221-OR: A Machine Learning Approach Identifies Modulators of Congestive Heart Failure Hospitalization Prevention among Patients with Type 2 Diabetes: A Revisit to the ACCORD Trial

Objective: To examine patient characteristics that modulate the efficacy of risk factor reduction in preventing hospitalization for congestive heart failure (CHF) in diabetes management. Methods: The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial tested the cardioprotective effect of achieving A1c Results: Individuals with baseline Alanine transaminase (ALT) at the lowest quartile ( 18 mg/dl and low-density lipoprotein > 110 mg/dl who were receiving ACE inhibitor at baseline also benefited substantially from SBP control (ARR: -1.6%, 95% CI: -2.8% to -0.3%). Optimizing lipid therapy with fenofibrate lowered CHF risk by -1.3% ~ -2.4% in individuals with normal baseline creatine phosphokinase (CPK) ( Conclusion: This novel approach demonstrates that ML can identify patients with characteristics that are more or less likely to reduce CHF risk from intensive therapy. Disclosure H. Kianmehr: None. J. Guo: None. L. Shi: Research Support; Self; AstraZeneca, Sanofi. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. H. Shao: Research Support; Self; Sanofi.