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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chinmay Belthangady & Stefanos Giampanis & Ivana Jankovic & Will Stedden & Paula Alves & Stephanie Chong & Charlotte Knott & Beau Norgeot, 2022. "Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33732-9
    DOI: 10.1038/s41467-022-33732-9
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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    Cited by:

    1. Ayis Pyrros & Stephen M. Borstelmann & Ramana Mantravadi & Zachary Zaiman & Kaesha Thomas & Brandon Price & Eugene Greenstein & Nasir Siddiqui & Melinda Willis & Ihar Shulhan & John Hines-Shah & Jeann, 2023. "Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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