Decision models of prediabetes populations: a systematic review.
Leal J., Morrow LM., Kurshid W., Pagano E., Feenstra T.
AIMS: With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarise and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, evaluate their potential use for the assessment of novel prevention strategies and discuss the knowledge gaps, challenges and opportunities. MATERIALS AND METHODS: We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardised form, and double extraction, four reviewers each extracted 50% of identified studies. RESULTS: Twenty-nine published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n=8) and type 2 diabetes (n=17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n=7) and accounted for heterogeneity amongst individuals with prediabetes (n=7). CONCLUSIONS: Current prediabetes decision models have considerable limitations in terms of their quality and validity and are not equipped to evaluate stratified strategies using novel biomarkers highlighting a clear need for more comprehensive prediabetes decision models. This article is protected by copyright. All rights reserved.