Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support

Sung Eun Choi, Seth A. Berkowitz, John S. Yudkin, Huseyin Naci and Sanjay Basu
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Sung Eun Choi: Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA
Seth A. Berkowitz: Division of General Medicine and Clinical Epidemiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
John S. Yudkin: University College London, London, UK
Huseyin Naci: London School of Economics, London, UK
Sanjay Basu: Center for Primary Care and Outcomes Research and Center for Population Health Sciences, Departments of Medicine and of Health Research and Policy, Stanford University, Stanford, CA, USA

Medical Decision Making, 2019, vol. 39, issue 3, 239-252

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.

Keywords: network meta-analysis; personalized medicine; shared decision making; type 2 diabetes mellitus (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:39:y:2019:i:3:p:239-252

DOI: 10.1177/0272989X19829735

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