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

Chinmay Belthangady, Stefanos Giampanis, Ivana Jankovic, Will Stedden, Paula Alves, Stephanie Chong, Charlotte Knott and Beau Norgeot ()
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Chinmay Belthangady: Elevance Health Palo Alto
Stefanos Giampanis: Elevance Health Palo Alto
Ivana Jankovic: Elevance Health Palo Alto
Will Stedden: Elevance Health Palo Alto
Paula Alves: Elevance Health Palo Alto
Stephanie Chong: Elevance Health Palo Alto
Charlotte Knott: Elevance Health Palo Alto
Beau Norgeot: Elevance Health Palo Alto

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4th 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 of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized evaluation of treatment selection. An average confounder-adjusted reduction in HbA1c of 0.69% [−0.75, −0.65] is observed between patients receiving high vs low ranked treatments across cohorts for which the difference was significant. This method can be extended to explore treatment optimization for other chronic conditions.

Date: 2022
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DOI: 10.1038/s41467-022-33732-9

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