Establishing a novel model to assess exercise capacity in chronic heart failure based on stress echocardiography

Abstract

Abstract Aims The aim of this study was to develop a simple, fast and efficient clinical diagnostic model, composed of exercise stress echocardiography (ESE) indicators, of the exercise capacity of patients with chronic heart failure (CHF) by comparing the effectiveness of different classifiers. Methods and results Eighty patients with CHF (aged 60 ± 11 years; 78% male) were prospectively enrolled in this study. All patients underwent both cardiopulmonary exercise test (CPET) and ESE and were divided into two groups according to the VE/VCO2 slope: 30 patients with VE/VCO2 slope ventilation classification (VC)1 (i.e., VE/VCO2 slope 0.05 for the above). Conclusions Compared with the LR, XGBT, CART and RF models, the LR model performed best at predicting the VE/VCO2 slope category of CHF patients. A score chart was created to predict VE/VCO2 slopes ≥ 30. ΔE, s′peak and sex are independent predictors of exercise capacity in CHF patients.

Keywords

• Cardiopulmonary exercise test
• Chronic heart failure
• Exercise capacity
• Exercise stress echocardiography
• Machine learning
• VE/VCO2 slope