Objective:

To develop non-invasive prediction models for the first ventilatory threshold (VT1) in patients with chronic heart failure (CHF) to improve exercise intensity prescriptions, addressing the inaccuracy of guideline-recommended methods and the limited accessibility of cardiopulmonary exercise testing (CPET).

Approach:

• Study Design: Analyzed 225 CHF patients who underwent standardized cardiopulmonary exercise testing (CPET) and developed prediction models using multivariate linear regression with ten-fold cross-validation, incorporating clinical parameters such as resting/peak exercise data, demographics, comorbidities, and medication.

Key Findings:

• 80% of patients achieved VO2-VT1 at 60%-90% of peak VO2.
• 75.6% reached HR-VT1 at 70%-90% of peak HR.
• The VO2-VT1 prediction model showed strong agreement (R2 = 0.65, RMSE = 1.42 mL/kg/min, ICC = 0.79).
• The HR-VT1 model demonstrated moderate-to-strong agreement (R2 = 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%).

Interpretation:

The validated models provide accurate, individualized estimation of VT1 parameters using basic clinical data.

Limitations:

• The study is limited to a specific population of Chinese CHF patients, which may affect generalizability.
• The models rely on readily available clinical parameters, which may not be uniformly accessible in all healthcare settings.

Conclusion:

The developed models offer a practical alternative to full CPET for personalized exercise prescription in resource-limited settings.