Abstract
Introduction: Sodium-glucose cotransporter type 2 inhibitors (SGLT2i), gliflozins, play an emerging role for the treatment of heart failure with reduced left ventricular ejection fraction (HFrEF). Nevertheless, the effects of SGLT2i on ventricular remodeling and function have not been completely understood yet. Explainable artificial intelligence represents an unprecedented explorative option to clinical research in this field. Based on echocardiographic evaluations, we identified some key clinical responses to gliflozins by employing a machine learning approach. Methods: Seventy-eight consecutive diabetic outpatients followed for HFrEF were enrolled in the study. Using a random forests classification, a single subject analysis was performed to define the profile of patients treated with gliflozins. An explainability analysis using Shapley values was used to outline clinical parameters that mostly improved after gliflozin therapy and machine learning runs highlighted specific variables predictive of gliflozin response. Results: The five-fold cross-validation analyses showed that gliflozins patients can be identified with a 0.70 ± 0.03% accuracy. The most relevant parameters distinguishing gliflozins patients were Right Ventricular S'-Velocity, Left Ventricular End Systolic Diameter and E/e' ratio. In addition, low Tricuspid Annular Plane Systolic Excursion values along with high Left Ventricular End Systolic Diameter and End Diastolic Volume values were associated to lower gliflozin efficacy in terms of anti-remodeling effects. Discussion: In conclusion, a machine learning analysis on a population of diabetic patients with HFrEF showed that SGLT2i treatment improved left ventricular remodeling, left ventricular diastolic and biventricular systolic function. This cardiovascular response may be predicted by routine echocardiographic parameters, with an explainable artificial intelligence approach, suggesting a lower efficacy in case of advanced stages of cardiac remodeling.