造影结果(n)			7.602	0.006
阴性	37	0
阳性	63	14

CPET	风险分层		t/Z值	P值
CPET	低危组(n = 100)	高危组(n = 14)	t/Z值	P值
Loadmax (W)	99.910±31.455	102.930±24.550	-0.344	0.731
VEmax (L/min)	47.910±13.419	52.070±11.552	-1.103	0.272
AT (ml/min)	879.430±215.891	937.860±238.943	-0.936	0.351
VO₂max (ml/min)	1341.000±343.888	1339.430±259.536	0.016	0.987
HRmax (次/min)	160±8	159±8	0.890	0.375
VO₂/HR (ml/次)	10.959±2.362	11.471±2.176	-0.765	0.446
VCO₂max (ml/min)	1508.780±451.536	1532.50±371.499	-0.188	0.851
EqCO₂max	30.689±3.803	33.343±4.770	-2.368	0.020
EqCO₂-AT	31.050±4.498	32.570±4.327	-1.191	0.236
RER	1.117±0.090	1.150(1.107, 1.203)	-1.270	0.204

稳定型冠心病患者心脏康复的风险分层模型

A Risk Prediction Model for Cardiac Rehabilitation in Patients with Stable Coronary Artery Disease

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