Kato Taishi, Asanoi Hidetsugu, Ohtani Tomohito, Sakata Yasushi
Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine Osaka Japan.
Toyama Nishi General Hospital Toyama Japan.
Circ Rep. 2025 Feb 27;7(4):257-266. doi: 10.1253/circrep.CR-24-0135. eCollection 2025 Apr 10.
Low peak oxygen uptake (V̇O), 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 V̇O in HF patients using machine learning (ML).
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, V̇O at rest, and V̇O 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).
Using readily available parameters, ML methods can enable accurate prediction of low peak V̇O in patients with HF.
低峰值摄氧量（V̇O），尤其是≤14 mL/（min·kg），是心力衰竭（HF）患者预后不良的有力指标。然而，如果未达到最大负荷，测量该参数有时会很困难。本研究使用机器学习（ML）开发了一种用于预测HF患者低峰值V̇O的分类模型。
我们回顾性分析了343例慢性HF且左心室射血分数<50%的患者的数据，这些患者接受了症状限制性心肺运动试验，并从其实验室、超声心动图和运动数据中提取了直至次最大负荷的33个变量。数据集以4∶1的比例随机分为训练集和测试集。使用了包括穷举搜索预测变量选择在内的ML方法，并应用支持向量机算法进行模型优化。我们确定了5个重要的预测变量：年龄、B型利钠肽、左心室舒张末期直径、静息V̇O以及呼吸交换率为1.00时的V̇O。使用这5个预测变量，在测试数据集上验证了优化后的预测模型，其准确率为85%，F1分数为0.81，受试者工作特征曲线下面积为0.94（95%置信区间：0.89 - 1.00）。
使用易于获得的参数，ML方法能够准确预测HF患者的低峰值V̇O。
