Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes.

ABSTRACT: Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4^th of the total healthcare spending in the United States (US). Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various combinations. Strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million high-risk diabetic patients (HbA1c ≥ 9%) in the US is analyzed to evaluate the comparative effectiveness for HbA1c reduction in this population of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical cohorts defined by age, insulin dependence, and a number

SUBMITTER: Belthangady C 

PROVIDER: S-EPMC9663714 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature