An Electronic Health Record Compatible Model to Predict Personalized Treatment Effects from the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real World Data

Abstract

Background: An intensive lifestyle modification program or metformin pharmacotherapy reduced the risk of developing diabetes in patients at high risk, but are not widely used in the 88 million American adults with prediabetes. Objective: Develop an electronic health record (EHR) based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit. Design: Cross-design synthesis: risk prediction model developed and validated in large observational database, treatment effect estimates from risk-based reanalysis of clinical trial data. Setting: Outpatient clinics in US. Patients: Risk model development cohort: 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); validation cohort: distinct sample of 1.1 million patients in OLDW. Randomized clinical trial cohort: 3081 people from the Diabetes Prevention Program (DPP) study. Interventions: Randomization in the DPP: 1) an intensive program of lifestyle modification; 2) standard lifestyle recommendations plus 850 mg metformin twice daily; or 3) standard lifestyle recommendations plus placebo twice daily. Results: Eleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (c-statistic = 0.76; observed 3-year diabetes rate was 1.8% in lowest-risk quarter and 19.6% in highest-risk quarter). In the DPP, the hazard ratio for lifestyle modification was constant across all levels of risk (HR = 0.43, 95% CI 0.35 to 0.53); while the HR for metformin was highly risk-dependent (HR HR = 1.1 [95% CI: 0.61 to 2.0] in the lowest risk quarter vs. HR=0.45 [95% CI: 0.35 to 0.59] in the highest risk quarter). Fifty-three percent of the benefits of population wide dissemination of the DPP lifestyle modification, and 76% of the benefits of population wide metformin therapy can be obtained targeting the highest risk quarter of patients. Limitations: Differences in variable definitions and in missingness across observational and trial settings may introduce estimation error in risk based treatment effects. Conclusion: An EHR compatible risk model might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.