UK Prospective Diabetes Study Outcomes Research Articles

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Comparison of prediction models for cardiovascular and mortality risk in people with type 2 diabetes: An external validation in 23 685 adults included in the UK Biobank.

To validate cardiovascular risk prediction models for individuals with diabetes using the UK Biobank in order to assess their applicability. We externally validated 19 cardiovascular risk scores from seven risk prediction models (Chang et al., Framingham, University of Hong Kong-Singapore [HKU-SG], Li et al, RECODe [risk equations for complications of type 2 diabetes], SCORE [Systematic Coronary Risk Evaluation] and the UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2]), identified from systematic reviews, using UK Biobank data from 2006 to 2021 (n = 23 685; participant age 40-71 years, 63.5% male). We evaluated performance by assessing the discrimination and calibration of the models for the endpoints of mortality, cardiovascular mortality, congestive heart failure, myocardial infarction, stroke, and ischaemic heart disease. Over a total of 269 430 person-years of follow-up (median 11.89 years), the models showed low-to-moderate discrimination performance on external validation (concordance indices [c-indices] 0.50-0.71). Most models had low calibration with overprediction of the observed risk. RECODe outperformed other models across four comparable endpoints for discrimination: all-cause mortality (c-index 0.67, 95% confidence interval [CI] 0.65-0.69), congestive heart failure (c-index 0.71, 95% CI 0.69-0.72), myocardial infarction (c-index 0.67, 95% CI 0.65-0.68); and stroke (c-index 0.65, 95% CI 0.62-0.68), and for calibration (except for all-cause mortality). The UKPDS OM2 had comparable performance to RECODe for all-cause mortality (c-index 0.67, 95% CI 0.66-0.69) and cardiovascular mortality (c-index 0.71, 95% CI 0.70-0.73), but worse performance for other outcomes. The models performed better for younger participants and somewhat better for non-White ethnicities. Models developed from non-Western datasets showed worse performance in our UK-based validation set. The RECODe model led to better risk estimations in this predominantly White European population. Further validation is needed in non-Western populations to assess generalizability to other populations.

Estimation of HbA1c after weight loss using a beta-regression for an economic evaluation of a behavioural weight management programme from the GLoW trial: a methodological study

BackgroundIn economic evaluations, average intervention effects are usually applied to a population. However, this fails to reflect the change in the distribution of HbA1c due to heterogeneous responses to weight loss. We aimed to investigate whether allowing heterogeneous treatment effects using a beta regression better represented the distribution of HbA1c after a weight-loss intervention, and how this affected cost effectiveness. MethodsThe Glucose Lowering through Weight Management (GLoW) trial evaluated the effectiveness of a diabetes education and weight-loss intervention against a standard diabetes education programme. Adults diagnosed with type 2 diabetes within 3 years were recruited from Clinical Commissioning Groups across 159 sites in England from July 20, 2018, to July 22, 2018. Ethics approval (18/ES/0048) and participant informed consent were obtained. Considering the between-treatment-arm difference in HbA1c after 12 months, we compared a mean-effect estimated from a mixed-effects regression to a heterogeneous effect estimated from a beta regression performed on 12-month HbA1c conditional on baseline HbA1c, gender, diabetes duration and intervention group. We used the School of Public Health Research (SPHR) Diabetes Treatment model to apply these treatment effects and evaluate the lifetime NHS costs and quality-adjusted life-years (QALYs), discounted at 3·5%. The microsimulation model estimated diabetes-related health outcomes using the UK Prospective Diabetes Study Outcomes Model 2 risk equations and risk factor trajectory equations, alongside estimating diabetes remission, osteoarthritis, and cancer. We calculated the incremental net benefit (INB) of the intervention using a £20 000 per QALY valuation, by deterministic analysis. The GLoW trial is registered with the ISRCTN Registry, ISRCTN18399564. FindingsThe trial recruited 577 participants (mean age 60 years; 278 [53%] female, 247 [47%] male; 474 [91%] white ethnic background). Applying heterogeneous HbA1c changes better reproduced the skewness in post-intervention HbA1c than applying a mean-effect (Kolmogorov-Smirnov test p=0·02 compared with p=0·0000007). The beta-regression method suggested the intervention was more cost-effective, estimating an INB of £736 per person, compared with £584 when applying the mean-effect. InterpretationAlternative regression specification methods should be considered when evaluating the cost-effectiveness of interventions if the key intervention outcomes are not normally distributed. However, this alternative method requires further investigation to conclude its appropriateness in evaluating cost-effectiveness in different contexts. FundingNational Institute for Health and Care Research (NIHR) Programme Grants for Applied Research Programme (Reference Number RP-PG0216-20010).

Health and economic impact of improved glucose, blood pressure and lipid control among German adults with type 2 diabetes: a modelling study.

The aim of this study was to estimate the long-term health and economic consequences of improved risk factor control in German adults with type2 diabetes. We used the UK Prospective Diabetes Study Outcomes Model2 to project the patient-level health outcomes and healthcare costs of people with type2 diabetes in Germany over 5, 10 and 30 years. We parameterised the model using the best available data on population characteristics, healthcare costs and health-related quality of life from German studies. The modelled scenarios were: (1) a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), of systolic BP (SBP) by 10 mmHg, or of LDL-cholesterol by 0.26 mmol/l in all patients, and (2) achievement of guideline care recommendations for HbA1c (≤53 mmol/mol [7%]), SBP (≤140 mmHg) or LDL-cholesterol (≤2.6 mmol/l) in patients who do not meet the recommendations. We calculated nationwide estimates using age- and sex-specific quality-adjusted life year (QALY) and cost estimates, type2 diabetes prevalence and population size. Over 10 years, a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), SBP by 10 mmHg or LDL-cholesterol by 0.26 mmol/l led to per-person savings in healthcare expenditures of €121, €238 and €34, and 0.01, 0.02 and 0.015 QALYs gained, respectively. Achieving guideline care recommendations for HbA1c, SBP or LDL-cholesterol could reduce healthcare expenditure by €451, €507 and €327 and gained 0.03, 0.05 and 0.06 additional QALYs in individuals who did not meet the recommendations. Nationally, achieving guideline care recommendations for HbA1c, SBP and LDL-cholesterol could reduce healthcare costs by over €1.9 billion. Sustained improvements in HbA1c, SBP and LDL-cholesterol control among diabetes patients in Germany can lead to substantial health benefits and reduce healthcare expenditures.

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups.

To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Healthy food prescription incentive programme for adults with type 2 diabetes who are experiencing food insecurity: protocol for a randomised controlled trial, modelling and implementation studies

IntroductionThe high cost of many healthy foods poses a challenge to maintaining optimal blood glucose levels for adults with type 2 diabetes mellitus who are experiencing food insecurity, leading to...

Lifetime cost-effectiveness simulation of once-weekly exenatide in type 2 diabetes: A cost-utility analysis based on the EXSCEL trial

AimsThe Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial assessed once-weekly exenatide (EQW) vs. placebo, added to usual care in 14,752 patients with type 2 diabetes mellitus (Clinicaltrials.gov: NCT01144338). We assessed the lifetime cost-effectiveness of adding EQW vs. usual care alone from a healthcare perspective. MethodsMedical resource use and EQ-5D utilities were collected throughout the study. Within-trial results were extrapolated to a lifetime horizon using the UK Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2), predicting predict cardiovascular and microvascular events. Cost-effectiveness was evaluated separately for US and UK settings, with outcomes measured in quality-adjusted life-years (QALYs). ResultsEQW plus usual care gained 0.162 QALYs at an additional cost of $41,545/patient, compared with usual care in a US setting. The incremental cost-effectiveness ratio (ICER) was $259,223/QALY. In a UK setting, the QALY gain was 0.151 at an additional cost of £6357: an ICER of £42,589/QALY. Sensitivity analyses ranged between $34,369–$269,571 and £3430–£46,560 per QALY gained. ConclusionsIn a lifetime extrapolation, adding EQW to usual care increased QALYs and costs compared with usual care alone. The base-case ICERs exceeded the commonly-cited cost-effectiveness thresholds of $100,000/QALY and £20,000/QALY. However, ICERs were considerably lower in some subgroups, and in sensitivity analyses.

Lifetime Cost-effectiveness of Oral Semaglutide Versus Dulaglutide and Liraglutide in Patients With Type 2 Diabetes Inadequately Controlled With Oral Antidiabetics

PurposeTo estimate the incremental cost-utility ratio of oral semaglutide (14 mg once daily) vs other glucagon-like peptide 1 receptor agonist treatments among adults with type 2 diabetes that was inadequately controlled with 1 to 2 oral antidiabetic drugs from a US payer perspective. MethodsA state-transition model with a competing risk approach was developed for diabetic complications and risk of cardiovascular events based on the UK Prospective Diabetes Study Outcomes Model 1 equations. Baseline population characteristics reflect the PIONEER 4 trial (Efficacy and Safety of Oral Semaglutide Versus Liraglutide and Versus Placebo in Subjects With Type 2 Diabetes Mellitus) of oral semaglutide. Model comparators included subcutaneous semaglutide, dulaglutide, and liraglutide. Treatment effects (change in glycosylated hemoglobin, weight, and systolic blood pressure) were estimated by network meta-analysis. Drug, management, and event costs (in 2019 US dollars), survival after nonfatal events, and utilities were obtained from the literature. Costs and quality-adjusted life-year (QALY) outcomes were discounted at 3% annually over a lifetime horizon. Probabilistic and 1-way sensitivity analyses were performed. FindingsTotal estimated costs and QALYs were $144,065 and 12.98 for oral semaglutide, $145,721 and 12.96 for dulaglutide, $145,833 and 12.99 for SC semaglutide, and $149,428 and 12.97 for liraglutide, respectively. Oral semaglutide was less costly and more effective than dulaglutide and liraglutide but less costly than subcutaneous semaglutide with similar effectiveness. Oral semaglutide was favored versus subcutaneous semaglutide in 52.10% of model replications at a willingness-to-pay of $150,000 per QALY. ImplicationsOral semaglutide is predicted to offer health benefits similar to subcutaneous semaglutide and ahead of dulaglutide and liraglutide. Oral semaglutide is a cost-effective glucagon-like peptide 1 receptor agonist treatment option.

Performance of the UK Prospective Diabetes Study Outcomes Model 2 in a Contemporary UK Type 2 Diabetes Trial Cohort

ObjectivesThe UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes’ projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017. MethodsThe UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias. ResultsThe UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72). ConclusionsThe UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts.

Prediction of mortality and major cardiovascular complications in type 2 diabetes: External validation of UK Prospective Diabetes Study outcomes model version 2 in two European observational cohorts.

To externally validate the UK Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) by comparing the predicted and observed outcomes in two European population-based cohorts of people with type 2 diabetes. We used data from the Casale Monferrato Survey (CMS; n = 1931) and a subgroup of the Hoorn Diabetes Care System (DCS) cohort (n = 5188). The following outcomes were analysed: all-cause mortality, myocardial infarction (MI), ischaemic heart disease (IHD), stroke, and congestive heart failure (CHF). Model performance was assessed by comparing predictions with observed cumulative incidences in each cohort during follow-up. All-cause mortality was overestimated by the UKPDS-OM2 in both the cohorts, with a bias of 0.05 in the CMS and 0.12 in the DCS at 10 years of follow-up. For MI, predictions were consistently higher than observed incidence over the entire follow-up in both cohorts (10 years bias 0.07 for CMS and 0.10 for DCS). The model performed well for stroke and IHD outcomes in both cohorts. CHF incidence was predicted well for the DCS (5 years bias -0.001), but underestimated for the CMS cohort. The UKPDS-OM2 consistently overpredicted the risk of mortality and MI in both cohorts during follow-up. Period effects may partially explain the differences. Results indicate that transferability is not satisfactory for all outcomes, and new or adjusted risk equations may be needed before applying the model to the Italian or Dutch settings.

Benchmarking the Cost-Effectiveness of Interventions Delaying Diabetes: A Simulation Study Based on NAVIGATOR Data.

OBJECTIVETo estimate using the UK Prospective Diabetes Study Outcomes Model Version 2 (UKPDS-OM2) the impact of delaying type 2 diabetes onset on costs and quality-adjusted life expectancy using trial participants who developed diabetes in the NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) study.RESEARCH DESIGN AND METHODSWe simulated the impact of delaying diabetes onset by 1–9 years, utilizing data from the 3,058 of 9,306 NAVIGATOR trial participants who developed type 2 diabetes. Costs and utility weights associated with diabetes and diabetes-related complications were obtained for the U.S. and U.K. settings, with costs expressed in 2017 values. We estimated discounted lifetime costs and quality-adjusted life years (QALYs) with 95% CIs.RESULTSGains in QALYs increased from 0.02 (U.S. setting, 95% CI 0.01, 0.03) to 0.15 (U.S. setting, 95% CI 0.10, 0.21) as the imposed time to diabetes onset was increased from 1 to 9 years, respectively. Savings in complication costs increased from $1,388 (95% CI $1,092, $1,669) for a 1-year delay to $8,437 (95% CI $6,611, $10,197) for a delay of 9 years. Interventions costing up to $567–$2,680 and £201–£947 per year would be cost-effective at $100,000 per QALY and £20,000 per QALY thresholds in the U.S. and U.K., respectively, as the modeled delay in diabetes onset was increased from 1 to 9 years.CONCLUSIONSSimulating a hypothetical diabetes-delaying intervention provides guidance concerning the maximum cost and minimum delay in diabetes onset needed to be cost-effective. These results can inform the ongoing debate about diabetes prevention strategies and the design of future intervention studies.

Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Introduction. Patient-level simulation models facilitate extrapolation of clinical trial data while allowing for heterogeneity, prior history, and nonlinearity. However, combining different types of uncertainty around within-trial and extrapolated results remains challenging. Methods. We tested 4 methods to combine parameter uncertainty (around the regression coefficients used to predict future events) with sampling uncertainty (uncertainty around mean risk factors within the finite sample whose outcomes are being predicted and the effect of treatment on these risk factors). We compared these 4 methods using a simulation study based on an economic evaluation extrapolating the AFORRD randomized controlled trial using the UK Prospective Diabetes Study Outcomes Model version 2. This established type 2 diabetes model predicts patient-level health outcomes and costs. Results. The 95% confidence intervals around life years gained gave 25% coverage when sampling uncertainty was excluded (i.e., 25% of 95% confidence intervals contained the “true” value). Allowing for sampling uncertainty as well as parameter uncertainty widened confidence intervals by 6.3-fold and gave 96.3% coverage. Methods adjusting for baseline risk factors that combine sampling and parameter uncertainty overcame the bias that can result from between-group baseline imbalance and gave confidence intervals around 50% wider than those just considering parameter uncertainty, with 99.8% coverage. Conclusions. Analyses extrapolating data for individual trial participants should include both sampling uncertainty and parameter uncertainty and should adjust for any imbalance in baseline covariates.

Validation of Risk Equations for Complications of Type 2 Diabetes (RECODe) Using Individual Participant Data From Diverse Longitudinal Cohorts in the U.S.

OBJECTIVEWe sought to validate Risk Equations for Complications of Type 2 Diabetes (RECODe) among diverse populations.RESEARCH DESIGN AND METHODSWe compared risk predictions from RECODe equations and from two alternative risk models (UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2] and American College of Cardiology/American Heart Association Pooled Cohort Equations) to observed outcomes in two studies: the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,555 adults with type 2 diabetes, median follow-up 9.1 years) and the Jackson Heart Study (JHS, n = 1,746 adults with type 2 diabetes, median follow-up 8.0 years). Outcomes included nephropathy by multiple measures (microalbuminuria, macroalbuminuria, renal failure, end-stage renal disease, and reduction in glomerular filtration rate), moderate to severe diabetic retinopathy by Airlie House classification, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, congestive heart failure, and all-cause mortality.RESULTSRECODe equations for microvascular and cardiovascular outcomes had C-statistics for discrimination ranging from 0.71 to 0.85 in MESA and 0.64 to 0.91 in JHS for alternative outcomes. Calibration slopes in MESA ranged from 0.62 for a composite nephropathy outcome, 0.83–1.04 for individual nephropathy outcomes, 1.07 for retinopathy, 1.00–1.05 for cardiovascular outcomes, and 1.03 for all-cause mortality. Slopes in JHS ranged from 0.47 for retinopathy, 0.97–1.16 for nephropathy, 0.72–1.05 for cardiovascular outcomes, and 1.01 for all-cause mortality. The alternative models had C-statistics 0.50–0.72 and calibration slopes 0.07–0.60.CONCLUSIONSRECODe equations improved risk estimation for diverse patients with type 2 diabetes, as compared with two commonly used alternatives.

Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials

In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12). Microvascular outcomes were nephropathy, retinopathy, and neuropathy. Cardiovascular outcomes were myocardial infarction, stroke, congestive heart failure, and cardiovascular mortality. We also included all-cause mortality as an outcome. We used a cross-validating machine learning method to select predictor variables from demographic characteristics, clinical variables, comorbidities, medications, and biomarkers into Cox proportional hazards models for each outcome. The new equations were compared to older risk equations by assessing model discrimination, calibration, and the net reclassification index. All equations had moderate internal and external discrimination (C-statistics 0·55-0·84 internally, 0·57-0·79 externally) and high internal and external calibration (slopes 0·71-1·31 between observed and estimated risk). Our equations had better discrimination and calibration than the UK Prospective Diabetes Study Outcomes Model 2 (for microvascular and cardiovascular outcomes, C-statistics 0·54-0·62, slopes 0·06-1·12) and the American College of Cardiology/American Heart Association Pooled Cohort Equations (for fatal or non-fatal myocardial infarction or stroke, C-statistics 0·61-0·66, slopes 0·30-0·39). RECODe might improve estimation of risk of complications for patients with type 2 diabetes. National Institute for Diabetes and Digestive and Kidney Disease, National Heart, Lung and Blood Institute, and National Institute on Minority Health and Health Disparities, National Institutes of Health, and US Department of Veterans Affairs.

Incremental Costs and Cost Effectiveness of Intensive Treatment in Individuals with Type 2 Diabetes Detected by Screening in the ADDITION-UK Trial: An Update with Empirical Trial–Based Cost Data

BackgroundThere is uncertainty about the cost effectiveness of early intensive treatment versus routine care in individuals with type 2 diabetes detected by screening. ObjectivesTo derive a trial-informed estimate of the incremental costs of intensive treatment as delivered in the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care-Europe (ADDITION) trial and to revisit the long-term cost-effectiveness analysis from the perspective of the UK National Health Service. MethodsWe analyzed the electronic primary care records of a subsample of the ADDITION-Cambridge trial cohort (n = 173). Unit costs of used primary care services were taken from the published literature. Incremental annual costs of intensive treatment versus routine care in years 1 to 5 after diagnosis were calculated using multilevel generalized linear models. We revisited the long-term cost-utility analyses for the ADDITION-UK trial cohort and reported results for ADDITION-Cambridge using the UK Prospective Diabetes Study Outcomes Model and the trial-informed cost estimates according to a previously developed evaluation framework. ResultsIncremental annual costs of intensive treatment over years 1 to 5 averaged £29.10 (standard error = £33.00) for consultations with general practitioners and nurses and £54.60 (standard error = £28.50) for metabolic and cardioprotective medication. For ADDITION-UK, over the 10-, 20-, and 30-year time horizon, adjusted incremental quality-adjusted life-years (QALYs) were 0.014, 0.043, and 0.048, and adjusted incremental costs were £1,021, £1,217, and £1,311, resulting in incremental cost-effectiveness ratios of £71,232/QALY, £28,444/QALY, and £27,549/QALY, respectively. Respective incremental cost-effectiveness ratios for ADDITION-Cambridge were slightly higher. ConclusionsThe incremental costs of intensive treatment as delivered in the ADDITION-Cambridge trial were lower than expected. Given UK willingness-to-pay thresholds in patients with screen-detected diabetes, intensive treatment is of borderline cost effectiveness over a time horizon of 20 years and more.

A randomised trial of the effect and cost-effectiveness of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with screen-detected type 2 diabetes: the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION-Europe) study.

Intensive treatment (IT) of cardiovascular risk factors can halve mortality among people with established type 2 diabetes but the effects of treatment earlier in the disease trajectory are uncertain. To quantify the cost-effectiveness of intensive multifactorial treatment of screen-detected diabetes. Pragmatic, multicentre, cluster-randomised, parallel-group trial. Three hundred and forty-three general practices in Denmark, the Netherlands, and Cambridge and Leicester, UK. Individuals aged 40-69 years with screen-detected diabetes. Screening plus routine care (RC) according to national guidelines or IT comprising screening and promotion of target-driven intensive management (medication and promotion of healthy lifestyles) of hyperglycaemia, blood pressure and cholesterol. The primary end point was a composite of first cardiovascular event (cardiovascular mortality/morbidity, revascularisation and non-traumatic amputation) during a mean [standard deviation (SD)] follow-up of 5.3 (1.6) years. Secondary end points were (1) all-cause mortality; (2) microvascular outcomes (kidney function, retinopathy and peripheral neuropathy); and (3) patient-reported outcomes (health status, well-being, quality of life, treatment satisfaction). Economic analyses estimated mean costs (UK 2009/10 prices) and quality-adjusted life-years from an NHS perspective. We extrapolated data to 30 years using the UK Prospective Diabetes Study outcomes model [version 1.3; (©) Isis Innovation Ltd 2010; see www.dtu.ox.ac.uk/outcomesmodel (accessed 27 January 2016)]. We included 3055 (RC, n = 1377; IT, n = 1678) of the 3057 recruited patients [mean (SD) age 60.3 (6.9) years] in intention-to-treat analyses. Prescription of glucose-lowering, antihypertensive and lipid-lowering medication increased in both groups, more so in the IT group than in the RC group. There were clinically important improvements in cardiovascular risk factors in both study groups. Modest but statistically significant differences between groups in reduction in glycated haemoglobin (HbA1c) levels, blood pressure and cholesterol favoured the IT group. The incidence of first cardiovascular event [IT 7.2%, 13.5 per 1000 person-years; RC 8.5%, 15.9 per 1000 person-years; hazard ratio 0.83, 95% confidence interval (CI) 0.65 to 1.05] and all-cause mortality (IT 6.2%, 11.6 per 1000 person-years; RC 6.7%, 12.5 per 1000 person-years; hazard ratio 0.91, 95% CI 0.69 to 1.21) did not differ between groups. At 5 years, albuminuria was present in 22.7% and 24.4% of participants in the IT and RC groups, respectively [odds ratio (OR) 0.87, 95% CI 0.72 to 1.07), retinopathy in 10.2% and 12.1%, respectively (OR 0.84, 95% CI 0.64 to 1.10), and neuropathy in 4.9% and 5.9% (OR 0.95, 95% CI 0.68 to 1.34), respectively. The estimated glomerular filtration rate increased between baseline and follow-up in both groups (IT 4.31 ml/minute; RC 6.44 ml/minute). Health status, well-being, diabetes-specific quality of life and treatment satisfaction did not differ between the groups. The intervention cost £981 per patient and was not cost-effective at costs ≥ £631 per patient. Compared with RC, IT was associated with modest increases in prescribed treatment, reduced levels of risk factors and non-significant reductions in cardiovascular events, microvascular complications and death over 5 years. IT did not adversely affect patient-reported outcomes. IT was not cost-effective but might be if delivered at a reduced cost. The lower than expected event rate, heterogeneity of intervention delivery between centres and improvements in general practice diabetes care limited the achievable differences in treatment between groups. Further follow-up to assess the legacy effects of early IT is warranted. ClinicalTrials.gov NCT00237549. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 64. See the NIHR Journals Library website for further project information.

Cost-effectiveness of metformin plus vildagliptin compared with metformin plus sulphonylurea for the treatment of patients with type 2 diabetes mellitus: a Portuguese healthcare system perspective

Objective:To evaluate the cost-effectiveness of vildagliptin plus metformin vs generic sulphonylurea plus metformin in patients with type 2 diabetes mellitus, not controlled with metformin, from a Portuguese healthcare system perspective.Methods:A cost-effectiveness model was constructed using risk equations from the UK Prospective Diabetes Study Outcomes Model with a 10,000-patient cohort and a lifetime horizon. The model predicted microvascular and macrovascular complications and mortality in yearly cycles. Patients entered the model as metformin monotherapy failures and switched to alternative treatments (metformin plus basal-bolus insulin and subsequently metformin plus intensive insulin) when glycated hemoglobin A1c >7.5% was reached. Baseline patient characteristics and clinical variables were derived from a Portuguese epidemiological study. Cost estimates were based on direct medical costs only. One-way and probabilistic sensitivity analyses were conducted to test the robustness of the model.Results:There were fewer non-fatal diabetes-related adverse events (AEs) in patients treated with metformin plus vildagliptin compared with patients treated with metformin plus sulphonylurea (6752 vs 6815). Addition of vildagliptin compared with sulphonylurea led to increased drug acquisition costs but reduced costs of AEs, managing morbidities, and monitoring patients. Treatment with metformin plus vildagliptin yielded a mean per-patient gain of 0.1279 quality-adjusted life years (QALYs) and a mean per-patient increase in total cost of €1161, giving an incremental cost-effectiveness ratio (ICER) of €9072 per QALY. Univariate analyses showed that ICER values were robust and ranged from €4195 to €16,052 per QALY when different parameters were varied.Limitations:The model excluded several diabetes-related morbidities, such as peripheral neuropathy and ulceration, and did not model second events. Patients were presumed to enter the model with no diabetes-related complications.Conclusion:Treatment with metformin plus vildagliptin compared with metformin plus sulphonylurea is expected to result in a lower incidence of diabetes-related AEs and to be a cost-effective treatment strategy.

Temporal Validation of the UKPDS Outcomes Model Using 10-Year Posttrial Monitoring Data

OBJECTIVETo evaluate the accuracy of the UK Prospective Diabetes Study Outcomes Model (UKPDS-OM) in predicting clinical outcomes during the UKPDS posttrial monitoring (PTM) period.RESEARCH DESIGN AND METHODSAt trial end in 1997, the 4,031 surviving UKPDS patients, of the 5,102 originally enrolled in the study, returned to their usual care providers, with no attempts made to maintain them in their randomized therapy groups. PTM risk factor data were collected for 5 years and clinical outcome data for 10 years. The UKPDS-OM was used firstly to forecast likely progression of HbA1c, systolic blood pressure, total-to-HDL cholesterol ratio, and smoking status and secondly to estimate the likely first occurrence of seven major diabetes-related complications or death from any cause. Model predictions were compared against observed PTM data for risk factor time paths and survival probabilities for major diabetes complications.RESULTSUKPDS-OM–forecasted risk factor time paths were similar to those observed for HbA1c (up to 3 years) and total-to-HDL cholesterol ratio but underestimated for systolic blood pressure and smoking status. Predicted 10-year event probabilities were similar to those observed for blindness, ischemic heart disease, myocardial infarction, and renal failure but were higher for heart failure and death from any cause and lower for stroke and amputation.CONCLUSIONSThe UKPDS-OM has good predictive accuracy for two of four risk factor time paths and for 10-year clinical outcome probabilities with the exception of stroke, amputation, heart failure, and death from any cause. An updated version of the model incorporating PTM data is being developed.

The economic analyses of the UK prospective diabetes study

An economic analysis was not initially included in the study design of the UK Prospective Diabetes Study (UKPDS). However, data were collected throughout the study on hospital drugs and medications used and these were supplemented near the end of the study by cross-sectional surveys of non-inpatient healthcare use and quality of life. Evaluations of tight vs. less tight blood pressure control, intensive vs. less conventional blood glucose control and metformin showed that each was highly cost-effective and that all could be provided at modest total cost. Further analyses showed that amputations and stroke had particularly severe consequences for quality of life, and that amputations and non-fatal MI had high cost consequences. Finally, patient-level data were used to construct a diabetes outcomes model, which estimates the probability of longer-term complications from patient-specific risk factors and can be used in populations at different stages of diabetes progression. The economic analyses arising from the UKPDS have provided new evidence to clinicians, policymakers and researchers on the consequences of diabetes and the cost-effectiveness of interventions, thereby assisting the development of treatment guidelines and improved standards of care. The analyses also illustrated a number of methodological innovations. Finally, the UKPDS Outcomes Model is gaining widespread acceptance as a validated tool for long-term economic and clinical prediction in diabetes.

Reassessment of cardiovascular risk in diabetes

To review recent trials and reassess cardiovascular risk in people with diabetes. Recent clinical trials have tended to focus on lower-risk participants with diabetes who have had event rates considerably lower than participants in the early lipid trials. Statin studies have generally shown benefit in those without cardiovascular disease and at lower levels of low-density lipoprotein cholesterol. Results of fibrate and glitazone studies have been mixed; the question of benefit among statin-treated patients remains unanswered. Investigators failed to confirm the benefits of glucose control observed in the original Diabetes Mellitus, Insulin Glucose Infusion in Acute Myocardial Infarction study possibly due to study design issues. Epidemiologic follow-up of the Diabetes Control and Complications Trial showed sustained benefit of glucose control. A number of studies have shown the benefit of inpatient control of blood glucose. We await the results of ongoing blood pressure trials and other ongoing trials, which should provide much new information. A conceptual model of cardiovascular risk for people with diabetes mellitus based on the UK Prospective Diabetes Study outcomes model is discussed. The majority of adults with diabetes have a substantially greater risk compared with those without diabetes and a small percentage has very high risk. A minority of individuals may have considerably lower 10-year risk.