A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model.

Background: The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors. Objectives: To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model. Data sources: Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022. Methods: Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling. Results: The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease. Limitations: The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps. Conclusions: The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling. Future work: A large (n = 15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment. Study registration: This study is registered as PROSPERO CRD42022366496. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Coronary artery disease affects around 2.3 million people in the United Kingdom. It is caused by a build-up of fatty plaques on the walls of the blood vessels that supply the heart muscle. This can reduce the flow of blood to the heart and result in people experiencing chest pain (angina), especially when they exercise. Over time, the fatty plaques can grow and block more or all of the artery and blood clots can also form, causing blockage. A heart attack happens when the supply of blood to the heart muscle is blocked. People who have episodes of chest pain, whose doctors think that they may have coronary artery disease, can have a type of imaging (computed tomography coronary angiography) which shows whether there is any narrowing of their coronary arteries. When offering treatment, specialist heart doctors are likely to consider a person's symptoms and other risk factors (such as family history of heart disease, diabetes and smoking history), as well as how much narrowing of the arteries has happened. CaRi-Heart® is a computer programme that uses information about inflammation in a person's coronary arteries, together with recognised risk factors, such as age, sex, smoking, high cholesterol levels, high blood pressure and diabetes, to estimate an individual's risk of dying from a heart attack in the next 8 years. There is evidence that CaRi-Heart® is better at estimating this risk than using information recognised risk factors alone. However, there is a lack of information about how treatment could change as a result of using CaRi-Heart® and whether any changes would improve outcomes for patients. There is also a lack of information about how much CaRi-Heart® would cost the National Health Service.

Unravelling Elements of Value of Healthcare and Assessing their Importance Using Evidence from Two Discrete-Choice Experiments in England.

BACKGROUND: Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England. METHOD: Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models. RESULTS: Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included. CONCLUSION: The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm.

The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries.

The impact of the COVID-19 pandemic on children and adolescent mental health in-patient service use in England: interrupted time-series analysis of national patient records.

BACKGROUND: During the initial phases of the COVID-19 pandemic, children and young people (CYP) faced significant restrictions. The virus and mitigation approaches significantly impacted how health services could function and be safely delivered. AIMS: To investigate the impact of COVID-19 lockdowns on CYP psychiatric admission trends during lockdown 1 (started 23 Mar 2020) and lockdown 2 (started 5 Nov 2020) of the COVID-19 pandemic in England. METHOD: Routinely collected, retrospective English administrative data regarding psychiatric hospital admissions, length of stay and patient demographic factors were analysed using an interrupted time series analysis (ITSA) to estimate the impact of COVID-19 lockdowns 1 and 2 on service use trends. We analysed data of 6250 CYP (up to 18 years of age) using ordinary least squares (OLS) regression analysis with Newey-West standard errors to handle autocorrelation and heteroscedasticity. RESULTS: Psychiatric hospital admissions for CYP significantly fell during lockdown 1, and then fell even further during lockdown 2. A greater proportion of admissions during lockdown were out of area or to independent sector units. During lockdown, the average age of CYP admitted was higher, and a greater proportion were female. There was also a significant increase in the proportion of looked-after children and CYP from the most socioeconomically deprived areas admitted during lockdown 2. CONCLUSIONS: During both lockdowns, fewer CYP had psychiatric admissions. The subsequent rise in admissions for more socioeconomically deprived CYP and looked-after children suggests that these CYP may have been disproportionately affected by the pandemic, or overlooked during earlier phases.

Cost-effectiveness of therapist-assisted internet-delivered psychological therapies for PTSD differing in trauma focus in England: an economic evaluation based on the STOP-PTSD trial.

BACKGROUND: Although there are effective psychological treatments for post-traumatic stress disorder (PTSD), they remain inaccessible for many people. Digitally enabled therapy is a way to overcome this problem; however, there is little evidence on which forms of these therapies are most cost effective in PTSD. We aimed to assess the cost-effectiveness of the STOP-PTSD trial, which evaluated two therapist-assisted, internet-delivered cognitive behavioural therapies: cognitive therapy for PTSD (iCT-PTSD) and a programme focusing on stress management (iStress-PTSD). METHODS: In this health economic evaluation, we used data from the STOP-PTSD trial (n=217), a single-blind, randomised controlled trial, to compare iCT-PTSD and iStress-PTSD in terms of resource use and health outcomes. In the trial, participants (aged ≥18 years) who met DSM-5 criteria for PTSD were recruited from primary care therapy services in South East England. The interventions were delivered online with therapist support for the first 12 weeks, and three telephone calls over the next 3 months. Participants completed questionnaires on symptoms, wellbeing, quality of life, and resource use at baseline, 13 weeks, 26 weeks, and 39 weeks after randomisation. We used a cost-effectiveness analysis to assess cost per quality-adjusted life year (QALY) at 39 weeks post-randomisation, from the perspective of the English National Health Service (NHS) and personal social services and on the basis of intention-to-treat for complete cases. Treatment modules and the platform design were developed with extensive input from service users: service users also advised on the trial protocol and methods, including the health economic measures. This is a pre-planned analysis of the STOP-PTSD trial; the trial was registered prospectively on the ISRCTN Registry (ISRCTN16806208). FINDINGS: NHS costs were similar across treatment groups, but clinical outcomes were superior for iCT-PTSD compared with iStress-PTSD. The incremental cost-effectiveness ratio for NHS costs and personal social services was estimated as £1921 per QALY. iCT-PTSD had an estimated 91·6% chance of being cost effective at the £20 000 per QALY threshold. From the societal perspective, iCT-PTSD was cost saving compared with iStress-PTSD. INTERPRETATION: iCT-PTSD is a cost-effective form of therapist-assisted, internet-delivered psychological therapy relative to iStress-PTSD, and it could be considered for clinical implementation. FUNDING: Wellcome Trust and National Institute of Health Research Oxford Health Biomedical Research Centre.

Rationing in an Era of Multiple Tight Constraints: Is Cost-Utility Analysis Still Fit for Purpose?

Cost-utility analysis may not be sufficient to support reimbursement decisions when the assessed health intervention requires a large proportion of the healthcare budget or when the monetary healthcare budget is not the only resource constraint. Such cases include joint replacement, coronavirus disease 2019 (COVID-19) interventions and settings where all resources are constrained (e.g. post-COVID-19 or in low/middle-income countries). Using literature on health technology assessment, rationing and reimbursement in healthcare, we identified seven alternative frameworks for simultaneous decisions about (dis)investment and proposed modifications to deal with multiple resource constraints. These frameworks comprised constrained optimisation; cost-effectiveness league table; 'step-in-the-right-direction' approach; heuristics based on effective gradients; weighted cost-effectiveness ratios; multicriteria decision analysis (MCDA); and programme budgeting and marginal analysis (PBMA). We used numerical examples to demonstrate how five of these alternative frameworks would operate. The modified frameworks we propose could be used in local commissioning and/or health technology assessment to supplement standard cost-utility analysis for interventions that have large budget impact and/or are subject to additional constraints.

Assessing the relationship between coverage of essential health services and poverty levels in low- and middle-income countries.

Universal health coverage (UHC) aims to provide essential health services and financial protection to all. This study aimed to assess the relationship between the service coverage aspect of universal health coverage and poverty in low- and middle-income countries (LMICs). Using country-level data from 96 LMICs from 1990 to 2017, we employed fixed-effects and random-effects regressions to investigate the association of eight service coverage indicators (inpatient admissions; antenatal care; skilled birth attendance; full immunization; cervical and breast cancer screening rates; diarrhoea and acute respiratory infection treatment rates) with poverty headcount ratios and gaps at the $1.90, $3.20 and $5.50 poverty lines. Missing data were imputed using within-country linear interpolation or extrapolation. One-unit increases in seven service indicators (breast cancer screening being the only one with no significant associations) were associated with reduced poverty headcounts by 2.54, 2.46 and 1.81 percentage points at the $1.90, $3.20 and $5.50 lines, respectively. The corresponding reductions in poverty gaps were 0.99 ($1.90), 1.83 ($3.20) and 1.89 ($5.50) percentage points. Apart from cervical cancer screening, which was only significant in one poverty headcount model ($5.50 line), all other service indicators were significant in either the poverty headcount or gap models at both $1.90 and $3.20 poverty lines. In LMICs, higher service coverage rates are associated with lower incidence and intensity of poverty. Further research is warranted to identify the causal pathways and specific circumstances in which improved health services in LMICs might help to reduce poverty.