Sung Eun Choi1, Seth A Berkowitz2, John S Yudkin3, Huseyin Naci4, Sanjay Basu5,6,7. 1. Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA. 2. Division of General Medicine and Clinical Epidemiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA. 3. University College London, London, UK. 4. London School of Economics, London, UK. 5. 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. 6. Center for Primary Care, Harvard Medical School, Boston, MA, USA. 7. School of Public Health, Imperial College, London, UK.
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.
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.
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