AI-guided integration of aging-related functional assessment to predict nonresponse to cardiac rehabilitation in heart failure
Abstract
BACKGROUND: Cardiac rehabilitation (CR) improves functional capacity and outcomes in patients with heart failure (HF). However, a clinically significant subgroup of patients exhibits limited improvements in aerobic capacity, indicating substantial heterogeneity in response and limiting the effectiveness of standardized rehabilitation programs. OBJECTIVE: To develop an interpretable artificial intelligence (AI)-guided framework to identify predictors of nonresponse to CR and support personalized rehabilitation strategies in HF, with age-related functional decline as a key determinant of heterogeneity. METHODS: Data on patient characteristics, exercise interventions, and response rates were extracted from Randomized controlled trials evaluating exercise-based CR in patients with HF that reported changes in peak oxygen uptake (VO₂peak) or functional capacity. Nonresponse was defined as <10% improvement in VO₂peak after CR. Coupling of eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) was introduced to characterize age-related determinants of nonresponse to CR in HF. RESULTS: Across 38 randomized trials including 5,198 patients, 30-45% failed to achieve clinically meaningful improvements in VO₂peak after CR. Advanced age was consistently associated with attenuated benefit, with older patients showing reduced gains. Non-response was linked to higher age, lower baseline aerobic capacity, metabolic comorbidities, impaired skeletal muscle function, and adverse psychosocial status. An interpretable AI-guided approach integrating aging-associated functional and psychosocial parameters was introduced to CR in patients with HF. Principal predictors of nonresponse included impaired baseline aerobic capacity, metabolic comorbidities, diminished skeletal muscle function and adverse psychosocial factors, delineating an age-modulated vulnerability to impaired rehabilitation adaptation. CONCLUSION: Age is associated with cardiac rehabilitation response in heart failure, with heterogeneous effects across outcomes. An AI-driven framework can be introduced to identify age-related non-response phenotypes, supporting precision rehabilitation strategies.
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BibTeX
@article{chemineau2026aiguided,
title = {{AI-guided integration of aging-related functional assessment to predict nonresponse to cardiac rehabilitation in heart failure}},
author = {Martin Chemineau and Ryeonshi Kang and Mathieu Cinato and Lesia Savchenko and Solomiia Kramar and Audrey Swiader and Anne Negre-Salvayre and Alexandre Heurteau and Raoul Raffel and Vanessa Nader and Charlotte Laborde and Nathalie Pizzinat and Jerome Roncalli and Yoan Sallami and Oxana Kunduzova},
journal = {Journal of Nutrition, Health \& Aging},
year = {2026},
month = oct,
doi = {10.1016/j.jnha.2026.100881},
url = {https://hal.science/hal-05789445v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AI-guided integration of aging-related functional assessment to predict nonresponse to cardiac rehabilitation in heart failure
AU - Martin Chemineau
AU - Ryeonshi Kang
AU - Mathieu Cinato
AU - Lesia Savchenko
AU - Solomiia Kramar
AU - Audrey Swiader
AU - Anne Negre-Salvayre
AU - Alexandre Heurteau
AU - Raoul Raffel
AU - Vanessa Nader
AU - Charlotte Laborde
AU - Nathalie Pizzinat
AU - Jerome Roncalli
AU - Yoan Sallami
AU - Oxana Kunduzova
PY - 2026
DA - 2026/10/09
JO - Journal of Nutrition, Health & Aging
DO - 10.1016/j.jnha.2026.100881
UR - https://hal.science/hal-05789445v1
AB - BACKGROUND: Cardiac rehabilitation (CR) improves functional capacity and outcomes in patients with heart failure (HF). However, a clinically significant subgroup of patients exhibits limited improvements in aerobic capacity, indicating substantial heterogeneity in response and limiting the effectiveness of standardized rehabilitation programs. OBJECTIVE: To develop an interpretable artificial intelligence (AI)-guided framework to identify predictors of nonresponse to CR and support personalized rehabilitation strategies in HF, with age-related functional decline as a key determinant of heterogeneity. METHODS: Data on patient characteristics, exercise interventions, and response rates were extracted from Randomized controlled trials evaluating exercise-based CR in patients with HF that reported changes in peak oxygen uptake (VO₂peak) or functional capacity. Nonresponse was defined as <10% improvement in VO₂peak after CR. Coupling of eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) was introduced to characterize age-related determinants of nonresponse to CR in HF. RESULTS: Across 38 randomized trials including 5,198 patients, 30-45% failed to achieve clinically meaningful improvements in VO₂peak after CR. Advanced age was consistently associated with attenuated benefit, with older patients showing reduced gains. Non-response was linked to higher age, lower baseline aerobic capacity, metabolic comorbidities, impaired skeletal muscle function, and adverse psychosocial status. An interpretable AI-guided approach integrating aging-associated functional and psychosocial parameters was introduced to CR in patients with HF. Principal predictors of nonresponse included impaired baseline aerobic capacity, metabolic comorbidities, diminished skeletal muscle function and adverse psychosocial factors, delineating an age-modulated vulnerability to impaired rehabilitation adaptation. CONCLUSION: Age is associated with cardiac rehabilitation response in heart failure, with heterogeneous effects across outcomes. An AI-driven framework can be introduced to identify age-related non-response phenotypes, supporting precision rehabilitation strategies.
SN - 1279-7707
SN - 1760-4788
ER -