AIMS:The Evoque™ transcatheter tricuspid valve replacement (TTVR) system demonstrated superiority over optimal medical treatment (OMT) in the TRISCEND II trial. We aimed to evaluate the cost-effectiveness of TTVR in patients with severe Tricuspid Regurgitation (TR) from the French healthcare perspective. METHODS AND RESULTS:A state-transition Markov model was developed to assess the cost-effectiveness of Evoque™ TTVR versus OMT alone over a lifetime horizon. The modeled cohort reflected the TRISCEND II population. Health states were defined by TR severity (none/trace, mild, moderate, severe) and death. Transition probabilities, adverse events, and hospitalisation rates were informed by TRISCEND II. Long-term survival outcomes were extrapolated by TR severity using published data. Utilities were estimated from NYHA class distribution. Costs and health outcomes were discounted at 2.5% annually.Evoque™ was associated with an incremental 1.76 life-years gained (LYG) and 1.63 quality-adjusted LYG (QALY). Incremental costs were €39,382 driven by device acquisition and procedure costs, resulting in an incremental cost-effectiveness ratio (ICERs) of €22,327/LYG and €24,109/QALY gained. Assuming no mortality difference in extrapolations, the ICER would increase to €86,428/QALY (+258%). The model was highly sensitive to assumptions on time horizon, utility, discount rate and post-trial mortality extrapolation, leading to major uncertainty. CONCLUSION:Evoque™ may represent a cost-effective strategy in severe TR patients, however this should be interpreted with caution, as results rely heavily on extrapolated data and are subject to high structural and parameter uncertainty. The projected long-term benefits are largely model driven rather than empirically demonstrated, emphasising the need for extended follow-up data.

Objective:To assess the cost-effectiveness of using artificial intelligence (AI)-derived software to assist reading CT scans of the chest to identify and analyse lung nodules compared to unaided reading in symptomatic, incidental and screening populations. Methods:Decision tree structures were developed in TreeAge Pro 2021. Structures were informed by British Thoracic Society clinical guidelines and clinical opinion. Results were presented as incremental cost-effectiveness ratios (ICERs) expressed as cost per quality-adjusted life-year (QALY) over a lifetime from the UK National Health Service and Personal Social Services perspective. Results:For the symptomatic population, the unaided radiologist reading strategy dominated the AI-assisted reading strategy. In the incidental population, unaided radiologist reading was cost-effective with an ICER of approximately £1000 per QALY. Conversely, in the screening population, AI-assisted radiologist reading dominated unaided reading. The cause of AI assistance being cost-effective depended on the number of people who had undergone CT surveillance because of non-cancerous findings. Given the limitations in the quality and quantity of evidence to inform inputs, these results should be interpreted with caution. Conclusion:Current analyses based on limited evidence suggested that, in the symptomatic and incidental populations, unaided radiologist reading may be the more cost-effective strategy, while in the screening population, AI-assisted radiologist reading appeared to be the dominant strategy. Better quality evidence is required to have a definitive answer about their cost-effectiveness. Advances in knowledge:This paper shows whether adding AI-derived software to radiologists' reading of CT scans to identify lung nodules offers good value for money.

Introduction Obesity affects over a quarter of the UK population and can lead to serious health issues. NHS Specialist Weight Management Services (WMS) offer treatments including lifestyle advice, psychological support and medications, but access and availability vary by region. Although around 4 million people could be eligible for NHS Specialist WMS annually, capacity is limited to 35 000, severely limiting overall access for those who need it. While digital technology has started to be used in WMS, more evidence is needed to confirm its long-term effectiveness, acceptability and cost-effectiveness. This study explores the use of Gro Health W8Buddy, a digital platform and app providing remote Specialist WMS. It aims to determine the long-term health benefits of remote WMS pathway Gro Health W8Buddy compared with standard NHS WMS delivered in hospitals, and to improve patients access to services.Methods and analysis The study is a real-world evaluation with observational data collection. We will recruit 450 study participants from four NHS specialist WMS who will choose either standard NHS WMS or the digital pathway Gro Health W8Buddy. Participants are being given the option to choose their pathway to generate real-world evidence. We will measure and analyse health outcomes including weight loss, time taken to be treated and cost-effectiveness, at 18 months and follow up at 24 months for later analysis (outside of this core funding). We will gather experiential data from patients and healthcare professionals through surveys, observation and interviews.Ethics and dissemination Ethical approval has been obtained from NHS Health Research Authority (HRA) and Health and Care Research Wales (HCRW) (Supplementary Figure 3) (REC reference: 25/EM/0147). Our findings will be disseminated through academic publications, conference presentations and stakeholder engagement.Trial registration ISRCTN89168871; Pre-results.

Obstructive sleep apnoea (OSA) is a chronic condition characterised by recurrent episodes of apnoea and hypopnoea. People with untreated OSA are at increased risk of excessive daytime sleepiness (EDS), functional impairment and developing cardiovascular conditions, metabolic and neurocognitive diseases. EDS is a key determinant of quality of life (QoL) and is associated with increased healthcare utilisation and accident risk. Untreated OSA can also lead to impaired work productivity, reduced health-related quality of life (HRQoL) and life expectancy, which contribute to the UK economic burden. To review and critically appraise all published economic evaluations that assessed the cost-effectiveness of devices and strategies to diagnose and manage people with moderate-to-severe OSA. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews guidelines and registered in the Prospective Register of Ongoing Systematic Reviews (CRD42024535732). We searched electronic databases up to March 2025. Two reviewers undertook screening, data extraction and quality appraisal using recognised reporting quality and methodological quality appraisal tools. The findings were summarised and discussed narratively. Twenty-nine studies were included. Fifteen studies included an economic evaluation alongside a clinical trial and 14 studies were model-based economic analyses. Although many analyses were methodologically sound in their overall approach, there were concerns regarding choice of time horizon, selection of health states, undertaking pre-model analyses and long-term adherence and efficacy of continuous positive airway pressure treatment (CPAP). Substantial methodological heterogeneity was identified across studies, including variation in diagnostic thresholds and scoring criteria used, model structures and assumptions regarding treatment adherence. Only four of twenty-nine included studies used home respiratory polygraphy (HRP) as a comparator, limiting the direct applicability of much of the evidence to the UK setting. Past economic analyses offer insight into the methods used to assess the cost-effectiveness of strategies to diagnose OSA. These economic analyses are a useful framework to build on, but clear gaps exist in the clinical and economic evidence. Future economic evaluations should prioritise contemporary diagnostic pathways and technologies, use consistent diagnostic definitions, and incorporate more realistic modelling of long-term treatment adherence and benefit. Until such information becomes available, the true cost-effectiveness of these strategies remains unknown.

There are significant regional disparities in NHS Tier 3 specialist weight management services (SWMS) across the UK. In the context of an ongoing policy shift from hospital to community-based care, a detailed examination of the components of Tier 3 services is needed to clarify the distribution of responsibilities across the weight management pathway. We conducted a comparative service mapping of SWMS across four NHS sites in England and Wales, with on-site observation, staff interviews and document review. While the core structure and service delivery at the four sites broadly aligned with the National Institute for Health and Care Excellence (NICE) guidance, substantial variations were identified in service accessibility and content, alongside widespread resource constraints and communication challenges. These findings suggest that the strategic shift of SWMS care from hospitals to community settings is proceeding without an infrastructure to meet anticipated demand. Clearer definitions of service requirements within SWMS, sustainable funding mechanisms and data-driven commissioning may help reduce regional variation and inequalities in service provision.

OBJECTIVES:To undertake a systematic review evaluating the clinical and cost-effectiveness of technology-enhanced positive end-expiratory pressure (PEEP) optimization strategies in adults and children receiving invasive mechanical ventilation on an ICU. DATA SOURCES:We searched key electronic databases (including MEDLINE and Embase) from inception to July 2024. STUDY SELECTION:We included randomized studies examining clinical or cost-effectiveness of technology-enhanced PEEP optimization strategies compared with standard care or an alternative PEEP optimization strategy in adults and children. The primary outcome was duration of mechanical ventilation and secondary outcomes were clinical effectiveness (e.g., mortality) and efficacy (e.g., PEEP). DATA EXTRACTION:Two reviewers independently assessed eligibility, extracted data, assessed risk of bias (Revised Cochrane tool) and performed Grading of Recommendations Assessment, Development and Evaluation evidence certainty assessments. DATA SYNTHESIS:Our database and trial register search retrieved 8845 results, of which 34 studies (2951 patients) were included. Eight studies were at low risk of bias. Across studies, 7 technologies were evaluated, most commonly esophageal balloon measurement of transpulmonary pressure (10 studies), electrical impedance tomography (7 studies), pressure-volume curve analysis (6 studies), and fully automated closed-loop ventilation (5 studies). Meta-analysis used random-effects models. Duration of mechanical ventilation was reported in only three studies (172 patients, two technologies) and there was no effect compared with standard care (mean difference -0.06 d; 95% CI, -0.20 to 0.09; very low-certainty evidence).For 28-day mortality (10 studies; 1,719 patients; six technologies), technology-enhanced PEEP optimization reduced 28-day mortality (risk ratio 0.69; 95% CI, 0.52-0.93; very low-certainty evidence). No significant differences were found for other clinical-effectiveness outcomes. We identified no evidence in children or on cost-effectiveness. CONCLUSIONS:Technology-enhanced PEEP optimization strategies did not reduce duration of mechanical ventilation, but these technologies may reduce mortality. Evidence certainty was low or very low, highlighting the urgent need for adequately powered randomized trials. REGISTRATION:PROSPERO (CRD42024555390).

Background There is widespread interest in the use of innovative ventilation technologies to improve clinical outcomes across the 13–20 million people each year globally that receive invasive ventilation on an intensive care unit. This scoping review aims to summarise the volume and nature of evidence underpinning the use of 22 innovative ventilation technologies in adults and children. Methods We searched MEDLINE, EMBASE, Cochrane library and other key databases from 2010 to May 2024 for primary studies and systematic reviews that evaluated the use of 22 innovative ventilation technologies in adults and children requiring, or at risk of requiring, invasive ventilation. We defined an innovative ventilation technology as a ventilation approach not currently recommended by clinical guidelines due to lack of or uncertainty of evidence. We summarise findings as evidence maps. Results Our search identified 22,274 records of which we included 851 studies (564 primary studies; 277 systematic reviews; 10 economic evaluation studies). Over 50% of studies focussed on non-invasive respiratory support strategies to reduce the risk of a primary tracheal intubation (n=319, 37%) or re-intubation (n=130, 15%). We identified ten or fewer studies for seven technologies, including phrenic nerve stimulation, artificial intelligence, and ultra-low tidal volume ventilation. Few studies include children (n=128, 15%) or report patient-focussed outcomes (n=19, 2%). Conclusions For many technologies despite being used in clinical practice, the available evidence is currently inadequate to determine its clinical effectiveness, particularly in children. Key technologies need to be evaluated in high-quality multi-centre clinical trials that report patient-focussed outcomes.

Objective To explore the challenges experienced by people with intellectual disability, their carers and health and social care professionals when using and managing medication.Design A synthesis of qualitative research using meta-ethnography.Data source We searched seven databases: MEDLINE, Embase, CINAHL, Science, Social Science and Conference Proceedings Citation Indices (Web of Science), Cochrane Library, PsycINFO and Proquest Dissertations and Theses from inception to September 2022 (updated in July 2023).Eligibility criteria for selecting studies We included studies exploring the challenges and perceptions of people with intellectual disability, their carers and health and social care professionals regarding medication management and use.Results We reviewed 7593 abstracts and 475 full texts, resulting in 45 included papers. Four major themes were identified: (1) Medication-related issues, (2) navigating autonomy and relationships, (3) knowledge and training needs and (4) inequalities in the healthcare system. We formulated a conceptual framework centred around people with intellectual disability and described the interconnectedness between them, their carers and health and social care professionals in the process of managing and using medication. We identified challenges that could be associated with the person, the medication and/or the context, along with a lack of understanding of these challenges and a lack of capability or resources to tackle them. We developed an overarching concept of ‘collective collaboration’ as a potential solution to prevent or mitigate problems related to medication use in people with intellectual disability.Conclusions The effective management of medication for people with intellectual disability requires a collaborative and holistic approach. By fostering person-centred care and shared decision-making, providing educational and practical support, and nurturing strong relationships between all partners involved to form a collective collaboration surrounding people with intellectual disability, improved medication adherence and optimised therapeutic outcomes can be achieved.PROSPERO registration number CRD42022362903.

Background:Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images. Objective:To assess the accuracy, clinical effectiveness and cost-effectiveness of using  software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading. Design:Systematic review and de novo cost-effectiveness analysis. Methods:Searches were undertaken from 2012 to January 2022. Company submissions were accepted until 31 August 2022. Study quality was assessed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2), the extension to QUADAS-2 for assessing risk of bias in comparative accuracy studies (QUADAS-C) and the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Outcomes were synthesised narratively. Two decision trees were used for cost-effectiveness: (1) a simple decision tree for the detection of actionable nodules and (2) a decision tree reflecting the full clinical pathways for people undergoing chest computed tomography scans. Models estimated incremental cost-effectiveness ratios, cost per correct detection of an actionable nodule, and cost per cancer detected and treated. We undertook scenario and sensitivity analyses. Results:Twenty-seven studies were included. All were rated as being at high risk of bias. Twenty-four of the included studies used retrospective data sets. Seventeen compared readers with and without artificial intelligence software. One reported prospective screening experiences before and after artificial intelligence software implementation. The remaining studies either evaluated stand-alone artificial intelligence or provided only non-comparative evidence. (1) Artificial intelligence assistance generally improved the detection of any nodules compared with unaided reading (three studies; average per-person sensitivity 0.43-0.68 for unaided and 0.79-0.99 for artificial intelligence-assisted reading), with similar or lower specificity (three studies; 0.77-1.00 for unaided and 0.81-0.97 for artificial intelligence-assisted reading). Nodule diameters were similar or significantly larger with semiautomatic measurements than with manual measurements. Intra-reader and inter-reader agreement in nodule size measurement and in risk classification generally improved with artificial intelligence assistance or were comparable to those with unaided reading. However, the effect on measurement accuracy is unclear. (2) Radiologist reading time generally decreased with artificial intelligence assistance in research settings. (3) Artificial intelligence assistance tended to increase allocated risk categories as defined by clinical guidelines. (4) No relevant clinical effectiveness and cost-effectiveness studies were identified. (5) The de novo cost-effectiveness analysis suggested that for symptomatic and incidental populations, artificial intelligence-assisted computed tomography image analysis dominated the unaided radiologist in cost per correct detection of an actionable nodule. However, when relevant costs and quality-adjusted life-years from the full clinical pathway were included, artificial intelligence-assisted computed tomography reading was dominated by the unaided reader. For screening, artificial intelligence-assisted computed tomography image analysis was cost-effective in the base case and all sensitivity and scenario analyses. Limitations:Due to the heterogeneity, sparseness, low quality and low applicability of the clinical effectiveness evidence and the major challenges in linking test accuracy evidence to clinical and economic outcomes, the findings presented here are highly uncertain and provide indicators/frameworks for future assessment. Conclusions:Artificial intelligence-assisted analysis of computed tomography scan images may reduce variability of and improve consistency in the measurement and clinical management of lung nodules. Artificial intelligence may increase nodule and cancer detection but may also increase the number of patients undergoing computed tomography surveillance unnecessarily. No direct comparative evidence was found, and nor was any direct evidence found on clinical outcomes and cost-effectiveness. Artificial intelligence-assisted image analysis may be cost-effective in screening for lung cancer but not for symptomatic populations. However, reliable estimates of cost-effectiveness cannot be obtained with current evidence. Study registration:This study is registered as PROSPERO CRD42021298449. Funding:This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135325) and is published in full in Health Technology Assessment; Vol. 29, No. 14. See the NIHR Funding and Awards website for further award information.

Due to the risk of Shiga-toxin producing Escherichia coli (STEC) transmission, current guidance advises excluding young children from childcare settings until microbiologically clear. Children can shed STEC for a prolonged period, and the cost-effectiveness of exclusion has not been evaluated. Our decision tree analysis, including probabilistic sensitivity analysis, estimated comparative health system costs and effects of exclusion until microbiological clearance versus return to childcare setting before this. Due to the risk of secondary cases, return before microbiological clearance resulted in the incremental loss of 0.019 QALYs, but savings of 156 pound. Using the willingness-to-pay threshold of 20000 pound per QALY, the incremental net monetary benefit of exclusion until microbiological clearance was 215 pound. Exclusion until microbiological clearance remained cost-effective if the total costs for managing the exclusion were below 576 pound. Return before microbiological clearance may, therefore, become cost-effective in cases where the costs of managing exclusion until microbiological clearance are high and/or the risk of secondary cases is very low. Broadening the decision perspective, including the costs of exclusion to the families, may also impact the recommendation. Further research is needed to assess the risk of STEC transmission from children who have clinically recovered and the impact of STEC and exclusion on families of the affected children.

OBJECTIVE:Spinal Muscular Atrophy (SMA) is a rare genetic disorder marked by progressive muscle weakness and mobility loss. It has a profound physical, emotional and social impact on patients and caregivers, requiring comprehensive medical and supportive care. SMA is classified into Types 1-4, with some individuals identified presymptomatically. This systematic review examined the safety and effectiveness of nusinersen and risdiplam for treating SMA. METHODS:We searched research databases, relevant websites and existing systematic reviews. Screening, data extraction and quality assessment were conducted independently by two authors, with discrepancies resolved by a third. Internal quality appraisal ensured methodological rigour. A total of 131 studies reported in 148 sources were included. The review is registered with PROSPERO (CRD42024512226). RESULTS:Both treatments showed improvements in motor function and milestones, with high survival rates across most SMA types. Motor function improvements were consistent, but other outcomes-such as bulbar and respiratory function, and ventilation needs-were variable. Adverse events were common across all treatments and SMA types, with some serious cases reported, including deaths in Types 1 and 2. INTERPRETATION:This comprehensive review highlights the clinical effectiveness and safety of nusinersen and risdiplam across all SMA types. However, variability in outcomes and limited comparative data introduce uncertainty. The findings underscore the need for more high-quality randomised controlled trials to strengthen the evidence base for SMA treatment.

Spinal muscular atrophy (SMA) is a rare, life-limiting neuromuscular disorder characterised by progressive motor neuron degeneration. The recent emergence of disease-modifying therapies (DMTs), nusinersen, onasemnogene abeparvovec, and risdiplam, has revolutionised SMA care but presents economic challenges due to high treatment costs and limited long-term evidence. To review and critically appraise economic evaluations that assessed the cost-effectiveness of DMTs in people living with SMA. A systematic literature review was conducted following Cochrane and PRISMA guidelines. Initial searches were conducted in January 2024 and updated in February 2025. Searches were carried out in key biomedical and economic databases, as well as grey literature. Two reviewers independently screened the titles and abstracts of all identified records, as well as the full texts of potentially relevant studies. Data extraction and quality appraisal employed established tools, including the CHEERS and Philips checklists. The conduct and findings of included studies were summarised and discussed narratively. Of 1,984 records, 21 studies met the inclusion criteria. All studies used Markov modelling approaches, varying by SMA type, time horizon (often lifetime), and assumptions around sustained treatment benefits. Key drivers of cost-effectiveness included treatment costs, health-state utility values (frequently based on expert opinion), and survival modelling. Heterogeneity was noted in health technology definitions, utility measurement, and data sources. Limitations across studies included reliance on short-term clinical data, inconsistent assumptions, and limited of transparency in modelling practices. Sensitivity analyses were inconsistently applied, limiting robustness of the findings reported in each study. The economic evaluation landscape for SMA treatments is evolving. However, challenges remain due to data gaps and methodological variability across studies. Future research should prioritise the integration of long-term real-world data into economic evaluations, consider the development of patient- and caregiver-derived utility values, and the use of transparent, standardised modelling approaches. These improvements will enhance the robustness, comparability, and policy relevance of economic evaluations in rare disease treatment funding.

Introduction Obstructive sleep apnoea (OSA) is a common, but underdiagnosed, sleep disorder. If untreated, it leads to poor health outcomes, including Alzheimer’s disease, cancer, cardiovascular disease and all-cause mortality. Our aim is to determine the feasibility and cost-effectiveness of moving the testing for OSA into general practice and how general practitioner (GP)-based screening affects overall detection rates.Methods and analysis Randomised controlled trial of case finding of OSA in general practice using a novel Medicines and Healthcare products Regulatory Agency-registered device (AcuPebble SA100) compared with usual care with internal feasibility phase. A diverse sample of general practices (approximately 40) from across the West Midlands Clinical Research Network will identify participants from their records. Eligible participants will be aged 50–70 years with body mass index >30 kg/m2 and diabetes (type 1 or 2) and/or hypertension (office blood pressure >145/90 mm Hg or on treatment). They will exclude individuals with known OSA or chronic obstructive pulmonary disease, or those they deem unable to take part. After eligibility screening, consent and baseline assessment, participants will be randomised to either the intervention or control group. Participants in the intervention arm will receive by post the AcuPebble sleep test kit. Those in the control arm will continue with usual care. Follow-up questionnaires will be completed at 6 months. The study is powered (90%) to detect a 5% difference and will require 606 patients in each arm (713 will be recruited to each arm to allow for attrition). Due to the nature of the intervention, participants and GPs will not be blinded to the allocation.Outcomes Primary: Detection rate of moderate-to-severe OSA in the intervention group versus control group. Secondary: Time to diagnosis and time to treatment for intervention versus control group for mild, moderate and severe OSA; cost-effectiveness analysis comparing the different testing pathways.Ethics and dissemination The trial started on 1 November 2022. Ethical approval was granted from the South Central Oxford A Research Ethics Committee on 9 June 2023 (23/SC/0188) (protocol amendment version 1.3; update with amendment and approval to renumber to V2.0 on 29 August 2023). Patient recruitment began on 7 January 2024; initial planned end date will be on 31 April 2025.Results will be uploaded to the ISRCTN register within 12 months of the end of the trial date, presented at conferences, submitted to peer-reviewed journals and distributed via our patient and public involvement networks.The University of Warwick will act as the trial sponsor. The trial will be conducted in accordance with the Sponsor and Primary Care Clinical Trials Unit standard operating procedures.Trial registration number ISRCTN 16982033.

OBJECTIVES:To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT. METHODS:A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis. RESULTS:Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (-7% to -3% for correctly detecting/categorising people without actionable nodules; -8% to -6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150-750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance. CONCLUSIONS:AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design. PROSPERO REGISTRATION NUMBER:CRD42021298449.

ObjectivesTo evaluate the cost-effectiveness of percutaneous repair (PR) for secondary mitral regurgitation.DesignAn economic evaluation using a time-varying Markov model comprising three states to assess the cost and effectiveness of PR added to guideline-directed medical treatment (GDMT) compared with GDMT alone. Clinical outcomes considered within the model were overall survival and heart failure (HF) hospitalisations (HFH), and the incremental cost-effectiveness ratio (ICER) was calculated. Cost data were derived from a literature search. Sensitivity analyses were undertaken.SettingThe French healthcare system perspective assuming a lifetime horizon.ParticipantsPublished data at 5 years obtained from patients enrolled in the Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients with Functional Mitral Regurgitation study.ResultsIn our base case, we chose cubic spline models to extrapolate overall survival, and we used log-logistic models to estimate cumulative HFH. After discounting, the model generated life-years of 3.843 years and 3.055 years for PR+GDMT and GDMT, respectively. Discounted total quality-adjusted life-year (QALY) values were 2.572 and 1.945 for PR+GDMT and GDMT, respectively (incremental 0.627 QALY). Discounted total costs were €42 709 and €20 732 for the intervention and the control groups, respectively (incremental €21,977), resulting in an ICER of €35,068/QALY. At a threshold of €50 000 per QALY, PR had a 0.85 probability of being cost-effective.ConclusionUpdated trial data have enabled investigators to provide a more reliable estimation of the ICER, which suggests that PR has good value for money compared with GDMT alone.

Objectives:Accurate measurement of lung nodules is pivotal to lung cancer detection and management. Nodule size forms the main basis of risk categorization in existing guidelines. However, measurements can be highly variable between manual readers. This article explores the impact of potentially improved nodule size measurement assisted by generic artificial intelligence (AI)-derived software on clinical management compared with manual measurement. Methods:The simulation study created a baseline cohort of people with lung nodules, guided by nodule size distributions reported in the literature. Precision and accuracy were simulated to emulate measurement of nodule size by radiologists with and without the assistance of AI-derived software and by the software alone. Nodule growth was modelled over a 4-year time frame, allowing evaluation of management strategies based on existing clinical guidelines. Results:Measurement assisted by AI-derived software increased cancer detection compared to an unassisted radiologist for a combined solid and sub-solid nodule population (62.5% vs 61.4%). AI-assisted measurement also correctly identified more benign nodules (95.8% vs 95.4%); however, it was associated with over an additional month of surveillance on average (5.12 vs 3.95 months). On average, with AI assistance people with cancer are diagnosed faster, and people without cancer are monitored longer. Conclusions:In this simulation, the potential benefits of improved accuracy and precision associated with AI-based diameter measurement is associated with additional monitoring of non-cancerous nodules. AI may offer additional benefits not captured in this simulation, and it is important to generate data supporting these, and adjust guidelines as necessary. Advances in knowledge:This article shows the effects of greater measurement accuracy associated with AI assistance compared with unassisted measurement.

Background:Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer. Objectives:This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation. Data sources:The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023. Methods:Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer. Results:None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage. Limitations:This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified. Conclusions:There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons. Future work:Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication. Study registration:This study is registered as PROSPERO CRD42023384164. Funding:This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.

ObjectiveTo review the survival modelling used in cost-effectiveness studies evaluating an interventional procedure and to discuss implications for decision-makers.DesignA case study of three economic evaluations that each used immature data from the EVEREST II High Surgical Risk (HSR) Study of transcatheter edge-to-edge repair (TEER) for patients with severe mitral regurgitation (MR) who were at high risk of surgery.SettingEstimation of patient survival in cost-effectiveness studies.ParticipantsThe EVEREST II HSR Study included 78 patients who had TEER of the mitral valve using the MitraClip device and a retrospectively identified control group of 36 patients who received medical management and were followed up for 12 months. Observed survival (TEER arm only) was updated at 5 years.ResultsTwo studies used 12-month observed mortality from EVEREST II HSR to model survival over lifetime horizons. Observed and modelled survival were associated with considerable uncertainty due to short follow-up and small numbers of participants. Modelling control patients’ survival required an approximate 10-fold extrapolation based on 12-month observation of only 38 patients. Observed 5-year survival in the TEER group differed from that less mature follow-up suggesting that survival modelling based on shorter follow-up was unsatisfactory. No public domain data for the control group are available beyond 12-month follow-up so meaningful estimates using mature data for both arms are currently not possible. A third study developed survival models using incompletely reported transitions between MR grades in EVEREST II HSR and mortality rates observed for different MR grades derived from a study in an unrelated population.ConclusionsModelling survival in such small samples followed up for only 12 months is associated with great uncertainty, and cost-effectiveness results based on these analyses should be viewed as premature and used cautiously in reimbursement decisions.

When updated clinical trial data becomes available reassessing the cost-effectiveness of technologies may modify estimates and influence decision-making. We investigated the impact of updated trial outcomes on the cost-effectiveness of percutaneous mitral repair (PR) for secondary mitral regurgitation. We updated our previous three-state time-varying Markov model to assess the cost-effectiveness of PR + guideline directed medical treatment (GDMT) versus GDMT alone. Key clinical inputs (overall survival (OS) and heart failure hospitalisations (HFH)) were obtained using the 3-year trial findings from the COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy) RCT. We calculated incremental cost-effectiveness ratios (ICER) and report how these differ between analyses based on early (2-year) and updated (3-year) evidence. Updated trial data showed an increase in mortality in the intervention arm between two and three years follow-up that was not seen in the control arm. Deterministic and multivariate cost-effectiveness modelling yielded incremental cost effectiveness ratios ICERs of €38,123 and €31,227 /QALY. Compared to our 2-year based estimate (€21,918 / QALY) these results imply an approximate 1.5-fold increase in ICER. The availability of updated survival analyses from the COAPT pivotal trial suggests previous estimates based on 2-year trial findings were over optimistic for the intervention.