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
| Autor: | Carolyn Koenig, David M. Kent, Francis R. Colangelo, John K. Cuddeback, Anastassios G. Pittas, Elizabeth L. Ciemins, Jason Nelson, David van Klaveren |
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| Přispěvatelé: | Public Health |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Diabetes risk business.industry Hazard ratio General Medicine medicine.disease Confidence interval Metformin Clinical trial SDG 3 - Good Health and Well-being Diabetes Mellitus Type 2 Internal medicine Diabetes mellitus Cohort Medicine Electronic Health Records Humans Hypoglycemic Agents Prediabetes Precision Medicine business Body mass index |
| Zdroj: | Mayo Clinic Proceedings, 97(4), 703-715. Elsevier Science |
| ISSN: | 1942-5546 0025-6196 |
| Popis: | OBJECTIVE: To 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.PATIENTS AND METHODS: A risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study.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% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (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 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients.CONCLUSION: The Tufts-Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions. |
| Databáze: | OpenAIRE |
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