Use of Big-Data Algorithms to Characterize Patients with T2D on Basal Insulin (BI) Who Add a Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA) and Predict Their A1C Response

Machine learning allows extensive analysis of big complex data. This study had two aims: 1) characterize patients on BI who add a GLP-1RA and 2) identify predictors of ≥1% decline in A1C. Patients with T2D who were prescribed BI for ≥90 days but not GLP-1RA for 180 days beforehand (in the U.S. IBM Explorys database between 2010 and 2016) were included (N=80,019). For the A1C analysis, A1C readings ≤180 days before, and 180-360 days after initiating GLP-1RA were required (N=8731). Logistic regression with 23 pre-specified variables, and subsequent hypothesis-free machine learning models, with 155000 additional variables covering clinical, claims and billing data addressed both aims. GLP-1RA initiators were characterized by a BI duration of u003e180 days (vs. ≤180 days) estimated odds ratio (OR) 5.87 (95% CI: 5.49-6.27), receiving oral antidiabetic drugs(s) OR 1.70 (1.64-1.77) and co-medication(s) (both vs. none) OR 3.22 (2.96-3.50), a BMI u003e30 kg/m 2 (vs. 2 ) OR 1.93 (1.84-2.03), age Disclosure E. Zimmermann: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. A. Lenart: Employee; Self; Novo Nordisk A/S. J. da Rocha Fernandes: Employee; Self; Novo Nordisk A/S, International Diabetes Federation. S. Eggert: Employee; Self; Novo Nordisk A/S. M.F. Ranthe: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S.