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Objectives: To estimate the hospital costs among persons with obesity undergoing bariatric surgery compared with those without bariatric surgery. Methods: We analysed the UK Biobank Cohort study linked to Hospital Episode Statistics, for all adults with obesity undergoing bariatric surgery at National Health Service hospitals in England, Scotland, or Wales from 2006 to 2017. Surgery patients were matched with controls who did not have bariatric surgery using propensity scores approach with a ratio of up to 1-to-5 by year. Inverse probability of censoring weighting was used to correct for potential informative censoring. Annual and cumulative hospital costs were assessed for the surgery and control groups. Results: We identified 348 surgical patients (198 gastric bypass, 73 sleeve gastrectomy, 77 gastric banding) during the study period. In total, 324 surgical patients and 1506 matched control participants were included after propensity score matching. Mean 5-year cumulative hospital costs were €11,659 for 348 surgical patients. Compared with controls, surgical patients (n = 324) had significantly higher inpatient expenditures in the surgery year (€7289 vs. €2635, P Conclusions: Bariatric surgery significantly increased the inpatient costs in the surgery year, but was associated with decreased costs in the subsequent 4 years. However, any cost savings made up to 4 years were not enough to compensate for the initial surgical expenditure.

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.

Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomized studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost–effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomized comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.

Supplemental Digital Content is available in the text., The United Kingdom was among the first countries to introduce a salt reduction program in 2003 to reduce cardiovascular disease (CVD) incidence risk. Despite its initial success, the program has stalled recently and is yet to achieve national and international targets. We used age- and sex-stratified salt intake of 19 to 64 years old participants in the National Diet and Nutrition Surveys 2000 to 2018 and a multistate life table model to assess the effects of the voluntary dietary salt reduction program on premature CVD, quality-adjusted survival, and health care and social care costs in England. The program reduced population-level salt intake from 9.38 grams/day per adult (SE, 0.16) in 2000 to 8.38 grams/day per adult (SE, 0.17) in 2018. Compared with a scenario of persistent 2000 levels, assuming that the population-level salt intake is maintained at 2018 values, by 2050, the program is projected to avoid 83 140 (95% CI, 73 710–84 520) premature ischemic heart disease (IHD) cases and 110 730 (95% CI, 98 390–112 260) premature strokes, generating 542 850 (95% CI, 529 020–556 850) extra quality-adjusted life-years and £1640 million (95% CI, £1570–£1660) health care cost savings for the adult population of England. We also projected the gains of achieving the World Health Organization target of 5 grams/day per adult by 2030, which by 2050 would avert further 87 870 (95% CI, 82 050–88 470) premature IHD cases, 126 010 (95% CI, 118 600–126 460) premature strokes and achieve £1260 million (95% CI, £1180–£1260) extra health care savings compared with maintaining 2018 levels. Strengthening the salt reduction program to achieve further reductions in population salt intake and CVD burden should be a high priority.

BackgroundOver 100,000 primary knee arthroplasty operations are undertaken annually in the UK. Around 15–30% of patients do not report a good outcome. Better rehabilitation strategies may improve patient-reported outcomes.ObjectivesTo compare the outcomes from a traditional outpatient physiotherapy model with those from a home-based rehabilitation programme for people assessed as being at risk of a poor outcome after knee arthroplasty.DesignAn individually randomised, two-arm controlled trial with a blinded outcome assessment, a parallel health economic evaluation and a nested qualitative study.SettingThe trial took place in 14 NHS physiotherapy departments.ParticipantsPeople identified as being at high risk of a poor outcome after knee arthroplasty.InterventionsA multicomponent home-based rehabilitation package delivered by rehabilitation assistants with supervision from qualified therapists compared with usual-care outpatient physiotherapy.Main outcome measuresThe primary outcome was the Late Life Function and Disability Instrument at 12 months. Secondary outcomes were the Oxford Knee Score (a disease-specific measure of function); Knee injury and Osteoarthritis Outcome Score; Quality of Life subscale; Physical Activity Scale for the Elderly; EuroQol-5 Dimensions, five-level version; and physical function assessed using the Figure-of-8 Walk Test, 30-Second Chair Stand Test and Single Leg Stance. Data on the use of health-care services, time off work and informal care were collected using participant diaries.ResultsIn total, 621 participants were randomised. A total of 309 participants were assigned to the COmmunity based Rehabilitation after Knee Arthroplasty (CORKA) home-based rehabilitation programme, receiving a median of five treatment sessions (interquartile range 4–7 sessions). A total of 312 participants were assigned to usual care, receiving a median of four sessions (interquartile range 2–6 sessions). The primary outcome, Late Life Function and Disability Instrument function total score at 12 months, was collected for 279 participants (89%) in the home-based CORKA group and 287 participants (92%) in the usual-care group. No clinically or statistically significant difference was found between the groups (intention-to-treat adjusted difference 0.49 points, 95% confidence interval –0.89 to 1.88 points;p = 0.48). There were no statistically significant differences between the groups in any of the patient-reported or physical secondary outcome measures at 6 or 12 months post randomisation. The health economic analysis found that the CORKA intervention was cheaper to provide than usual care (£66 less per participant). Total societal costs (combining health-care costs and other costs) were lower for the CORKA intervention than usual care (£316 less per participant). Adopting a societal perspective, CORKA had a 75% probability of being cost-effective at a threshold of £30,000 per quality-adjusted life-year. Adopting the narrower health and social care perspective, CORKA had a 43% probability of being cost-effective at the same threshold.LimitationsThe interventions were of short duration and were set within current commissioning guidance for UK physiotherapy. Participants and treating therapists could not be blinded.ConclusionsThis randomised controlled trial found no important differences in outcomes when post-arthroplasty rehabilitation was delivered using a home-based, rehabilitation assistant-delivered rehabilitation package or a traditional outpatient model. However, the health economic evaluation found that when adopting a societal perspective, the CORKA home-based intervention was cost-saving and more effective than, and thus dominant over, usual care, owing to reduced time away from paid employment for this group. Further research could look at identifying the risk of poor outcome and further evaluation of a cost-effective treatment, including the workforce model to deliver it.Trial registrationCurrent Controlled Trials ISRCTN13517704.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 24, No. 65. See the NIHR Journals Library website for further project information.

ObjectivesTo estimate the impact of the COVID-19 pandemic on cardiovascular disease (CVD) and CVD management using routinely collected medication data as a proxy.DesignDescriptive and interrupted time series analysis using anonymised individual-level population-scale data for 1.32 billion records of dispensed CVD medications across 15.8 million individuals in England, Scotland and Wales.SettingCommunity dispensed CVD medications with 100% coverage from England, Scotland and Wales, plus primary care prescribed CVD medications from England (including 98% English general practices).Participants15.8 million individuals aged 18+ years alive on 1stApril 2018 dispensed at least one CVD medicine in a year from England, Scotland and Wales.Main outcome measuresMonthly counts, percent annual change (1stApril 2018 to 31stJuly 2021) and annual rates (1stMarch 2018 to 28thFebruary 2021) of medicines dispensed by CVD/ CVD risk factor; prevalent and incident use.ResultsYear-on-year change in dispensed CVD medicines by month were observed, with notable uplifts ahead of the first (11.8% higher in March 2020) but not subsequent national lockdowns. Using hypertension as one example of the indirect impact of the pandemic, we observed 491,203 fewer individuals initiated antihypertensive treatment across England, Scotland and Wales during the period March 2020 to end May 2021 than would have been expected compared to 2019. We estimated that this missed antihypertension treatment could result in 13,659 additional CVD events should individuals remain untreated, including 2,281 additional myocardial infarctions (MIs) and 3,474 additional strokes. Incident use of lipid-lowering medicines decreased by an average 14,793 per month in early 2021 compared with the equivalent months prior to the pandemic in 2019. In contrast, the use of incident medicines to treat type-2 diabetes (T2DM) increased by approximately 1,642 patients per month.ConclusionsManagement of key CVD risk factors as proxied by incident use of CVD medicines has not returned to pre-pandemic levels in the UK. Novel methods to identify and treat individuals who have missed treatment are urgently required to avoid large numbers of additional future CVD events, further adding indirect cost of the COVID-19 pandemic.

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

IntroductionThe National Institute for Health and Care Excellence (NICE) intends to increasingly use real-world evidence in developing guidance. To increase trust in such evidence, NICE has developed a framework for developing and assessing real-world evidence studies, including understanding the value of the selected data source for the decision problem.MethodsStarting with published high-quality studies about data quality, we developed a conceptual model of the elements needed to understand the quality of a data source. Results from a literature search were then mapped to the model. We used this to design a structured reporting tool, the data Suitability Assessment Tool (dataSAT), and tested it in several cases studies. Additionally, we engaged with internal and external stakeholders to obtain feedback on the tool and revised it accordingly.ResultsDataSAT covers provenance of the data, assessment of data quality, and the data’s relevance to the research question. For data provenance, information is requested about the data source independent of the study’s interests, including the purpose, setting, dates of operation, funding, data specification, and management and quality assurance plans for the data sources. Data quality is covered by quantitively assessing the completeness and accuracy of the following key study elements to inform critical appraisal of the study: population inclusion and exclusion criteria; intervention; comparator; and outcomes and key covariates. The findings on data sources and data quality are then interpreted in terms of relevance to the decision problem. This includes relevance to the population in the United Kingdom, the treatment pathway and care setting, the availability of key study elements, time-related factors such as length of follow up, and the effects of sample size and missing data on the validity of findings.ConclusionsDataSAT allows summary information on source data, including quality and relevance, to be reported in a structured manner, enabling decision makers to better understand how the data influence the robustness of analyses used in health technology assessment. This helps increase trust in the use of real-world evidence.

IntroductionRecent reviews have shown that many real-world evidence (RWE) studies suffer from avoidable methodological flaws. Meanwhile, the National Institute for Health and Care Excellence (NICE) is seeing an increase in RWE submissions in Health Technology Appraisals and is keen to support the use of this evidence. However, limited guidance exists for the development and assessment of RWE, risking both missed opportunities for unbiased evidence generation and inconsistent decision making based on that evidence. As part of its RWE framework, NICE has developed methods guidance to provide clear expectations for the conduct and reporting of non-randomized comparative effects studies using real world data.MethodsA conceptual model and draft framework were developed based on established international best practices in RWE and observational research. This was refined with focused literature searches, for example, on the use of external control arm studies. We then engaged with external stakeholders to incorporate their feedback and develop case studies. A reporting template was developed and tested on multiple use cases.Results & ConclusionsThe guidance stresses the central importance of a target trial approach to study design, e.g., adopting an active comparator, new user design, where possible. Target trial emulation is a useful tool to improve the quality and transparency of RWE studies, helping to overcome selection and confounding biases. Various other study design and analytical approaches are outlined for addressing confounding bias and biases due to missing data, measurement error, or misclassification, which are common challenges in RWE. Alongside traditional approaches to sensitivity analysis, the framework promotes quantitative bias analyses which includes a range of methods to assess and communicate the potential impact of remaining bias to study findings by quantifying the direction, magnitude, and uncertainty of bias. A reporting template, based on common methodological pitfalls, is provided to help evidence developers consider key areas of bias in their work and to inform reviewers of any approaches used to investigate or resolve these.

IntroductionThe National Institute for Health and Care Excellence (NICE) is determined to make better use of health and social care data in the development of its guidance. Real-world data (RWD) has the potential to significantly improve our understanding of the value of new and existing health and social care interventions. RWD is already widely used to characterize populations, interventions, and outcomes and to populate economic models, but its use in estimating the effects of interventions remains limited, especially for medicines. Key barriers to its greater use in this context include limited transparency around studies, sometimes a lack of confidence in their integrity, and methodological concerns around how studies have addressed major sources of potential bias.MethodsThis abstract focuses on the real-world evidence (RWE) framework developed by NICE to support its ambitions. The framework was developed in an iterative way based on: reviews of best practice approaches to the conduct and assessment of real-world evidence studies; case studies; and workshops with key external stakeholders. The initial version of this living framework focuses on de novo RWE studies using individual patient data.ResultsThe RWE framework consists of an overarching research governance framework which describes expectations around the planning, conduct, and reporting of RWE studies across uses of real-world data. Uses are categorized by risk according to their importance to decision-making, the impact of decisions on patient and system outcomes, and their complexity as proxied by risk of bias. Studies of the effects of interventions on patient health and system outcomes are considered the highest risk. The research governance framework is supported by a tool to aid assessment of data suitability for its intended application, and detailed guidance on the conduct and reporting of comparative effect studies using RWD, following the target trial approach.ConclusionsThe RWE Framework underpins NICE’s ambitions to make better use of RWD in its guidance and is intended to improve the quality and utility of RWE studies submitted to NICE enabling more consistent and appropriate evaluation.

ObjectiveTo compare real-world effectiveness and safety of direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (AFib) for prevention of stroke.Study design and settingA comparative cohort study in UK general practice data from The Health Improvement Network database.Participants and interventionsBefore matching, 5655 patients ≥18 years with nonvalvular AFib who initiated at least one DOAC between 1 July 2014 and 31 December 2020 were included. DOACs of interest included apixaban, rivaroxaban, edoxaban and dabigatran, with the primary comparison between apixaban and rivaroxaban. Initiators of DOACs were defined as new users with no record of prescription for any DOAC during 12 months before index date.Primary and secondary outcome measuresThe primary outcome was stroke (ischaemic or haemorrhagic). Secondary outcomes included the occurrence of all-cause mortality, myocardial infarction (MI), transient ischaemic attacks (TIA), major bleeding events and a composite angina/MI/stroke (AMS) endpoint.ResultsCompared with rivaroxaban, patients initiating apixaban showed similar rates of stroke (HR: 0.93; 95% CI 0.64 to 1.34), all-cause mortality (HR: 1.03; 95% CI 0.87 to 1.22), MI (HR: 0.95; 95% CI 0.54 to 1.68), TIA (HR: 1.03; 95% CI 0.61 to 1.72) and AMS (HR: 0.96; 95% CI 0.72 to 1.27). Apixaban initiators showed lower rates of major bleeding events (HR: 0.60; 95% CI 0.47 to 0.75).ConclusionsAmong patients with nonvalvular AFib, apixaban was as effective as rivaroxaban in reducing rate of stroke and safer in terms of major bleeding episodes. This head-to-head comparison supports conclusions drawn from indirect comparisons of DOAC trials against warfarin and demonstrates the potential for real-world evidence to fill evidence gaps and reduce uncertainty in both health technology assessment decision-making and clinical guideline development.

Health technology assessment (HTA) is increasingly informed by nonrandomized studies, but there is limited guidance from HTA bodies on expectations around evidence quality and study conduct. We developed recommendations to support the appropriate use of such evidence based on a pragmatic literature review and a workshop involving 16 experts from eight countries as part of the EU’s Horizon-2020 IMPACT-HTA program (work package six). To ensure HTA processes remain rigorous and robust, HTA bodies should demand clear, extensive and structured reporting of nonrandomized studies, including an in-depth assessment of the risk of bias. In recognition of the additional uncertainty imparted by nonrandomized designs in estimates of treatment effects, HTA bodies should strengthen early scientific advice and engage in collaborative efforts to improve use of real-world data.

Transparency in health economic decision modelling is important for engendering confidence in the models and in the reliability of model-based cost-effectiveness analyses. The Mount Hood Diabetes Challenge Network has taken a lead in promoting transparency through validation with biennial conferences in which diabetes modelling groups meet to compare simulated outcomes of pre-specified scenarios often based on the results of pivotal clinical trials. Model registration is a potential method for promoting transparency, while also reducing the duplication of effort. An important network initiative is the ongoing construction of a diabetes model registry (https://www.mthooddiabeteschallenge.com). Following the 2012 International Society for Pharmacoeconomics and Outcomes Research and the Society of Medical Decision Making (ISPOR-SMDM) guidelines, we recommend that modelling groups provide technical and non-technical documentation sufficient to enable model reproduction, but not necessarily provide the model code. We also request that modelling groups upload documentation on the methods and outcomes of validation efforts, and run reference case simulations so that model outcomes can be compared. In this paper, we discuss conflicting definitions of transparency in health economic modelling, and describe the ongoing development of a registry of economic models for diabetes through the Mount Hood Diabetes Challenge Network, its objectives and potential further developments, and highlight the challenges in its construction and maintenance. The support of key stakeholders such as decision-making bodies and journals is key to ensuring the success of this and other registries. In the absence of public funding, the development of a network of modellers is of huge value in enhancing transparency, whether through registries or other means.

Statin-based treatments reduce cardiovascular disease (CVD) risk in patients with non-dialysis chronic kidney disease (CKD), but it is unclear which regimen is the most cost-effective. We used the Study of Heart and Renal Protection (SHARP) CKD-CVD policy model to evaluate the effect of statins and ezetimibe on quality-adjusted life years (QALYs) and health care costs in the United States (US) and the United Kingdom (UK). Net costs below $100,000/QALY (US) or 20,000 pound/QALY (UK) were considered cost-effective. We investigated statin regimens with or without ezetimibe 10 mg. Treatment effects on cardiovascular risk were estimated per 1-mmol/L reduction in low-density lipoprotein (LDL) cholesterol as reported in the Cholesterol Treatment Trialists' Collaboration meta-analysis, and reductions in LDL cholesterol were estimated for each statin/ezetimibe regimen. In the US, atorvastatin 40 mg ($0.103/day as of January 2019) increased life expectancy by 0.23 to 0.31 QALYs in non-dialysis patients with stages 3B to 5 CKD, at a net cost of $20,300 to $78,200/QALY. Adding ezetimibe 10 mg ($0.203/day) increased life expectancy by an additional 0.05 to 0.07 QALYs, at a net cost of $43,600 to $91,500/QALY. The cost-effectiveness findings and policy implications in the UK were similar. In summary, in patients with non-dialysis-dependent CKD, the evidence suggests that statin/ezetimibe combination therapy is a cost-effective treatment to reduce the risk of CVD.

© 2019 The Authors Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS) Objective: The study objective was to estimate the cost-effectiveness of a commercially provided low-energy total diet replacement (TDR) program compared with nurse-led behavioral support. Methods: A multistate life table model and the weight reduction observed in a randomized controlled trial were used to evaluate the quality-adjusted life-years and direct health care costs (in United Kingdom 2017 prices) over a lifetime with TDR versus nurse-led support in adults who had obesity, assuming that (i) weight returns to baseline over 5 years and (ii) a 1-kg weight loss is maintained after 5 years following TDR. Results: The per-person costs of the TDR and nurse-led programs were £796 and £34, respectively. The incremental cost-effectiveness ratio of TDR was £12,955 (95% CI: £8,082-£17,827) assuming that all weight lost is regained and £3,203 (£2,580-£3,825) assuming that a 1-kg weight loss is maintained after 5 years. TDR was estimated to be more cost-effective (i.e., lower incremental cost-effectiveness ratios) in older adults and those with a higher BMI, with little difference by gender. Conclusions: At current retail prices and with plausible long-term weight regain trajectories, TDR is projected to be cost-effective in adults with obesity and could be considered as an option to treat obesity in routine health care settings.

BACKGROUND The PD MED study reported small but persistent benefits in patient-rated mobility scores and quality of life from initiating therapy with levodopa compared with levodopa-sparing therapies in early Parkinson's disease (PD). OBJECTIVES The objective was to estimate the cost-effectiveness of levodopa-sparing therapy (dopamine agonists or monoamine oxidase type B inhibitors compared with levodopa alone. METHODS PD MED is a pragmatic, open-label randomized, controlled trial in which patients newly diagnosed with PD were randomly assigned between levodopa-sparing therapy (dopamine agonists or monoamine oxidase type B inhibitors ) and levodopa alone. Mean quality-adjusted life-years and costs were calculated for each participant. Differences in mean quality-adjusted life-years and costs between levodopa and levodopa-sparing therapies and between dopamine agonists and monoamine oxidase type B inhibitors were estimated using linear regression. RESULTS Over a mean observation period of 4 years, levodopa was associated with significantly higher quality-adjusted life-years (difference, 0.18; 95% CI, 0.05-0.30; P

Background Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis. Methods In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I² value was less than 0·4. Findings The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Selfcontrolled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12–2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22–3·95]), chest pain or angina (1·15 [1·05–1·26]), and heart failure (1·22 [1·02–1·45]). Interpretation Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit–risk trade-off when counselling those on hydroxychloroquine treatment.

Background To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza. Methods We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014–19. Results 6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use. Conclusions Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19., Detailed knowledge of the characteristics of COVID-19 patients helps with public health planning. Here, the authors use routinely-collected data from seven databases in three countries to describe the characteristics of >30,000 patients admitted with COVID-19 and compare them with those admitted for influenza in previous years.

BackgroundHydroxychloroquine has recently received Emergency Use Authorization by the FDA and is currently prescribed in combination with azithromycin for COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin.MethodsNew user cohort studies were conducted including 16 severe adverse events (SAEs). Rheumatoid arthritis patients aged 18+ and initiating hydroxychloroquine were compared to those initiating sulfasalazine and followed up over 30 days. Self-controlled case series (SCCS) were conducted to further establish safety in wider populations. Separately, SAEs associated with hydroxychloroquine- azithromycin (compared to hydroxychloroquine-amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, Netherlands, Spain, UK, and USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (CalHRs) according to drug use. Estimates were pooled where I2ResultsOverall, 956,374 and 310,350 users of hydroxychloroquine and sulfasalazine, and 323,122 and 351,956 users of hydroxychloroquine-azithromycin and hydroxychloroquine-amoxicillin were included. No excess risk of SAEs was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. SCCS confirmed these findings. However, when azithromycin was added to hydroxychloroquine, we observed an increased risk of 30-day cardiovascular mortality (CalHR2.19 [1.22- 3.94]), chest pain/angina (CalHR 1.15 [95% CI 1.05-1.26]), and heart failure (CalHR 1.22 [95% CI 1.02- 1.45])ConclusionsShort-term hydroxychloroquine treatment is safe, but addition of azithromycin may induce heart failure and cardiovascular mortality, potentially due to synergistic effects on QT length. We call for caution if such combination is to be used in the management of Covid-19.Trial registration numberRegistered with EU PAS; Reference number EUPAS34497 (http://www.encepp.eu/encepp/viewResource.htm?id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine.Funding sourcesThis research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and Senior Research Fellowship (DPA), US National Institutes of Health, Janssen Research & Development, IQVIA, and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [grant number: HI16C0992]. Personal funding included Versus Arthritis [21605] (JL), MRC-DTP [MR/K501256/1] (JL), MRC and FAME (APU). The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. No funders had a direct role in this study. The views and opinions expressed are those of the authors and do not necessarily reflect those of the Clinician Scientist Award programme, NIHR, NHS or the Department of Health, England.

Background Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis. Methods In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I2 value was less than 0·4. Findings The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12–2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22–3·95]), chest pain or angina (1·15 [1·05–1·26]), and heart failure (1·22 [1·02–1·45]). Interpretation Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit–risk trade-off when counselling those on hydroxychloroquine treatment. Funding National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.

Objective To present a long-term policy model of cardiovascular disease (CVD) in moderate-to-advanced chronic kidney disease (CKD). Methods A Markov model with transitions between CKD stages (3B, 4, 5, on dialysis, with kidney transplant) and cardiovascular events (major atherosclerotic events, haemorrhagic stroke, vascular death) was developed with individualised CKD and CVD risks estimated using the 5 years’ follow-up data of the 9270 patients with moderate-to-severe CKD in the Study of Heart and Renal Protection (SHARP) and multivariate parametric survival analysis. The model was assessed in three further CKD cohorts and compared with currently used risk scores. Results Higher age, previous cardiovascular events and advanced CKD were the main contributors to increased individual disease risks. CKD and CVD risks predicted by the state-transition model corresponded well to risks observed in SHARP and external cohorts. The model’s predictions of vascular risk and progression to end-stage renal disease were better than, or comparable to, those produced by other risk scores. As an illustration, at age 60–69 years, projected survival for SHARP participants in CKD stage 3B was 13.5 years (10.6 quality-adjusted life years (QALYs)) in men and 14.8 years (10.7 QALYs) in women. Corresponding projections for participants on dialysis were 7.5 (5.6 QALYs) and 7.8 years (5.4 QALYs). A non-fatal major atherosclerotic event reduced life expectancy by about 2 years in stage 3B and by 1 year in dialysis. Conclusions The SHARP CKD-CVD model is a novel resource for evaluating health outcomes and cost-effectiveness of interventions in CKD. Trial registration number NCT00125593 and ISRCTN54137607; Post-results.

Summary Excess weight is associated with increased total healthcare costs, but it is less well known how the associations between excess weight and costs vary across different types of healthcare service. We reviewed studies using individual participant data to estimate associations between body mass index and healthcare costs, and summarized how annual healthcare costs for overweight (body mass index 25 to

Objective To estimate the effect of BMI on cause‐specific hospital admissions and costs in men and women is not well understood, and this study’s aim is to address this. Methods For 451,320 men and women aged 40 years or older recruited into the UK Biobank, followed up for 6 years on average, this study estimated annual rates and costs (at 2016 UK prices) of hospital admissions, overall and by diagnostic category (using International Classification of Diseases, Tenth Revision chapters), in relation to BMI. Results Among those who were never smokers, a 2‐kg/m2 higher BMI (above 20 kg/m2) was associated with a 6.2% (99% CI: 5.3% to 7.2%) higher admission rate and an 8.6% (99% CI: 7.3% to 10.0%) higher annual cost in men and with a 5.7% (99% CI: 4.9% to 6.6%) higher admission rate and an 8.4% (99% CI: 7.5% to 9.3%) higher annual cost in women. Higher BMI was associated with higher admission rates and costs for many types of health conditions, particularly for endocrine, skin, and musculoskeletal disorders in both men and women and for circulatory diseases in men. Conclusions BMI beyond healthy weight is strongly associated with higher annual rates of hospital admission and higher costs in both men and women across a wide range of health conditions.

Summary Background Late-stage isolated medial knee osteoarthritis can be treated with total knee replacement (TKR) or partial knee replacement (PKR). There is high variation in treatment choice and little robust evidence to guide selection. The Total or Partial Knee Arthroplasty Trial (TOPKAT) therefore aims to assess the clinical effectiveness and cost-effectiveness of TKR versus PKR in patients with medial compartment osteoarthritis of the knee, and this represents an analysis of the main endpoints at 5 years. Methods Our multicentre, pragmatic randomised controlled trial was done at 27 UK sites. We used a combined expertise-based and equipoise-based approach, in which patients with isolated osteoarthritis of the medial compartment of the knee and who satisfied general requirements for a medial PKR were randomly assigned (1:1) to receive PKR or TKR by surgeons who were either expert in and willing to perform both surgeries or by a surgeon with particular expertise in the allocated procedure. The primary endpoint was the Oxford Knee Score (OKS) 5 years after randomisation in all patients assigned to groups. Health-care costs (in UK 2017 prices) and cost-effectiveness were also assessed. This trial is registered with ISRCTN (ISRCTN03013488) and ClinicalTrials.gov ( NCT01352247 ). Findings Between Jan 18, 2010, and Sept 30, 2013, we assessed 962 patients for their eligibility, of whom 431 (45%) patients were excluded (121 [13%] patients did not meet the inclusion criteria and 310 [32%] patients declined to participate) and 528 (55%) patients were randomly assigned to groups. 94% of participants responded to the follow-up survey 5 years after their operation. At the 5-year follow-up, we found no difference in OKS between groups (mean difference 1·04, 95% CI −0·42 to 2·50; p=0·159). In our within-trial cost-effectiveness analysis, we found that PKR was more effective (0·240 additional quality-adjusted life-years, 95% CI 0·046 to 0·434) and less expensive (−£910, 95% CI −1503 to −317) than TKR during the 5 years of follow-up. This finding was a result of slightly better outcomes, lower costs of surgery, and lower follow-up health-care costs with PKR than TKR. Interpretation Both TKR and PKR are effective, offer similar clinical outcomes, and result in a similar incidence of re-operations and complications. Based on our clinical findings, and results regarding the lower costs and better cost-effectiveness with PKR during the 5-year study period, we suggest that PKR should be considered the first choice for patients with late-stage isolated medial compartment osteoarthritis. Funding National Institute for Health Research Health Technology Assessment Programme.

Aim In Type 2 diabetes, there is no clear understanding of how people perceive their risk of experiencing diabetes‐related complications. To address this issue, we undertook an evidence‐based synthesis of how people with Type 2 diabetes perceive their risk of complications. Methods We performed a systematic search of nine electronic databases for peer‐reviewed articles published on or before 1 March 2016. Data from 18 studies reporting lay perceptions of risks for complications in Type 2 diabetes populations were included. Publication year ranged between 2002 and 2014. Results Methods used to assess risk perceptions were heterogeneous, ranging from questionnaires measuring the accuracy of perceived risks to semi‐structured and focus group interviews. We found evidence of low risk awareness in most dimensions of risk perceptions measured and the existence of optimistic bias. Conclusions Perceptions were generally biased and varied according to the dimension of risk measured, the subpopulation concerned and the type of complications considered. Future work is needed to identify the best practical ways of correcting for biased risk perceptions so as to encourage self‐care behaviours and treatment adherence., What's new? This systematic review is the first to provide an evidence‐based synthesis of risk perceptions for diabetes‐related complications in Type 2 diabetes populations.The review highlights the large heterogeneity in study designs and methods used to assess risk perceptions.Risk perceptions of people with Type 2 diabetes are generally biased, characterized by low risk awareness with most measurement methods and contaminated by optimistic bias.The specific biases identified in this review will help design more effective risk communication interventions.

IntroductionDifferences between healthcare datasets in structure, content, and coding systems are widely recognized as significant barriers to generating robust evidence for regulatory and medical decision making. As a result, there is a growing interest in using common data models embedded within large data networks. By standardizing the structure, contents, and semantics of disparate healthcare databases, common data models like the Observational and Medical Outcomes Partnerships common data model (OMOP-CDM) enable multidatabase studies to be undertaken at speed and in a transparent way. To date, little attention has been given to their potential role in health technology assessment (HTA).MethodsWe identify the uses of observational data in generating evidence in HTA, some common analytical challenges faced in their estimation, and the infrastructural, technical, and data reusability constraints that limit its wider use. We discuss where and how the OMOP-CDM could overcome these barriers in relation to different types of evidence requirements.ResultsThe OMOP-CDM increases the interoperability of otherwise disparate datasets, allowing reliable evidence to be generated from multidatabase studies at speed and transparently. The current analytical tools are best suited for clinical characterization and population-level effect estimation. Further developments to these tools are required to support analyses common in HTA like parametric survival modeling. Differences in costing methods as well as the structure of healthcare delivery between countries may limit the feasibility and value of standardization.ConclusionsThe OMOP-CDM has the potential to support reliable and timely evidence generation in HTA. The analytical tools should be further developed to support common HTA use cases.

Background Excess weight is associated with poor health and increased healthcare costs. There are no reliable data describing the association between BMI and the use and costs of primary care services in the United Kingdom. Methods Among 69,440 participants in the Million Women Study with primary care records in the Clinical Practice Research Datalink between April 2006 (mean age 64 years) and March 2014, the annual rates and costs of their primary care consultations, prescription medications, and diagnostic and monitoring tests were estimated in relation to their body-mass index (BMI) at recruitment in 1996-2001 (mean age 56 years). Associations of BMI with annual costs were projected to all women in England aged 55-79 years in 2013. Results Over an average follow-up of 6.0 years, annual rates and mean costs were lowest for women with a BMI of 20 to prescription medication costs, of which 27% were for diabetes drugs, 19% for circulatory system drugs, and 13% for analgesics. Conclusions Excess body weight is associated with higher use and costs of primary care services among women in England. Reducing the prevalence of excess weight could reduce pressures on primary care.

The study objective was to estimate the cost-effectiveness of a commercially provided low-energy total diet replacement (TDR) program compared with nurse-led behavioral support.A multistate life table model and the weight reduction observed in a randomized controlled trial were used to evaluate the quality-adjusted life-years and direct health care costs (in United Kingdom 2017 prices) over a lifetime with TDR versus nurse-led support in adults who had obesity, assuming that (i) weight returns to baseline over 5 years and (ii) a 1-kg weight loss is maintained after 5 years following TDR.The per-person costs of the TDR and nurse-led programs were £796 and £34, respectively. The incremental cost-effectiveness ratio of TDR was £12,955 (95% CI: £8,082-£17,827) assuming that all weight lost is regained and £3,203 (£2,580-£3,825) assuming that a 1-kg weight loss is maintained after 5 years. TDR was estimated to be more cost-effective (i.e., lower incremental cost-effectiveness ratios) in older adults and those with a higher BMI, with little difference by gender.At current retail prices and with plausible long-term weight regain trajectories, TDR is projected to be cost-effective in adults with obesity and could be considered as an option to treat obesity in routine health care settings.

Summary Background Excess weight is associated with poor health and increased health-care costs. However, a detailed understanding of the effects of excess weight on total hospital costs and costs for different health conditions is needed. Methods Women in England aged 50–64 years were recruited into the prospective Million Women Study cohort in 1996–2001 through 60 NHS breast cancer screening centres. Participants were followed up and annual hospital costs and admission rates were estimated for April 1, 2006, to March 31, 2011, in relation to body-mass index (BMI) at recruitment, overall and for categories of health conditions defined by the International Classification of Diseases 10th revision chapter of the primary diagnosis at admission. Associations of BMI with hospital costs were projected to the 2013 population of women aged 55–79 years in England. Findings 1 093 866 women who provided information on height and weight, had a BMI of at least 18·5 kg/m 2 , and had no previous cancer at recruitment, were followed up for an average of 4·9 years from April 1, 2006 (12·3 years from recruitment), during which time 1·84 million hospital admissions were recorded. Annual hospital costs were lowest for women with a BMI of 20·0 kg/m 2 to less than 22·5 kg/m 2 (£567 per woman per year, 99% CI 556–577). Every 2 kg/m 2 increase in BMI above 20 kg/m 2 was associated with a 7·4% (7·1–7·6) increase in annual hospital costs. Excess weight was associated with increased costs for all diagnostic categories, except respiratory conditions and fractures. £662 million (14·6%) of the estimated £4·5 billion of total annual hospital costs among all women aged 55–79 years in England was attributed to excess weight (BMI ≥25 kg/m 2 ), of which £517 million (78%) arose from hospital admissions with procedures. £258 million (39%) of the costs attributed to excess weight were due to musculoskeletal admissions, mainly for knee replacement surgeries. Interpretation Excess body weight is associated with increased hospital costs for middle-aged and older women in England across a broad range of conditions, especially knee replacement surgery and diabetes. These results provide reliable up-to-date estimates of the health-care costs of excess weight and emphasise the need for investment to tackle this public health issue. Funding Cancer Research UK; Medical Research Council; National Institute for Health Research.

Excess weight is associated with increased total healthcare costs, but it is less well known how the associations between excess weight and costs vary across different types of healthcare service. We reviewed studies using individual participant data to estimate associations between body mass index and healthcare costs, and summarized how annual healthcare costs for overweight (body mass index 25 to30 kg/m

Objectives: The use of value of information methods to inform trial design has been widely advocated but there have been few empirical applications of these methods and there is little evidence they are widely used in decision making. This study considers the usefulness of value of information models in the context of a real clinical decision problem relating to alternative diagnostic strategies for patients with a recent non-ST elevated myocardial infarction.Methods: A pretrial economic model is constructed to consider the cost-effectiveness of two competing strategies: coronary angiography alone or in conjunction with fractional flow reserve measurement. A closed-form solution to the expected benefits of information is used with optimal sample size estimated for a range of models reflecting increasingly realistic assumptions and alternative decision contexts.Results: Fractional flow reserve measurement is expected to be cost-effective with an incremental cost-effectiveness ratio of GBP 1,621, however, there is considerable uncertainty in this estimate and consequently a large expected value to reducing this uncertainty via a trial. The recommended sample size is strongly affected by the reality of the assumptions of the expected value of information (EVI) model and the decision context.Conclusions: Value of information models can provide a simple and flexible approach to clinical trial design and are more consistent with the constraints and objectives of the healthcare system than traditional frequentist approaches. However, the variation in sample size estimates demonstrates that it is essential that appropriate model parameters and decision contexts are used in their application.

Background— Extended-release niacin with laropiprant did not significantly reduce the risk of major vascular events and increased the risk of serious adverse events in Heart Protection Study 2-Treatment of HDL to Reduce the Incidence of Vascular Events (HPS2-THRIVE), but its net effects on health and healthcare costs are unknown. Methods and Results— 25 673 participants aged 50 to 80 years with previous cardiovascular disease were randomized to 2 g of extended-release niacin with 40 mg of laropiprant daily versus matching placebo, in addition to effective statin-based low-density lipoprotein cholesterol–lowering treatment. The net effects of niacin–laropiprant on quality–adjusted life years and hospital care costs (2012 UK £; converted into US $ using purchasing power parity index) during 4 years in HPS2-THRIVE were evaluated using estimates of the impact of serious adverse events on health-related quality of life and hospital care costs. During the study, participants assigned niacin–laropiprant experienced marginally but not statistically significantly lower survival (0.012 fewer years [standard error (SE) 0.007]), fewer quality-adjusted life years (0.023 [SE 0.007] fewer using UK EQ-5D scores; 0.020 [SE 0.006] fewer using US EQ-5D scores) and accrued greater hospital costs (UK £101 [SE £37]; US $145 [SE $53]). Stroke, heart failure, musculoskeletal events, gastrointestinal events, and infections were associated with significant decreases in health-related quality of life in both the year of the event and in subsequent years. All serious vascular and nonvascular events were associated with substantial increases in hospital care costs. Conclusions— In HPS2-THRIVE, the addition of extended-release niacin–laropiprant to statin-based therapy reduced quality of life–adjusted survival and increased hospital costs. Clinical Trial Registration— URL: http://clinicaltrials.gov . Unique identifier: NCT00461630.

Objectives A policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. In this study, we introduce a cardiovascular disease (CVD) policy model which can be used to model remaining life expectancy including a measure of socioeconomic deprivation as an independent risk factor for CVD. Design A state transition model was developed using the Scottish Heart Health Extended Cohort (SHHEC) linked to Scottish morbidity and death records. Individuals start in a CVD-free state and can transit to three CVD event states plus a non-CVD death state. Individuals who have a non-fatal first event are then followed up until death. Taking a competing risk approach, the cause-specific hazards of a first event are modelled using parametric survival analysis. Survival following a first non-fatal event is also modelled parametrically. We assessed discrimination, validation and calibration of our model. Results Our model achieved a good level of discrimination in each component (c-statistics for men (women)—non-fatal coronary heart disease (CHD): 0.70 (0.74), non-fatal cerebrovascular disease (CBVD): 0.73 (0.76), fatal CVD: 0.77 (0.80), fatal non-CVD: 0.74 (0.72), survival after non-fatal CHD: 0.68 (0.67) and survival after non-fatal CBVD: 0.65 (0.66)). In general, our model predictions were comparable with observed event rates for a Scottish randomised statin trial population which has an overlapping follow-up period with SHHEC. After applying a calibration factor, our predictions of life expectancy closely match those published in recent national life tables. Conclusions Our model can be used to estimate the impact of primary prevention interventions on life expectancy and can assess the impact of interventions on inequalities.

Objective. To compare a range of statistical models to enable the estimation of EQ-5D-3L utilities from responses to the Parkinson’s Disease Questionnaire 39 (PDQ-39). Methods. Linear regression, beta regression, mixtures of linear regressions and beta regressions, and multinomial logistic regression were compared in terms of their ability to accurately predict EQ-5D-3L utilities from responses to the PDQ-39 using mean error (ME), mean absolute error (MAE), and mean square error (MSE), overall and by Hoehn and Yahr stage. Models were estimated using data from the PD MED trial ( n = 9123) and assessed on both the estimation data as well as external data from the PD SURG trial ( n = 917). Results. Overall, the differences in the metrics of fit between models were small in both data sets, with performance poorer for all models in PD SURG. The performance across Hoehn and Yahr stages 1 to 3 were also similar, but multinomial logistic regression was found to exhibit less bias and better individual-level predictive accuracy in PD MED for those in Hoehn and Yahr stages 4 or 5. Overall, the multinomial logistic regression reported an ME of 0.038 out of sample and MAEs of 0.128 and 0.164 and MSEs of 0.030 and 0.044 in the estimation and external data sets, respectively. Poorer levels of the mobility domain score of the PDQ-39 were associated with increased odds of reporting problems for all EQ-5D domains except anxiety/depression. Conclusions. Finite mixture models with only few components can approximate the distribution of EQ-5D-3L utilities well but did not demonstrate improvements in predictive accuracy compared with multinomial logistic regression in the present data set.

The EHDEN network has the potential to support the development of externally validated clinical prediction models at high speed and in a transparent manner. Under the incoming Medical Device Regulations in Europe (2017/745), clinical prediction models that are used to inform further testing or treatment decisions for patients are likely to be classified as medium to high risk medical devices and be regulated accordingly. The clinical evidence required to demonstrate performance and safety over a device’s lifetime and the intensity of post-market surveillance are greater than in the outgoing directives. This, in turn, is likely to lead to greater scrutiny by health technology assessment bodies and has prompted the development of evidence standards for the demonstration of clinical benefit. The development and use of such models must also adhere to other European and national regulations such as the General Data Protection Regulation. To ensure that high-quality clinical prediction models developed through the EHDEN network can usefully inform clinical practice, it is essential that developers take into account these changing regulatory requirements. In this report we provide an overview of the incoming regulations and evidence standards for clinical algorithms, as well as best practice methods for clinical prediction model development. We discuss the challenges that this will pose for those developing clinical prediction models on the EHDEN platform and provide guidance for the future development of such models. Models developed on the EHDEN network using OHDSI tools are well placed to provide substantial evidence on a model’s performance both in-sample and in one or more external datasets and to do so in a transparent manner following the principles of open science. However, it is also important to demonstrate clinical effectiveness against usual practice, ideally through a randomised controlled trial. Developers must also establish mechanisms to collect data on the real-world use of their clinical algorithms to support post-market surveillance and vigilance activities. Some commentators have argued that European regulations require decisions made based on clinical algorithms to be explainable (conceptualisations of which differ), which may impede the use of certain machine learning methods. However, there remains considerable debate about to what extent European regulations demand this. Others argue that demonstrable performance benefits are of utmost importance. It is also essential that those developing prediction models engage with healthcare practitioners, managers, and patients to ensure that any model developed is acceptable to clinicians and patients and can be integrated within existing clinical software systems. Finally, ethical concerns (e.g. due to unfair discrimination) need to be placed at the forefront of algorithmic development and application.