Post-infarct cardiac remodeling predictions with machine learning

Xavier Dieu (1) , Floris Chabrun (1) , Fabrice Prunier (1) , Denis Angoulvant (2) , Nathan Mewton (3) , François Roubille (4, 5) , Pascal Reynier (1) , Marc Ferre (1) , Valérie Moal (1) , Laurane Cottin (6) , Alain Furber (1) , Gabriel Garcia (1) , Loic Bière (1) , Delphine Mirebeau-Prunier (1)

1 MITOVASC - MitoVasc - Physiopathologie Cardiovasculaire et Mitochondriale
2 T2i - Transplantation, Immunologie, Inflammation [Tours]
3 CarMeN - Cardiovasculaire, métabolisme, diabétologie et nutrition
4 PhyMedExp - Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046]
5 CHRU Montpellier - Centre Hospitalier Régional Universitaire [Montpellier]
6 CRCINA - Centre de Recherche en Cancérologie et Immunologie Nantes-Angers

Background: We sought to improve the risk prediction of 3-month left ventricular remodeling (LVR) occurrence after myocardial infarction (MI), using a machine learning approach. Methods: Patients were included from a prospective cohort study analyzing the incidence of LVR in ST-elevation MI in 443 patients that were monitored at Angers University Hospital, France. Clinical, biological and cardiac magnetic resonance (CMR) imaging data from the first week post MI were collected, and LVR was assessed with CMR at 3 month. Data were processed with a machine learning pipeline using multiple feature selection algorithms to identify the most informative variables. Results: We retrieved 133 clinical, biological and CMR imaging variables, from 379 patients with ST-elevation MI. A baseline logistic regression model using previously known variables achieved an AUC of 0.71 on the test set, with 67% sensitivity and 64% specificity. In comparison, our best predictive model was a neural network using seven variables (in order of importance): creatine kinase, mean corpuscular volume, baseline left atrial surface, history of diabetes, history of hypertension, red blood cell distribution width, and creatinine. This model achieved an AUC of 0.78 on the test set, reaching a sensitivity of 92% and a specificity of 55%, outperforming the baseline model. Conclusion: These preliminary results show the value of using an unbiased data-driven machine learning approach. We reached a higher level of sensitivity compared to traditional methods for the prediction of a 3-month post-MI LVR

Mots clés: Left ventricular remodeling; Myocardial infarction; Machine learning; Deep learning; Neural networks; Cardiac magnetic resonance

Domaines: Cardiologie et système cardiovasculaire; Santé publique et épidémiologie; Machine Learning [stat.ML]; Apprentissage [cs.LG]

Xavier Dieu, Floris Chabrun, Fabrice Prunier, Denis Angoulvant, Nathan Mewton, et al.. Post-infarct cardiac remodeling predictions with machine learning. International Journal of Cardiology, 2022, 355, pp.1-4. ⟨10.1016/j.ijcard.2022.02.009⟩. ⟨hal-03572426⟩