AI-derived score to predict effort intolerance and adverse outcome across the heart failure spectrum
L Del Punta, S Moura Ferreira, G Georgiopoulos, G Mastoras, N De Biase, V Di Fiore, C Macheda, G Savarese, S Taddei, J Verwerft, S Masi, N R PuglieseAbstract
Background
Peak oxygen consumption (VO₂) is a prognostic indicator in heart failure (HF), but cardiopulmonary exercise testing (CPET) has limited feasibility.
Purpose
To develop an AI-driven model to predict effort intolerance (i.e., VO₂<16 mL/kg/min) in patients across HF spectrum.
Methods
The model was derived in a cohort of 1,333 subjects - 351 with reduced (<50%, HFrEF), 371 with preserved (>50%, HFpEF) left ventricular ejection fraction (LVEF), 611 with cardiovascular risk factors or structural heart disease without HF (Stages A–B) - and externally validated in a cohort of 1,101 subjects. All participants underwent laboratory test, rest echocardiography, CPET.
Results
A neural network including age, sex, body mass index (BMI), haemoglobin, systolic mitral annulus tissue velocity (S’), systolic pulmonary artery pressure (sPAP), and β-blocker therapy achieved good discrimination (AUC 0.86±0.01 in derivation; 0.76±0.06 in validation). A simplified AI-VO₂ score (BMI, haemoglobin, LV S’, sPAP) showed good performance (AUC 0.79±0.05) and predicted HF hospitalization or all-cause death (adjusted HR 1.06 per point; 95% CI 1.03–1.10) in the derivation cohort. External validation confirmed the performance (AUC 0.73±0.02; unadjusted HR 1.18 per point, 95% CI 1.13–1.24).
Conclusion
The AI-VO₂ score could identify patients with effort intolerance and adverse outcomes in HF spectrum.AI Vo2 scoreFor image description, please refer to the figure legend and surrounding text.Survival analysisFor image description, please refer to the figure legend and surrounding text.