Frontiers in cardiovascular medicine · 2026
Non-invasive prediction of the first ventilatory threshold in Chinese patients with chronic heart failure for personalized exercise prescription.
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Liu Xiaoling, Yuan Jue, Yang Qingxuan, Fan Yuxuan, Xiu Wenjuan, Shen Yuqin
Аннотация
PURPOSE: This study aimed to develop non-invasive prediction models for the first ventilatory threshold (VT1) in patients with chronic heart failure (CHF), to address the inaccuracy of guideline-recommended, percentage-based exercise intensity prescriptions (40%-69% peak VO2, 55%-75% peak HR) and the limited accessibility of gold-standard cardiopulmonary exercise testing (CPET).
METHODS: We analyzed 225 CHF patients who underwent standardized CPET. Using multivariate linear regression with ten-fold cross-validation, we developed prediction models for VT1 oxygen consumption (VO2) and heart rate (HR) based on readily available clinical parameters, including resting/peak exercise data, demographics, comorbidities, and medication. Model performance was evaluated using R 2, root mean squared error (RMSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland-Altman analysis.
RESULTS: Traditional percentage-based methods were inadequate: 80% of patients achieved VO2-VT1 at 60%-90% of peak VO2, and 75.6% reached HR-VT1 at 70%-90% of peak HR. The VO2-VT1 prediction model showed strong agreement (R 2 = 0.65, RMSE = 1.42 mL/kg/min, ICC = 0.79). The HR-VT1 model demonstrated moderate-to-strong agreement (R 2 = 0.57, RMSE = 8.4 bpm, ICC = 0.71). Bland-Altman analysis indicated good agreement for both models (Within LoA: 95.1% and 95.6%).
CONCLUSION: CHF patients exhibit distinct exercise intensity patterns. Our validated models enable accurate, individualized estimation of VT1 parameters using basic clinical and exercise test data, offering a practical alternative to full CPET for personalized exercise prescription in resource-limited settings.
METHODS: We analyzed 225 CHF patients who underwent standardized CPET. Using multivariate linear regression with ten-fold cross-validation, we developed prediction models for VT1 oxygen consumption (VO2) and heart rate (HR) based on readily available clinical parameters, including resting/peak exercise data, demographics, comorbidities, and medication. Model performance was evaluated using R 2, root mean squared error (RMSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland-Altman analysis.
RESULTS: Traditional percentage-based methods were inadequate: 80% of patients achieved VO2-VT1 at 60%-90% of peak VO2, and 75.6% reached HR-VT1 at 70%-90% of peak HR. The VO2-VT1 prediction model showed strong agreement (R 2 = 0.65, RMSE = 1.42 mL/kg/min, ICC = 0.79). The HR-VT1 model demonstrated moderate-to-strong agreement (R 2 = 0.57, RMSE = 8.4 bpm, ICC = 0.71). Bland-Altman analysis indicated good agreement for both models (Within LoA: 95.1% and 95.6%).
CONCLUSION: CHF patients exhibit distinct exercise intensity patterns. Our validated models enable accurate, individualized estimation of VT1 parameters using basic clinical and exercise test data, offering a practical alternative to full CPET for personalized exercise prescription in resource-limited settings.