ArticleMedical decision making : an international journal of the Society for Medical Decision Making2019
Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Empagliflozin treatment effects across categories of baseline HbA1c, body weight and blood pressure as an add-on to metformin in patients with type 2 diabetes.Diabetes, obesity & metabolism · 2021Trial
- Is There Any Difference in Preferences Between Patients and Physicians? Evidence From Anti-Hyperglycemic Medications Choices for Type 2 Diabetes.Journal of diabetes · 2026Article
- Practical applications of methods to incorporate patient preferences into medical decision models: a scoping review.BMC medical informatics and decision making · 2025Article
- Sulfonylurea prescription patterns in elderly patients with type 2 diabetes mellitus: A comprehensive analysis of real-world data from pharmacies in Japan.Journal of diabetes investigation · 2024Article
- Advanced Glycation End Products and Their Effect on Vascular Complications in Type 2 Diabetes Mellitus.Nutrients · 2022Review
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPersonalizing 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.
methodsBased 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.
resultsPatients 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.
conclusionsModels can help integrate meta-analytic treatment effect estimates with individualized risk calculations and preferences, to aid personalized treatment selection.
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