Decision models of prediabetes populations: A systematic review

Abstract

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 summarize and assess the quality and validity of decision models and model‐based economic evaluations of populations with prediabetes, to evaluate their potential use for the assessment of novel prevention strategies and to 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 we 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 standardized form and double extraction, each of four reviewers extracted 50% of the identified studies. Results A total of 29 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 among individuals with prediabetes (n = 7). Conclusions Current prediabetes decision models have considerable limitations in terms of their quality and validity and do not allow evaluation of stratified strategies using novel biomarkers, highlighting a clear need for more comprehensive prediabetes decision models.

Increased risks of different grades of non‐alcoholic fatty liver disease in prediabetic subjects with impaired fasting glucose and glucose tolerance, including the isolated glycosylated hemoglobin levels of 5.7–6.4% in a Chinese population

“…Since 2009, in addition to pre-existing diabetes based on fasting plasma glucose (FPG) and 2-h plasma glucose (2-h PG) after an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c) has been suggested to be a diagnostic criterion for the detection of diabetes and prediabetes 8 . However, many reports have pointed out diagnostic discrepancies among the three criteria used to categorize hyperglycemia, which could indicate distinct pathophysiological processes and aspects of glucose metabolism different from impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) [8][9][10] . When compared with use of the OGTT, there are several advantages of using FPG and HbA1c values, including greater convenience and fewer day-to-day perturbations (or greater stability), respectively 11 .…”

How valid are projections of the future prevalence of diabetes? Rapid reviews of prevalence-based and Markov chain models and comparisons of different models’ projections for England

“…We report three estimates of diabetes prevalence in England for 2025 (with 95% CIs where available): 4.39 by PHE,12 4.19 (2.93 to 6.19) by the APHO model,18 25 and 5.46 (5.32 to 5.59) from the QOF trend. We and Leal et al 7 found only minority of articles reported tests of validation. Such checks are vital for Markov chain models given the different data sources used to estimate transition probabilities.…”

“…In seven articles these probabilities do vary by age 20–23 25 29 30. In their systematic review of models of the economic impacts of preventive interventions, Leal et al 7 also found the majority of models assumed that ‘the rate of progression to T2D was constant across the entire pre-diabetes population’. They attribute this in part to limitations in the available data, but highlight the ‘stark contrast’ between these simple models and ‘The complexity of risk prediction models for diabetes incidence and the variety of covariates used’ 46 47.…”

“…The limitations of our research are our models are simple and transparent, and, as we did not undertake systematic reviews, we may have omitted relevant articles. The systematic review by Leal et al 7 reviewed 29 studies, which included 12 of the 17 studies of Markov chain models that we reviewed. Their principal findings are strikingly similar to ours.…”

“…We reviewed the transition probabilities of the different models, and whether they were used to estimate the future prevalence of T2D without a preventive intervention and tests of validation. In our discussion, we refer to the systematic review by Leal et al 7 of models of pre-diabetes populations used for reported economic outcomes or evaluations, which has been recently published.…”