Causal Deep Learning on Real-world Data Reveals the Comparative Effectiveness of Anti-hyperglycemic Treatments

Abstract Type 2 Diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of total U.S. healthcare spending. Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various permutations and combinations. Personalized strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million diabetics was analyzed to evaluate the comparative effectiveness of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical subgroups defined by age, insulin dependence, and number of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized recommendations of treatment selection. Significant differences were observed in blood sugar reduction between patients receiving high vs low ranked treatment options and that less than 2% of the population is on a highly ranked treatment. This method can be extended to explore treatment optimization of other chronic conditions.