Abstract 13186: A Machine Learning Evaluation of Patient Characteristics Associated With Guideline-Directed Prescribing for Heart Failure and Reduced Ejection Fraction

Abstract

Introduction: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently under-prescribed guideline-directed medications. Many barriers to prescribing are known, including patient characteristics (e.g., higher potassium values within normal limits predict under-prescribing of some medications). However, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. To overcome the limitations of traditional methods, we used a supervised machine learning (ML) approach to identify potential new barriers to prescribing. Methods: We used electronic health record data to evaluate characteristics of adults with HFrEF within the UCHealth system. Characteristics evaluated included insurance type, medications, diagnoses and lab values. We evaluated the predictive performance of ML algorithms and statistical models based on area under the receiver operating characteristic curve (AUC) to predict prescription of 4 types of medications: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). We identified top characteristics associated with prescribing each medication type from the models with the best predictive performance. Results: For 3,835 patients meeting inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB and 40% a MRA. The best-predicting model for each medication type was a random forest. Figure 1 describes the top characteristics associated with each medication type. Conclusions: In this hypothesis generating ML study, we identified additional patient characteristics that may be associated with under-prescribing for HFrEF. These results also align with some patient characteristics that are known barriers to prescribing. Additional research is needed to further elucidate barriers to prescribing for HFrEF.