Comparison of medical resources and costs among patients with coronary heart disease and impaired glucose tolerance in the Acarbose Cardiovascular Evaluation trial.

BACKGROUND: The Acarbose Cardiovascular Evaluation (ACE) trial (ISRCTN91899513) evaluated the alpha-glucosidase inhibitor acarbose, compared with placebo, in 6522 patients with coronary heart disease and impaired glucose tolerance in China and showed a reduced incidence of diabetes. We assessed the within-trial medical resource use and costs, and quality-adjusted life years (QALYs). METHODS: Resource use data were collected prospectively within the ACE trial. Hospitalizations, medications, and outpatient visits were valued using Chinese unit costs. Medication use was measured in drug days, with cardiovascular and diabetes drugs summed across the trial by participant. Health-related quality of life was captured using the EuroQol-5 Dimension-3 Level questionnaire. Regression analyses were used to compare resource use, costs, and QALYs, accounting for regional variation. Costs and QALYs were discounted at 3% yearly. RESULTS: Hospitalizations were 6% higher in the acarbose arm during the trial (rate ratio 1.06, p = .009), but there were no significant differences in total inpatient days (rate ratio 1.04, p = .30). Total costs per participant, including study drug, were significantly higher for acarbose (¥ [Yuan] 56 480, £6213), compared with placebo (¥48 079, £5289; mean ratio 1.18, p < 0.001). QALYs reported by participants in the acarbose arm (3.96 QALYs) were marginally higher than in the placebo arm (3.95 QALYs), but the difference was not statistically significant (0.01 QALYs; p = .58). CONCLUSIONS: Acarbose, compared with placebo, participants cost more due to study drug costs and reported no statistically significant difference in QALYs. These higher within-trial costs could potentially be offset in future by savings from the acarbose-related lower incidence of diabetes.

Decision models of prediabetes populations: A systematic review.

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.