Cost-effectiveness of applying high-sensitivity troponin I to a score for cardiovascular risk prediction in asymptomatic population.

INTRODUCTION: Risk stratification scores such as the European Systematic COronary Risk Evaluation (SCORE) are used to guide individuals on cardiovascular disease (CVD) prevention. Adding high-sensitivity troponin I (hsTnI) to such risk scores has the potential to improve accuracy of CVD prediction. We investigated how applying hsTnI in addition to SCORE may impact management, outcome, and cost-effectiveness. METHODS: Characteristics of 72,190 apparently healthy individuals from the Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project were included into a discrete-event simulation comparing two strategies for assessing CVD risk. The standard strategy reflecting current practice employed SCORE (SCORE); the alternative strategy involved adding hsTnI information for further stratifying SCORE risk categories (S-SCORE). Individuals were followed over ten years from baseline examination to CVD event, death or end of follow-up. The model tracked the occurrence of events and calculated direct costs of screening, prevention, and treatment from a European health system perspective. Cost-effectiveness was expressed as incremental cost-effectiveness ratio (ICER) in € per quality-adjusted life year (QALYs) gained during 10 years of follow-up. Outputs were validated against observed rates, and results were tested in deterministic and probabilistic sensitivity analyses. RESULTS: S-SCORE yielded a change in management for 10.0% of individuals, and a reduction in CVD events (4.85% vs. 5.38%, p<0.001) and mortality (6.80% vs. 7.04%, p<0.001). S-SCORE led to 23 (95%CI: 20-26) additional event-free years and 7 (95%CI: 5-9) additional QALYs per 1,000 subjects screened, and resulted in a relative risk reduction for CVD of 9.9% (95%CI: 7.3-13.5%) with a number needed to screen to prevent one event of 183 (95%CI: 172 to 203). S-SCORE increased costs per subject by 187€ (95%CI: 177 € to 196 €), leading to an ICER of 27,440€/QALY gained. Sensitivity analysis was performed with eligibility for treatment being the most sensitive. CONCLUSION: Adding a person's hsTnI value to SCORE can impact clinical decision making and eventually improves QALYs and is cost-effective compared to CVD prevention strategies using SCORE alone. Stratifying SCORE risk classes for hsTnI would likely offer cost-effective alternatives, particularly when targeting higher risk groups.

A comparison of international modelling methods to evaluate health economics of colorectal cancer screening: a systematic review protocol.

BACKGROUND: Colorectal cancer (CRC) is becoming an increasing health problem worldwide. However, with the help of screening, early diagnosis can reduce incidence and mortality rates. To elevate the economic burden that CRC can cause, cost-effectiveness analysis (CEA) can assist healthcare systems to make screening programmes more cost-effective and prolong survival for early-stage CRC patients. This review aims to identify different CEA modelling methods used internationally to evaluate health economics of CRC screening. METHODS: This review will systematically search electronic databases which include MEDLINE, EMBASE, Web of Science and Scopus. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidance recommendations will design the review, and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement will be used to extract relevant data from studies retrieved. Two reviewers will screen through the evidence using the PICOS (Participant, Intervention, Comparators, Outcomes, Study Design) framework, with a third reviewer to settle any disagreements. Once data extraction and quality assessment are complete, the results will be presented qualitatively and tabulated using the CHEERS checklist. DISCUSSION: The results obtained from the systematic review will highlight how different CRC screening programmes around the world utilise and incorporate health economic modelling methods to be more cost-effective. This information can help modellers develop CEA models which can be adapted to suit the specific screening programmes that they are evaluating. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022296113.

Extensions of Health Economic Evaluations in R for Microsoft Excel Users: A Tutorial for Incorporating Heterogeneity and Conducting Value of Information Analyses.

Advanced health economic analysis techniques currently performed in Microsoft Excel, such as incorporating heterogeneity, time-dependent transitions and a value of information analysis, can be easily transferred to R. Often the outputs of survival analyses (such as Weibull regression models) will estimate the impacts of correlated patient characteristics on patient outcomes, and are utilised directly as inputs for health economic decision models. This tutorial provides a step-by-step guide of how to conduct such analyses with a Markov model developed in R, and offers a comparison with established analyses performed in Microsoft Excel. This is done through the conversion of a previously published Microsoft Excel case study of a hip replacement surgery cost-effectiveness model. We hope that this paper can act as a facilitator in switching decision models from Microsoft Excel to R for complex health economic analyses, providing open-access code and data, suitable for future adaptation.

Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial.

A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

PURPOSE: Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN: Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. RESULTS: Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS: Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

A retrospective analysis of budget impact models submitted to the National Centre for Pharmacoeconomics in Ireland.

BACKGROUND: The National Centre for Pharmacoeconomics (NCPE) is a National HTA Agency in Ireland responsible for assessment of comparative clinical effectiveness, cost-effectiveness and potential budget impact of drugs on behalf of the Health Service Executive. This research aims to assess if the budget impact models submitted to the NCPE have accurate predicted utilisation, assess if the models are consistent in the parameters included, and determine if probabilistic sensitivity analyses would aid the characterization of uncertainty. METHODS: A retrospective analysis of budget impact models that had been submitted (January 2010-December 2017 inclusive) to the NCPE was performed. The input parameters in the budget impact model were recorded. For each drug, annual realised utilisation was compared with what had been predicted by the respective budget impact model. A probabilistic sensitivity analysis was also performed on each model. RESULTS: A total of 12 models were included; each model pertained to one drug for one indication. Of the 12 models, six underpredicted and six overpredicted the annual realised utilisation. There were a range of different parameters included in each of the budget impact models. A probabilistic sensitivity analysis did not improve the characterization of uncertainty. CONCLUSION: This research has demonstrated that budget impact models submitted to a national HTA agency have limited accuracy in predicting realised utilisation, and there is inconsistency among the parameters included. An electronic budget impact template for applicants has been developed, as a more systematic approach, for their submissions to the NCPE.

Health Technology Assessment of Drugs in Ireland: An Analysis of Timelines.

BACKGROUND: The National Centre for Pharmacoeconomics (NCPE) is commissioned by the Corporate Pharmaceutical Unit of the Health Service Executive (HSE-CPU) to assess the evidence for the comparative effectiveness and cost effectiveness of drugs for use by patients in Ireland. All new drugs are required to undergo rapid review (RR) appraisal by the NCPE. Following this, high-cost drugs or those predicted to have a significant budget impact then undergo a full health technology assessment (HTA) appraisal by the NCPE. OBJECTIVE: The objective of this paper was to quantify each stage of the timeline from marketing authorisation (MA) to completion of HTA appraisal and explore the association between submission features and the time to appraise RRs and HTAs. METHODS: All RRs and HTAs submitted to the NCPE (2015-2017 inclusive) were included in the dataset. Several dates and features of each submission were also listed for the purpose of analysis. RESULTS: A total of 158 RR and 49 HTA appraisals were completed by the NCPE between 2015 and 2017. The median time from MA to submission of RR was 59 days; the median time to appraise RR was 31.5 days. Only 49% of RRs appraised (2015-2017 inclusive) were recommended for HTA. The median time from RR decision to submission of HTA was 115 days, and the median time taken by the NCPE to appraise an HTA was 131 days. CONCLUSION: This paper identifies which stages of the process make a substantial contribution to the HTA timeline. Time to submission of RR varied widely between submissions, with only a few companies choosing to submit prior to an MA being granted. The average RR appraisal time was in line with the 4-week timeframe set out in a 2016 agreement. The time to appraise an HTA was longer than the 90-day timeframe.

Difficulties arising in reimbursement recommendations on new medicines due to inadequate reporting of population adjustment indirect comparison methods.

Indirect treatment comparisons are useful to estimate relative treatment effects when head-to-head studies are not conducted. Statisticians at the National Centre for Pharmacoeconomics Ireland (NCPE) and Scottish Medicines Consortium (SMC) assess the clinical and cost-effectiveness of new medicines as part of multidisciplinary teams. We describe some shared observations on areas where reporting of population-adjustment indirect comparison methods is causing uncertainty in our recommendations to decision-making committees when assessing reimbursement of medicines.

Effect of major lifestyle risk factors, independent and jointly, on life expectancy with and without cardiovascular disease: results from the Consortium on Health and Ageing Network of Cohorts in Europe and the United States (CHANCES).

Seldom have studies taken account of changes in lifestyle habits in the elderly, or investigated their impact on disease-free life expectancy (LE) and LE with cardiovascular disease (CVD). Using data on subjects aged 50+ years from three European cohorts (RCPH, ESTHER and Tromsø), we used multi-state Markov models to calculate the independent and joint effects of smoking, physical activity, obesity and alcohol consumption on LE with and without CVD. Men and women aged 50 years who have a favourable lifestyle (overweight but not obese, light/moderate drinker, non-smoker and participates in vigorous physical activity) lived between 7.4 (in Tromsø men) and 15.7 (in ESTHER women) years longer than those with an unfavourable lifestyle (overweight but not obese, light/moderate drinker, smoker and does not participate in physical activity). The greater part of the extra life years was in terms of "disease-free" years, though a healthy lifestyle was also associated with extra years lived after a CVD event. There are sizeable benefits to LE without CVD and also for survival after CVD onset when people favour a lifestyle characterized by salutary behaviours. Remaining a non-smoker yielded the greatest extra years in overall LE, when compared to the effects of routinely taking physical activity, being overweight but not obese, and drinking in moderation. The majority of the overall LE benefit is in disease free years. Therefore, it is important for policy makers and the public to know that prevention through maintaining a favourable lifestyle is "never too late".