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Aims Risk assessment is essential in the prevention of cardiovascular disease. In patients with recent acute coronary syndrome (ACS) or coronary revascularization, risk prediction tools, like the European Society of Cardiology guideline recommended SMART-REACH risk score, are increasingly used to predict the risk of recurrent cardiovascular events enabling risk-based personalized prevention. However, little is known about the association between risk stratification and the social and healthcare costs at a population level. This study evaluated the associations between baseline SMART-REACH risk scores, long-term recurrent clinical events, cumulative costs, and post-index event LDL-C goal attainment in patients with recent ACS and/or revascularization. Methods and results This retrospective study used electronic health records and was conducted in the North Karelia region of Finland. The study cohort included all patients aged 45–85 admitted to a hospital for ACS or who underwent percutaneous coronary intervention or coronary artery bypass surgery between 1 January 2017 and 31 December 2021. Patients were divided into quintiles based on their baseline SMART-REACH risk scores to examine the associations between predicted 5-year scores and selected clinical and economic outcomes. In addition, simple age-based stratification was conducted as a sensitivity analysis. The observed 5-year cumulative incidence of recurrent events ranged from 20% in the lowest to 41% in the highest risk quintile, whereas the corresponding predicted risks ranged from 13% to 51%, and cumulative 5-year mean total costs per patient ranged from 15 827 to 46 182€, respectively. Both monitoring and attainment of low LDL-C values were suboptimal. Conclusion The use of the SMART-REACH quintiles as a population-level risk stratification tool successfully stratified patients into subgroups with different cumulative numbers of recurrent events and cumulative total costs. However, more research is needed to define clinically and economically optimal threshold values for a population-level stratification. [ABSTRACT FROM AUTHOR]

Background: Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. Methods: Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. Results: Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (−0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049). Conclusions: Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions. Highlights: The findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs). There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective To determine the cost effectiveness of strategies for preventing neonatal infection with group B streptococci and other bacteria in the UK and the value of further information from research. Design Use of a decision model to compare the cost effectiveness of prenatal testing for group B streptococcal infection (by polymerase chain reaction or culture), prepartum antibiotic treatment (intravenous penicillin or oral erythromycin), and vaccination during pregnancy (not yet available) for serious bacterial infection in early infancy across 12 maternal risk groups. Model parameters were estimated using multi-parameter evidence synthesis to incorporate all relevant data inputs. Data sources 32 systematic reviews were conducted: 14 integrated results from published studies, 24 involved analyses of primary datasets, and five included expert opinion. Main outcomes measures Healthcare costs per quality adjusted life year (QALY) gained. Results Current best practice (to treat only high risk women without prior testing for infection) and universal testing by culture or polymerase chain reaction were not cost effective options. Immediate extension of current best practice to treat all women with preterm and high risk term deliveries without testing (11% treated) would result in substantial net benefits. Currently, addition of culture testing for low risk term women, while treating all preterm and high risk term women, would be the most cost effective option (21% treated). If available in the future, vaccination combined with treating all preterm and high risk term women and no testing for low risk women would probably be marginally more cost effective and would limit antibiotic exposure to 11% of women. The value of information is highest (£67m) if vaccination is included as an option. Conclusions Extension of current best practice to treat all women with preterm and high risk term deliveries is readily achievable and would be beneficial. The choice between a [ABSTRACT FROM AUTHOR]

Meta-analysis allows the quantitative combination of results of multiple studies that address similar research questions. Traditional meta-analysis of studies involving a direct comparison of two treatment alternatives can be applied to estimate the overall relative efficacy of these two treatment alternatives. All treatment options relevant to practical treatment decisions are, however, not always compared directly against each other in clinical studies, but indirect comparison via a common comparator may be possible. To use all relevant evidence from both direct and indirect comparisons of treatment options, advanced methods of meta-analysis have been developed. These so-called network meta-analyses extend the traditional meta-analysis to cases where a network of studies enables different pair-wise direct and indirect comparisons between multiple treatment alternatives, thereby forming a network of relevant evidence.

Objective: To assess the cost effectiveness of primary angioplasty, compared with medical management with thrombolytic drugs, to achieve reperfusion after acute myocardial infarction (AMI) from the perspective of the UK NHS., Design: Bayesian evidence synthesis and decision analytic model., Methods: A systematic review was conducted and Bayesian statistical methods used to synthesise evidence from 22 randomised control trials. Resource utilisation was based on UK registry data, published literature and national databases, with unit costs taken from routine NHS sources and published literature., Main Outcome Measure: Costs from a health service perspective and outcomes measured as quality-adjusted life years (QALYs)., Results: For the base case, the incremental cost-effectiveness ratio of primary angioplasty was 9241 pounds sterling for each additional QALY, with a probability of being cost effective of 0.90 for a cost-effectiveness threshold of 20,000 pounds sterling. Results were sensitive to variations in the additional time required to initiate treatment with primary angioplasty., Conclusions: Primary angioplasty is cost effective for the treatment of AMI on the basis of threshold cost-effectiveness values used in the NHS and subject to a delay of up to about 80 minutes. These findings are mainly explained by the superior mortality benefit and the prevention of non-fatal outcomes associated with primary angioplasty for delays of up to this length.