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

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.