扩张型心肌病合并肺动脉高压的风险因素分析及预测模型构建

骆航宙; 周平; 陈宇凯

doi:10.16766/j.cnki.issn.1674-4152.004203

扩张型心肌病合并肺动脉高压的风险因素分析及预测模型构建

doi: 10.16766/j.cnki.issn.1674-4152.004203

杭州师范大学附属医院心血管内科，浙江杭州 310000

基金项目:

浙江省医药卫生科技计划项目 2024KY199

详细信息

通讯作者:
陈宇凯，E-mail：3210392322@qq.com

中图分类号: R542.2 R544.16
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出版历程
- 收稿日期: 2025-06-12

A study on risk factor analysis and prediction model construction for dilated cardiomyopathy complicated with pulmonary hypertension

Department of Cardiovascular, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang 310000, China

摘要

摘要: 目的探讨扩张型心肌病(DCM)合并肺动脉高压(PH)的风险因素，构建并验证其预测模型，为早期识别高危人群及优化临床管理提供依据。方法选取2022年10月—2024年10月杭州师范大学附属医院收治的122例DCM患者，按是否合并PH分为PH组与非PH组(各61例)。采用二元logistic回归分析研究DCM合并PH的风险因素并构建预测模型，通过ROC曲线评估预测模型的效能，并在训练集和验证集上验证模型性能。结果 2组心电图QRS波、心电图中V1导联的R波+V5导联的S波振幅(RV₁+SV₅)、N末端脑钠肽前体(NT-proBNP)、左前降支冠状动脉(LAD)、肺动脉收缩压(PASP)差异有统计学意义(P < 0.05)。二元logistic回归分析结果显示，心电图QRS波、RV₁+SV₅、NT-proBNP、PASP均是DCM合并PH的影响因素(P < 0.05)。ROC分析显示心电图QRS波、RV₁+SV₅、NT-proBNP、PASP预测DCM出现PH的AUC为0.862、0.362、0.840、0.848，灵敏度分别为70.50%、88.50%、68.90%、86.90%，特异度分别为95.10%、9.80%、90.20%、73.80%；采用校准曲线评估模型的校准度，结果提示该列线图分度良好。依据预测模型计算公式得到预测模型数据，将预测模型数据作为检验变量，并以组别作为状态变量。建立ROC曲线分析各项模型的区分度，结果显示预测模型的AUC为0.985，灵敏度为91.80%，特异度为98.40%；绘制列线图校准曲线，训练集与验证集中校准曲线与理想曲线有较高的一致性，预测模型与实际验证差距较小。结论心电图QRS波、NT-proBNP、PASP是DCM合并PH的关键预测因子，综合模型预测效能优异，可为早期筛查及个体化治疗提供重要参考。
- 扩张型心肌病 /
- 肺动脉高压 /
- 风险因素 /
- 预测模型
Abstract: Objective To investigate risk factors for pulmonary hypertension (PH) in patients diagnosed with dilated cardiomyopathy (DCM) and develop a predictive model to facilitate early identification of patients who are at high risk, thus enabling optimized clinical management. Methods A total of 122 patients diagnosed with DCM patients were admitted to the Affiliated Hospital of Hangzhou Normal University between October 2022 and October 2024, were divided into PH and non-PH groups (61 each). Binary logistic regression was used to identify risk factors for PH in DCM and construct a predictive model. The predictive model ' s performance was evaluated using ROC curves, with calibration assessment being conducted in both the training and validation sets. Results Statistically significant differences were identified (P < 0.05) between the two groups with regard to QRS wave duration on electrocardiogram (ECG), the sum of R-wave amplitude in lead V1 and S-wave amplitude in lead V5 (RV₁+SV₅) on ECG, N-terminal pro-brain natriuretic peptide (NT-proBNP), left anterior descending coronary artery (LAD), and pulmonary artery systolic pressure (PASP). Binary logistic regression analysis revealed that QRS wave duration, RV₁+SV₅, NT-proBNP, and PASP were all influential factors for DCM complicated by PH (P < 0.05). ROC analysis demonstrated that the AUCs for predicting PH in DCM using QRS wave duration, RV₁+SV₅, NT-proBNP, and PASP were 0.862, 0.362, 0.840, and 0.848, respectively, the sensitivity were 70.50%, 88.50%, 68.90% and 86.90%, and the specificity were 95.10%, 9.80%, 90.20% and 73.80%, respectively. The calibration curve was utilized to evaluate the calibration of the model, and the results indicated effective discrimination of the nomogram. The prediction model ' s calculation formula was utilized to obtain prediction model data, which was then employed as test variables, with group assignment designated as the state variable. An ROC curve was undertaken for the purpose of analyzing the discrimination of each model, and the results showed an AUC of 0.985 for the prediction model, with a sensitivity of 91.80% and a specificity of 98.40%. A calibration curve for the nomogram was plotted, and there was a high degree of consistency between the calibration curves for the training and validation sets and the ideal curve, indicating a minor discrepancy between the prediction model and the actual validation. Conclusion QRS duration, NT-proBNP, and PASP are key predictors of PH in DCM. The model offers excellent predictive performance, supporting early screening and personalized treatment strategies for improved precision medicine.
- Dilated cardiomyopathy /
- Pulmonary hypertension /
- Risk factors /
- Prediction model
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图 1 预测DCM合并PH风险的列线图预测模型

Figure 1. Nomogram for predicting the risk of DCM with PH

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图 2 预测DCM出现PH的训练集及验证集ROC曲线

Figure 2. ROC curves of training and validation sets for predicting PH in DCM

下载: 全尺寸图片幻灯片

图 3 扩张型心肌病合并肺动脉高压预测模型的ROC曲线及训练集、验证集的校准曲线

Figure 3. Calibration and ROC curves of the prediction model in the training and validation sets

下载: 全尺寸图片幻灯片

表 1 2组DCM患者临床资料比较

Table 1. Comparison of clinical data between two groups of DCM patients

项目	类别	PH组(n=61)	非PH组(n=61)	统计量	P值
年龄(x ±s，岁)		48.93±12.66	47.31±12.03	0.726^a	0.469
性别[例(%)]	男性	43(70.49)	36(59.02)	1.760^b	0.185
	女性	18(29.51)	25(40.98)
BMI(x ±s)		24.03±3.68	23.51±3.07	0.847^a	0.398
吸烟[例(%)]	是	30(49.18)	27(44.26)	0.296^b	0.586
	否	31(50.82)	34(55.74)
糖尿病[例(%)]	是	13(21.31)	14(22.95)	0.048^b	0.827
	否	48(78.69)	47(77.05)
NYHA心功能分级[例(%)]	Ⅱ级	23(37.70)	31(50.82)	2.126^b	0.145
	Ⅲ~Ⅳ级	38(62.30)	30(49.18)
HR(x ±s，次/min)		99.03±12.07	94.07±17.98	1.791^a	0.076
SBP(x ±s，mmHg)		114.00±13.99	114.05±13.05	0.020^a	0.984
DBP(x ±s，mmHg)		71.00±12.96	71.03±12.04	0.013^a	0.989
HGB(x ±s，g/L)		137.00±15.00	135.98±12.97	0.402^a	0.689
心电图QRS波(x ±s，ms)		129.85±13.02	114.17±9.66	7.554^a	< 0.001
RV₁+SV₅(x ±s，mV)		0.71±0.13	0.83±0.21	3.798^a	< 0.001
NT-proBNP(x ±s，ng/L)		2 445.23±378.46	2 033.19±257.70	7.029^a	< 0.001
ALB(x ±s，mg/L)		41.58±3.31	42.07±3.14	0.839^a	0.403
LAD(x ±s，mm)		47.59±4.38	43.89±3.89	4.933^a	< 0.001
PASP(x ±s，mmHg)		33.16±2.32	29.58±2.68	7.888^a	< 0.001
LVEDD(x ±s，mm)		65.28±4.52	64.17±5.71	1.190^a	0.236
LVEF(x ±s，%)		34.13±3.39	35.32±3.75	1.839^a	0.068
MPAD(x ±s，mm)		26.74±2.81	25.74±3.63	1.702^a	0.091
RVTD(x ±s，mm)		37.59±4.26	36.43±3.80	1.587^a	0.115
注：^a为t值，^b为χ²值；1 mmHg=0.133 kPa。

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表 2 DCM合并PH风险因素的二元logistic回归分析

Table 2. Binary logistic regression analysis of risk factors for DCM combined with PH

变量	B	SE	Waldχ²	P值	OR值	95% CI
心电图QRS波	-0.133	0.061	4.672	0.031	0.876	0.777~0.988
RV₁+SV₅	-23.265	8.877	6.867	0.002	0.000	0.000~0.003
NT-proBNP	-0.007	0.003	6.135	0.013	0.993	0.988~0.999
LAD	-0.223	0.181	1.529	0.216	0.800	0.562~1.140
PASP	-0.733	0.268	7.481	0.006	0.481	0.284~0.812

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表 3 心电图QRS波、RV₁+SV₅、NT-proBNP、PASP预测DCM出现PH的价值

Table 3. The value of ECG QRS wave, RV1+SV5, NT-proBNP, and PASP for PH prediction in DCM

指标	AUC	95% CI	SE	P值	cut-off值	约登指数	灵敏度(%)	特异度(%)
心电图QRS波	0.862	0.793~0.930	0.035	< 0.001	125.305	0.656	70.50	95.10
RV₁+SV₅	0.362	0.259~0.464	0.052	0.008	0.565	-0.017	88.50	9.80
NT-proBNP	0.840	0.768~0.912	0.037	< 0.001	2 257.675	0.591	68.90	90.20
PASP	0.848	0.777~0.918	0.036	< 0.001	31.145	0.607	86.90	73.80

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