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BACKGROUND

Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery.

METHODS AND RESULTS

For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune-inflammation index, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701-0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521-0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram.

CONCLUSIONS

A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery.

REGISTRATION

https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.

背景

文献中关于缺血性心肌病和射血分数保留的心力衰竭患者冠状动脉旁路移植术疗效的可用数据有限。因此，我们的目标是使用机器学习技术整合临床特征、生物标志物数据和超声心动图数据，以增强对接受冠状动脉旁路移植手术的缺血性心肌病和射血分数保留的心力衰竭患者的理解和风险分层。

方法和结果

在本研究中，294例接受冠状动脉旁路移植手术的缺血性心肌病和射血分数保留的心力衰竭患者被分配到开发队列（n = 176）和独立验证队列（n = 118）。为每位患者提取了总共52个临床变量。主要临床终点是主要不良心血管事件的发生率，包括心脏死亡、急性心肌梗死、急性心力衰竭和移植物失败。通过最小绝对收缩和选择算子回归，为最终预测列线图选择了4个预测因子：糖尿病、高血压、全身免疫炎症指数和NT-proBNP（N端前脑钠肽）。预测列线图在开发队列（C指数，0.768 [95% CI，0.701 - 0.835]）和独立验证队列（C指数，0.633 [95% CI，0.521 - 0.745]）中均取得了令人满意的预测性能。在开发队列和独立验证队列中，主要不良心血管事件的可能性均具有充分的校准。决策曲线分析证实了所建立的预测列线图的临床实用性。