• Alberto Cordero, Vicente Bertomeu-Gonzalez, José V Segura, Javier Morales, Belén Álvarez-Álvarez, David Escribano, Moisés Rodríguez-Manero, Belén Cid-Alvarez, José M García-Acuña, José Ramón González-Juanatey, and Asunción Martínez-Mayoral.
• Departamento de Cardiología, Hospital IMED Elche, Elche, Alicante, España; Grupo de Investigación Cardiovascular, Universidad Miguel Hernández, Elche, Alicante, España; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España. Electronic address: acorderofort@gmail.com.
• Med Clin (Barc). 2024 Aug 30; 163 (4): 167174167-174.

BackgroundCoronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be useful in identifying variables related to the development of cardiovascular complications.MethodsWe included all consecutive patients discharged after ACS in two Spanish centers between 2006 and 2017. Clinical data were collected and patients were followed up for a median of 53months. Decision tree models were created by the model-based recursive partitioning algorithm.ResultsThe cohort consisted of 7,097 patients with a median follow-up of 53months (interquartile range: 18-77). The readmission rate for HF was 13.6% (964 patients). Eight relevant variables were identified to predict HF hospitalization time: HF at index hospitalization, diabetes, atrial fibrillation, glomerular filtration rate, age, Charlson index, hemoglobin, and left ventricular ejection fraction. The decision tree model provided 15 clinical risk patterns with significantly different HF readmission rates.ConclusionsThe decision tree model, obtained by AI, identified 8 leading variables capable of predicting HF and generated 15 differentiated clinical patterns with respect to the probability of being hospitalized for HF. An electronic application was created and made available for free.Copyright © 2024 Elsevier España, S.L.U. All rights reserved.

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