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Personalizing second-line Type 2 diabetes treatment selection: combining network meta-analysis, individualized risk, and patient preferences for unified decision support

Choi, Sung Eun, Berkowitz, Seth A., Yudkin, John S., Naci, Huseyin and Basu, Sanjay (2019) Personalizing second-line Type 2 diabetes treatment selection: combining network meta-analysis, individualized risk, and patient preferences for unified decision support. Medical Decision Making, 39 (3). pp. 239-252. ISSN 0272-989X

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Identification Number: 10.1177/0272989X19829735

Abstract

Background. Personalizing medical treatment often requires practitioners to compare multiple treatment options, assess a patient’s unique risk and benefit from each option, and elicit a patient’s preferences around treatment. We integrated these 3 considerations into a decision-modeling framework for the selection of second-line glycemic therapy for type 2 diabetes. Methods. Based on multicriteria decision analysis, we developed a unified treatment decision support tool accounting for 3 factors: patient preferences, disease outcomes, and medication efficacy and safety profiles. By standardizing and multiplying these 3 factors, we calculated the ranking score for each medication. This approach was applied to determining second-line glycemic therapy by integrating 1) treatment efficacy and side-effect data from a network meta-analysis of 301 randomized trials (N = 219,277), 2) validated risk equations for type 2 diabetes complications, and 3) patient preferences around treatment (e.g., to avoid daily glucose testing). Data from participants with type 2 diabetes in the U.S. National Health and Nutrition Examination Survey (NHANES 2003–2014, N = 1107) were used to explore variations in treatment recommendations and associated quality-adjusted life-years given different patient features. Results. Patients at the highest microvascular disease risk had glucagon-like peptide 1 agonists or basal insulin recommended as top choices, whereas those wanting to avoid an injected medication or daily glucose testing had sodium-glucose linked transporter 2 or dipeptidyl peptidase 4 inhibitors commonly recommended, and those with major cost concerns had sulfonylureas commonly recommended. By converting from the most common sulfonylurea treatment to the model-recommended treatment, NHANES participants were expected to save an average of 0.036 quality-adjusted life-years per person (about a half month) from 10 years of treatment. Conclusions. Models can help integrate meta-analytic treatment effect estimates with individualized risk calculations and preferences, to aid personalized treatment selection.

Item Type: Article
Additional Information: © 2019 The Authors
Divisions: Health Policy
Subjects: R Medicine > RZ Other systems of medicine
Date Deposited: 27 Mar 2019 14:12
Last Modified: 16 Jan 2020 00:48
URI: http://eprints.lse.ac.uk/id/eprint/100363

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