The implications of competing risks and direct treatment disutility in cardiovascular disease and osteoporotic fracture: risk prediction and cost effectiveness analysis.

Background: Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates. Objectives: Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments. Design, participants, main outcome measures, data sources: Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25-84 years for cardiovascular disease and aged 30-99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making). Results: CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell's c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85-99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best-worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk. Limitations: Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated. Conclusions: Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making. Study registration: This study is registered as PROSPERO CRD42021249959. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Before offering a medicine to prevent disease, prescribers must expect it to do more good than harm. This balance depends on how likely it is that the person will develop the disease we want to prevent. But people might first die for other reasons. We call this a 'competing risk'. In most cases, the mathematical tools we use to estimate the chance of developing a disease do not account for competing risks. Another problem is that, when weighing up the benefits and harms of medicines, we ignore the hassle they cause patients, even when they do not cause side effects. We used two examples: statins to prevent heart disease and bisphosphonates to prevent fractures. First, we assessed if existing tools get predictions wrong by not accounting for competing risks. We found that they exaggerate the chance of heart attacks and strokes. However, the exaggeration is greatest among people who would clearly benefit from preventative treatment. So it may not change treatment decisions much. The fracture prediction tool we studied was very inaccurate, exaggerating risk among older people, but underestimating risk among younger people. We made a new fracture risk prediction tool. It gave better predictions, but it was still inaccurate for people aged > 85 years and those with several health problems. Next, we asked people questions designed to put a number on the hassle that statins and bisphosphonates cause. Most people thought that taking either is inconvenient, but the hassle factor for bisphosphonates is bigger. Finally, we updated the mathematical models that the National Institute for Health and Care Excellence used when recommending statins and bisphosphonates. We worked out if competing risks and the hassle of taking medicines make a difference to results. Statins remain a good idea for almost everyone, unless they really hate the idea of taking them. But bisphosphonates would do more harm than good for anyone who agrees with the hassle factor we found.

Quantifying the impact of taking medicines for primary prevention: a time-trade off study to elicit direct treatment disutility in the UK.

BACKGROUND: Direct treatment disutility (DTD) represents an individual's disutility associated with the inconvenience of taking medicine over a long period of time. OBJECTIVES: The main aim of this study was to elicit DTD values for taking a statin or a bisphosphonate for primary prevention. A secondary aim was to understand factors which influence DTD values. METHODS: Design: We used a cross-sectional study consisting of time-trade off exercises embedded within online surveys. Respondents were asked to compare a one-off pill ('Medicine A') assumed to have no inconvenience and a daily pill ('Medicine B') over 10 years (statins) or 5 years (bisphosphonates).Setting: Individuals from National Health Service (NHS) primary care and the general population were surveyed using an online panel company.Participants: Two types of participants were recruited. First, a purposive sample of patients with experience of taking a statin (n=260) or bisphosphonate (n=100) were recruited from an NHS sampling frame. Patients needed to be aged over 30, have experience of taking the medicine of interest and have no diagnosis of dementia or of using dementia drugs. Second, a demographically balanced sample of members of the public were recruited for statins (n=376) and bisphosphonates (n=359).Primary and secondary outcome measures: Primary outcome was mean DTD. Regression analysis explored factors which could influence DTD values. RESULTS: A total of 879 respondents were included for analysis (514 for statins and 365 for bisphosphonates). The majority of respondents reported a disutility associated with medicine use. Mean DTD for statins was 0.034 and for bisphosphonates 0.067, respectively. Respondent characteristics including age and sex did not influence DTD. Experience of bisphosphonate-use reduced reported disutilities. CONCLUSIONS: Statins and bisphosphonates have a quantifiable DTD. The size of estimated disutilities suggest they are likely to be important for cost-effectiveness, particularly in individuals at low-risk when treated for primary prevention.

Fluticasone Furoate/Umeclidinium/Vilanterol (FF/UMEC/VI) Triple Therapy Compared with Other Therapies for the Treatment of COPD: A Network Meta-Analysis.

INTRODUCTION: Randomized controlled trials (RCTs) comparing triple therapies (inhaled corticosteroid [ICS], long-acting ß2-agonist [LABA], and long-acting muscarinic antagonist [LAMA]) for the treatment of chronic obstructive pulmonary disease (COPD) are limited. This network meta-analysis (NMA) investigated the comparative efficacy of single-inhaler fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) versus any triple (ICS/LABA/LAMA) combinations and dual therapies in patients with COPD. METHODS: This NMA was conducted on the basis of a systematic literature review (SLR), which identified RCTs in adults aged at least 40 years with COPD. The RCTs compared different ICS/LABA/LAMA combinations or an ICS/LABA/LAMA combination with any dual therapy (ICS/LABA or LAMA/LABA). Outcomes of interest included forced expiratory volume in 1 s (FEV1), annualized rate of combined moderate and severe exacerbations, St George's Respiratory Questionnaire (SGRQ) total score and SGRQ responders, transition dyspnea index focal score, and rescue medication use (RMU). Analyses were conducted at 24 weeks (primary endpoint), and 12 and 52 weeks (if feasible). RESULTS: The NMA was informed by five trials reporting FEV1 at 24 weeks. FF/UMEC/VI was statistically significantly more effective at increasing trough FEV1 (based on change from baseline) than all triple comparators in the network apart from UMEC + FF/VI. The NMA was informed by 17 trials reporting moderate or severe exacerbation endpoints. FF/UMEC/VI demonstrated statistically significant improvements in annualized rate of combined moderate or severe exacerbations versus single-inhaler budesonide/glycopyrronium bromide/formoterol fumarate (BUD/GLY/FOR). At 24 weeks, the NMA was informed by five trials. FF/UMEC/VI showed statistically significant improvements in annualized rate of combined moderate or severe exacerbations versus UMEC + FF/VI and BUD/GLY/FOR. FF/UMEC/VI also demonstrated improvements in mean SGRQ score versus other triple therapy comparators at 24 weeks, and a significant reduction in RMU compared with BUD/GLY/FOR (160/18/9.6). CONCLUSION: The findings of this NMA suggest favorable efficacy with single-inhaler triple therapy comprising FF/UMEC/VI. Further analysis is required as additional evidence becomes available.

Preventing hepatitis B virus infection among healthcare professionals: potential impact of a 2-dose versus 3-dose vaccine.

The exposure risk to the highly infectious hepatitis B virus (HBV) is an established and recognizable hazard to healthcare professionals (HCPs). In the United States, implementing preemptive vaccination programs and safety procedures resulted in drastic reductions in HBV infections among HCPs; however, many HCPs remain unprotected and risk of exposure persists, especially among those first entering a healthcare system and undergoing professional training. First-generation HBV vaccines require completion of a 3-dose schedule over a 6-month interval for maximum immunogenicity. By comparison, HepB-CpG (HEPLISAV-B®) is a 2-dose HBV vaccine licensed in the United States in 2017, inducing rapid seroprotection over a 1-month interval and may represent a more effective strategy for combating HBV infection in US healthcare systems. In this modeling study, the health and economic impact of implementing an HBV vaccination strategy with HepB-CpG versus the 3-dose HBV vaccine (Engerix-B®) was evaluated among HCPs newly entering a healthcare system. The model used effective seroprotection rate, a real-world metric accounting for HCP vaccine compliance and seroprotection rates for different dosing regimens and considered current pricing for postexposure prophylaxis treatment. Compared with the 3-dose vaccine, HepB-CpG was anticipated to provide faster, increased protection against HBV infection among newly entered HCPs. In protecting a greater percentage of HCPs, HepB-CpG was also projected to substantially reduce the risk of HBV exposure. Accordingly, an economic analysis showed HepB-CpG vaccination would reduce costs of postexposure prophylaxis treatment compared with the 3-dose vaccine. Overall, HepB-CpG represents an effective therapeutic strategy against HBV infection for US healthcare systems.

Effect of competing mortality risks on predictive performance of the QRISK3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study.

BACKGROUND: Primary prevention of cardiovascular disease (CVD) is guided by risk-prediction tools, but these rarely account for the risk of dying from other conditions (ie, competing mortality risk). In England and Wales, the recommended risk-prediction tool is QRISK2, and a new version (QRISK3) has been derived and internally validated. We aimed to externally validate QRISK3 and to assess the effects of competing mortality risk on its predictive performance. METHODS: For this retrospective population cohort study, we used data from the Clinical Practice Research Datalink. We included patients aged 25-84 years with no previous history of CVD or statin treatment who were permanently registered with a primary care practice, had up-to-standard data for at least 1 year, and had linkage to Hospital Episode Statistics discharge and Office of National Statistics mortality data. We compared the QRISK3-predicted 10-year CVD risk with the observed 10-year risk in the whole population and in important subgroups of age and multimorbidity. QRISK3 discrimination and calibration were examined with and without accounting for competing risks. FINDINGS: Our study population included 1 484 597 women with 42 451 incident CVD events (4·9 cases per 1000 person-years of follow-up, 95% CI 4·89-4·99), and 1 420 176 men with 53 066 incident CVD events (6·7 cases per 1000 person-years, 6·66-6·78), with median follow-up of 5·0 years (IQR 1·9-9·2). Non-CVD death rose markedly with age (0·4% of women and 0·5% of men aged 25-44 years had a non-CVD death vs 20·1% of women and 19·6% of men aged 75-84 years). QRISK3 discrimination in the whole population was excellent (Harrell's C-statistic 0·865 in women and 0·834 in men) but was poor in older age groups (<0·65 in all subgroups aged 65 years or older). Ignoring competing risks, QRISK3 calibration in the whole population and in younger people was excellent, but there was significant over-prediction in older people. Accounting for competing risks, QRISK3 systematically over-predicted CVD risk, particularly in older people and in those with high multimorbidity. INTERPRETATION: QRISK3 performed well at the whole population level when ignoring competing mortality risk. The tool performed considerably less well in important subgroups, including older people and people with multimorbidity, and less well again after accounting for competing mortality risk. FUNDING: National Institute for Health Research.

Modeling the Economic Impact of Interventions for Older Populations with Multimorbidity: A Method of Linking Multiple Single-Disease Models.

Introduction. Individuals from older populations tend to have more than 1 health condition (multimorbidity). Current approaches to produce economic evidence for clinical guidelines using decision-analytic models typically use a single-disease approach, which may not appropriately reflect the competing risks within a population with multimorbidity. This study aims to demonstrate a proof-of-concept method of modeling multiple conditions in a single decision-analytic model to estimate the impact of multimorbidity on the cost-effectiveness of interventions. Methods. Multiple conditions were modeled within a single decision-analytic model by linking multiple single-disease models. Individual discrete event simulation models were developed to evaluate the cost-effectiveness of preventative interventions for a case study assuming a UK National Health Service perspective. The case study used 3 diseases (heart disease, Alzheimer's disease, and osteoporosis) that were combined within a single linked model. The linked model, with and without correlations between diseases incorporated, simulated the general population aged 45 years and older to compare results in terms of lifetime costs and quality-adjusted life-years (QALYs). Results. The estimated incremental costs and QALYs for health care interventions differed when 3 diseases were modeled simultaneously (£840; 0.234 QALYs) compared with aggregated results from 3 single-disease models (£408; 0.280QALYs). With correlations between diseases additionally incorporated, both absolute and incremental costs and QALY estimates changed in different directions, suggesting that the inclusion of correlations can alter model results. Discussion. Linking multiple single-disease models provides a methodological option for decision analysts who undertake research on populations with multimorbidity. It also has potential for wider applications in informing decisions on commissioning of health care services and long-term priority setting across diseases and health care programs through providing potentially more accurate estimations of the relative cost-effectiveness of interventions.

Replicating Health Economic Models: Firm Foundations or a House of Cards?

Health economic evaluation is a framework for the comparative analysis of the incremental health gains and costs associated with competing decision alternatives. The process of developing health economic models is usually complex, financially expensive and time-consuming. For these reasons, model development is sometimes based on previous model-based analyses; this endeavour is usually referred to as model replication. Such model replication activity may involve the comprehensive reproduction of an existing model or 'borrowing' all or part of a previously developed model structure. Generally speaking, the replication of an existing model may require substantially less effort than developing a new de novo model by bypassing, or undertaking in only a perfunctory manner, certain aspects of model development such as the development of a complete conceptual model and/or comprehensive literature searching for model parameters. A further motivation for model replication may be to draw on the credibility or prestige of previous analyses that have been published and/or used to inform decision making. The acceptability and appropriateness of replicating models depends on the decision-making context: there exists a trade-off between the 'savings' afforded by model replication and the potential 'costs' associated with reduced model credibility due to the omission of certain stages of model development. This paper provides an overview of the different levels of, and motivations for, replicating health economic models, and discusses the advantages, disadvantages and caveats associated with this type of modelling activity. Irrespective of whether replicated models should be considered appropriate or not, complete replicability is generally accepted as a desirable property of health economic models, as reflected in critical appraisal checklists and good practice guidelines. To this end, the feasibility of comprehensive model replication is explored empirically across a small number of recent case studies. Recommendations are put forward for improving reporting standards to enhance comprehensive model replicability.

Bayesian meta-analysis on medical devices: application to implantable cardioverter defibrillators.

OBJECTIVES: The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device. METHODS: In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference. RESULTS: Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60-0.89) (predictive relative risk [RR] = 0.77 [0.41-1.26]) and 0.83 (0.70-0.97) (predictive RR = 0.84 [0.55-1.22]) with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs. CONCLUSIONS: The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies.