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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.

We sought to evaluate outcomes, costs of care, quality of life and predictors at 12 months in patients with an acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI) and evaluated use of optimal secondary prevention therapy, defined as use of aspirin and clopidogrel along with ≥ 3 of the following 4 therapies at both hospital discharge and at one-year post-PCI: statins, beta-blockers, ARB/ACE-inhibitors, and exercise or diet.Data were from the prospective, observational APTOR study of 14 European countries from 2007 to 2009 (n=4184 patients).Optimal therapy was received in 43% of patients. Use of optimal therapy varied significantly by country. Diet or exercise at 1 year was more likely prescribed to the optimal cohort (34% vs 16%) as was dual antiplatelet therapy (99% vs 49%). Rates of CV event (3.1% vs 3.5%), bleeding (2.9% vs 2.8%) and mortality (0.9% vs 1.3%) at 1 year were similar between the optimal and non-optimal cohorts, respectively. Total costs were similar for both cohorts, but differences in post-discharge costs were observed (optimal: £1760 [£1682-£1844]; non-optimal: £1492 [£1434-£1554]), primarily due to post-discharge medication and resource use.In conclusion, in this contemporary, European ACS-PCI registry, optimal therapy was low (50%) overall, particularly for diet or exercise and dual antiplatelet therapy, highlighting a considerable gap between evidence-based guidelines and implementation of such treatments. Whether this gap reflects a missed opportunity to improve patient outcomes or whether it reflects appropriate deviation from guidelines by front-line clinicians requires further investigation.

 The ‘node-splitting’ model (Dias et al. 2010)  The direct and indirect evidence for each pairwise contrast are split out and compared  Model assesses whether relaxing the consistency constraint improves model fit  The ‘inconsistency’ model (Dias et al. 2010; Dias et al. 2013)  Independent treatment effects, whereby the consistency constraint is relaxed, are estimated for each treatment comparison for which trial evidence is available.  Where trials have more than two treatment arms, independent treatment effects are estimated for the comparisons against a common reference arm rather than for all the possible comparisons within a trial.  This can be compared to the ‘consistency’ model which is the traditional NMA approach assuming consistency between studies in the treatment effects.  The ‘design-by-treatment interaction’ model (Higgins et al. 2012).  ‘design’ refers to the combination of treatments included in a study e.g. estimating the relative treatment effect of A vs B using different designs AB (two arm study comparing A and B) or ABC (3 arm study comparing A, B and C).  The inconsistency between treatment effects using alternative study designs is estimated (Higgins et al. 2012)  These models were applied to the datasets to determine whether inconsistency was present and to examine potential causes. Table 2: Inconsistency results from ‘design-by-treatment’ model (16-36 week add-on to MET ± SU ± TZD) Assessing consistency in a network meta-analysis to compare once weekly dulaglutide versus other GLP-1 receptor agonists in patients with type 2 diabetes