PREDICTION OF EJECTION FRACTION OF LEFT VENTRICLE IN PATIENTS WITH TYPE 2 DIABETES MELLITUS ON EMPAGLIFLOZIN: A SIX-MONTH ASSESSMENT
Keywords:
diabetes mellitus, machine learning, risk prediction, EMPAGLIFLOZINAbstract
Diabetes mellitus type 2 (T2DM) is a growing global health concern, often leading to cardiovascular complications, including left ventricular dysfunction. Predicting left ventricular ejection fraction (LVEF) can be crucial for early intervention and management. Empagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, has garnered attention for its efficacy in managing type 2 diabetes mellitus (T2DM) and its potential cardiovascular benefits. This study aims to identify significant predictors of LVEF in T2DM patients treated with Empagliflozin after six months of therapy. We aim to identify outcomes related to cardiac function and the predictive factors influencing LVEF changes during this treatment phase. We applied a systematic feature selection approach through generalized linear models (GLM) to build a predictive model based on a cohort of 130 patients.
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