William J Valentine1, Richard F Pollock2, Rhodri Saunders2, Jay Bae3, Kirsi Norrbacka4, Kristina Boye3. 1. Ossian Health Economics and Communications, Basel, Switzerland. Electronic address: valentine@ossianconsulting.com. 2. Ossian Health Economics and Communications, Basel, Switzerland. 3. Eli Lilly and Company, Indianapolis, IN, USA. 4. Eli Lilly and Company, Helsinki, Finland.
Abstract
BACKGROUND: Recent publications describing long-term follow-up from landmark trials and diabetes registries represent an opportunity to revisit modeling options in type 1 diabetes mellitus (T1DM). OBJECTIVES: To develop a new product-independent model capable of predicting long-term clinical and cost outcomes. METHODS: After a systematic literature review to identify clinical trial and registry data, a model was developed (the PRIME Diabetes Model) to simulate T1DM progression and complication onset. The model runs as a patient-level simulation, making use of covariance matrices for cohort generation and risk factor progression, and simulating myocardial infarction, stroke, angina, heart failure, nephropathy, retinopathy, macular edema, neuropathy, amputation, hypoglycemia, ketoacidosis, mortality, and risk factor evolution. Several approaches novel to T1DM modeling were used, including patient characteristics and risk factor covariance, a glycated hemoglobin progression model derived from patient-level data, and model averaging approaches to evaluate complication risk. RESULTS: Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME Diabetes Model projects long-term patient outcomes consistent with those reported for a number of long-term studies. Macrovascular end points were reliably reproduced across five different populations and microvascular complication risk was accurately predicted on the basis of comparisons with landmark studies and published registry data. CONCLUSIONS: The PRIME Diabetes Model is product-independent, available online, and has been developed in line with good practice guidelines. Validation has indicated that outcomes from long-term studies can be reliably reproduced. The model offers new approaches to long-standing challenges in diabetes modeling and may become a valuable tool for informing health care policy.
BACKGROUND: Recent publications describing long-term follow-up from landmark trials and diabetes registries represent an opportunity to revisit modeling options in type 1 diabetes mellitus (T1DM). OBJECTIVES: To develop a new product-independent model capable of predicting long-term clinical and cost outcomes. METHODS: After a systematic literature review to identify clinical trial and registry data, a model was developed (the PRIME Diabetes Model) to simulate T1DM progression and complication onset. The model runs as a patient-level simulation, making use of covariance matrices for cohort generation and risk factor progression, and simulating myocardial infarction, stroke, angina, heart failure, nephropathy, retinopathy, macular edema, neuropathy, amputation, hypoglycemia, ketoacidosis, mortality, and risk factor evolution. Several approaches novel to T1DM modeling were used, including patient characteristics and risk factor covariance, a glycated hemoglobin progression model derived from patient-level data, and model averaging approaches to evaluate complication risk. RESULTS: Validation analyses comparing modeled outcomes with published studies demonstrated that the PRIME Diabetes Model projects long-term patient outcomes consistent with those reported for a number of long-term studies. Macrovascular end points were reliably reproduced across five different populations and microvascular complication risk was accurately predicted on the basis of comparisons with landmark studies and published registry data. CONCLUSIONS: The PRIME Diabetes Model is product-independent, available online, and has been developed in line with good practice guidelines. Validation has indicated that outcomes from long-term studies can be reliably reproduced. The model offers new approaches to long-standing challenges in diabetes modeling and may become a valuable tool for informing health care policy.
Authors: Zoe McCarroll; Julia Townson; Timothy Pickles; John W Gregory; Rebecca Playle; Michael Robling; Dyfrig A Hughes Journal: BMJ Open Date: 2021-05-19 Impact factor: 2.692
Authors: An Tran-Duy; Josh Knight; Andrew J Palmer; Dennis Petrie; Tom W C Lung; William H Herman; Björn Eliasson; Ann-Marie Svensson; Philip M Clarke Journal: Diabetes Care Date: 2020-06-12 Impact factor: 19.112