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Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials

BACKGROUND: Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large...

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Detalles Bibliográficos
Autores principales: Kresoja, Karl-Patrik, Unterhuber, Matthias, Wachter, Rolf, Rommel, Karl-Philipp, Besler, Christian, Shah, Sanjiv, Thiele, Holger, Edelmann, Frank, Lurz, Philipp
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498181/
https://www.ncbi.nlm.nih.gov/pubmed/37689023
http://dx.doi.org/10.1016/j.ebiom.2023.104795
Descripción
Sumario:BACKGROUND: Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large randomized clinical trials. METHODS: Using a reiterative cluster allocating permutation approach, patients from the derivation cohort (Aldo-DHF) were identified according to their treatment response to spironolactone with respect to improvement in E/e’. Heterogenous features of response (‘responders’ and ‘non-responders’) were characterized by an extreme gradient boosting (XGBoost) algorithm. XGBoost was used to predict treatment response in the validation cohort (TOPCAT). The primary endpoint of the validation cohort was a combined endpoint of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. Patients with missing variables for the XGboost model were excluded from the validation analysis. FINDINGS: Out of 422 patients from the derivation cohort, reiterative cluster allocating permutation identified 159 patients (38%) as spironolactone responders, in whom E/e’ significantly improved (p = 0.005). Within the validation cohort (n = 525) spironolactone treatment significantly reduced the occurrence of the primary outcome among responders (n = 185, p log rank = 0.008), but not among patients in the non-responder group (n = 340, p log rank = 0.52). INTERPRETATION: Machine learning approaches might aid in identifying HFpEF patients who are likely to show a favorable therapeutic response to spironolactone. FUNDING: See Acknowledgements section at the end of the manuscript.