Machine-Learning-Based Prediction of Exercise Intolerance of Patients With Heart Failure Using Pragmatic Submaximal Exercise Parameters.

Circulation reports Pub Date : 2025-02-27 eCollection Date: 2025-04-10 DOI:10.1253/circrep.CR-24-0135
Taishi Kato, Hidetsugu Asanoi, Tomohito Ohtani, Yasushi Sakata
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Abstract

Background: Low peak oxygen uptake (V̇O2), especially ≤14 mL/min/kg, is a strong indicator of poor prognosis in patients with heart failure (HF). However, measuring this parameter is sometimes difficult if the maximal workload is not reached. This study developed a predictive classification model for low peak V̇O2 in HF patients using machine learning (ML).

Methods and results: We retrospectively analyzed the data for 343 patients with chronic HF and left ventricular ejection fraction <50% who underwent a symptom-limited cardiopulmonary exercise test and extracted 33 variables from their laboratory, echocardiographic, and exercise data up to the submaximal workload. The dataset was randomly divided into training and testing datasets in a 4 : 1 ratio. ML methods, including an exhaustive search for predictor selection, were used, and a support vector machine algorithm was applied for model optimization. We identified 5 important predictors: age, B-type natriuretic peptide, left ventricular end-diastolic diameter, V̇O2 at rest, and V̇O2 at respiratory exchange ratio of 1.00. Using these 5 predictors, an optimized predictive model was validated on the testing dataset, yielding an accuracy of 85%, F1 score of 0.81, and area under the receiver operating curve of 0.94 (95% confidence interval: 0.89-1.00).

Conclusions: Using readily available parameters, ML methods can enable accurate prediction of low peak V̇O2 in patients with HF.

基于机器学习的心衰患者运动耐受性预测应用实用亚极限运动参数。
背景:低峰值摄氧量(V氧),特别是≤14 mL/min/kg,是心衰(HF)患者预后不良的一个重要指标。然而,如果没有达到最大工作量,测量这个参数有时会很困难。本研究利用机器学习(ML)技术建立了心衰患者低峰值V (O2)预测分类模型。方法与结果:回顾性分析343例慢性HF患者静息时左室射血分数2,呼吸交换比1.00时V / O2的资料。利用这5个预测因子,在测试数据集上验证了优化后的预测模型,准确率为85%,F1得分为0.81,受试者工作曲线下面积为0.94(95%置信区间:0.89-1.00)。结论:利用现成的参数,ML方法可以准确预测心衰患者的低峰值V (O2)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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