How health technology assessment can help to address challenges in drug repurposing: a conceptual framework.

Drug repurposing faces various challenges that can impede its success. We developed a framework outlining key challenges in drug repurposing to explore when and how health technology assessment (HTA) methods can address them. We identified 20 drug-repurposing challenges across the categories of data access, research and development, collaboration, business case, regulatory and legal challenges. Early incorporation of HTA methods, including literature review, empirical research, stakeholder consultation, health economic evaluation and uncertainty assessment, can help to address these challenges. HTA methods canassess the value proposition of repurposed drugs, inform further research and ultimately help to bring cost-effective repurposed drugs to patients.

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

BACKGROUND: The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. METHODS: We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. RESULTS: In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). CONCLUSIONS: MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.

Background: Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke. Objectives: To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting. Methods: Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care. Results: A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher. Limitations and conclusions: The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective. Future work: Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice. Study registration: This study is registered as PROSPERO CRD42021269609. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.

Stroke is a serious life-threatening medical condition caused by a blood clot or haemorrhage in the brain. Quick and effective management, including a brain scan, of the patients with suspected stroke can make a big difference in their outcome. Artificial intelligence-derived computer programmes exist that are intended to help with the interpretation of computed tomography scans of the brain in stroke. We undertook a thorough review of the existing research into the effectiveness and value for money of using these programmes to help doctors and other specialists to interpret computed tomography brain scans. We found very little evidence to tell us how well artificial intelligence-derived computer programmes work in practice. Some studies have looked at artificial intelligence-derived computer programmes on their own (i.e. not taken together with a doctor's judgement, as they were designed to be used). Other studies have looked at what happens to patients who are treated for stroke when artificial intelligence-derived computer programmes are used; these studies provide no information about whether using artificial intelligence-derived computer programmes may have led to patients who could have benefitted from treatment being missed. It is unclear how well artificial intelligence-derived software-assisted review works when added to current clinical practice.

Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy.

OBJECTIVE: We aimed to perform an early cost-effectiveness analysis of using a whole-genome sequencing-based tumor mutation burden (WGS-TMB), instead of programmed death-ligand 1 (PD-L1), for immunotherapy treatment selection in patients with non-squamous advanced/metastatic non-small cell lung cancer ineligible for targeted therapy, from a Dutch healthcare perspective. METHODS: A decision-model simulating individual patients with metastatic non-small cell lung cancer was used to evaluate diagnostic strategies to select first-line immunotherapy only or the immunotherapy plus chemotherapy combination. Treatment was selected using PD-L1 [A, current practice], WGS-TMB [B], and both PD-L1 and WGS-TMB [C]. Strategies D, E, and F take into account a patient's disease burden, in addition to PD-L1, WGS-TMB, and both PD-L1 and WGS-TMB, respectively. Disease burden was defined as a fast-growing tumor, a high number of metastases, and/or weight loss. A threshold of 10 mutations per mega-base was used to classify patients into TMB-high and TMB-low groups. Outcomes were discounted quality-adjusted life-years (QALYs) and healthcare costs measured from the start of first-line treatment to death. Healthcare costs includes drug acquisition, follow-up costs, and molecular diagnostic tests (i.e., standard diagnostic techniques and/or WGS for strategies involving TMB). Results were reported using the net monetary benefit at a willingness-to-pay threshold of €80,000/QALY. Additional scenario and threshold analyses were performed. RESULTS: Strategy B had the lowest QALYs (1.84) and lowest healthcare costs (€120,800). The highest QALYs and healthcare costs were 2.00 and €140,400 in strategy F. In the base-case analysis, strategy A was cost effective with the highest net monetary benefit (€27,300), followed by strategy B (€26,700). Strategy B was cost effective when the cost of WGS testing was decreased by at least 24% or when immunotherapy results in an additional 0.5 year of life gained or more for TMB high compared with TMB low. Strategies C and F, which combined TMB and PD-L1 had the highest net monetary benefit (≥ €76,900) when the cost of WGS testing, immunotherapy, and chemotherapy acquisition were simultaneously reduced by at least 47%, 39%, and 43%, respectively. Furthermore, strategy C resulted in the highest net monetary benefit (≥ €39,900) in a scenario where patients with both PD-L1 low and TMB low were treated with chemotherapy instead of immunotherapy plus chemotherapy. CONCLUSIONS: The use of WGS-TMB is not cost effective compared to PD-L1 for immunotherapy treatment selection in non-squamous metastatic non-small cell lung cancer in the Netherlands. WGS-TMB could become cost effective provided there is a reduction in the cost of WGS testing or there is an increase in the predictive value of WGS-TMB for immunotherapy effectiveness. Alternatively, a combination strategy of PD-L1 testing with WGS-TMB would be cost effective if used to support the choice to withhold immunotherapy in patients with a low expected benefit of immunotherapy.

Cost of illness of patients with small fiber neuropathy in the Netherlands.

ABSTRACT: Neuropathic pain is associated with substantial healthcare costs. However, cost-of-illness studies of small fiber neuropathy (SFN) are scarce. Our aim was to estimate the healthcare, patient and family, and productivity costs of patients with SFN in the Netherlands from a healthcare and societal perspective. In addition, the association of costs with age, pain impact on daily life, anxiety, depression, and quality of life (Qol) were examined. Cost questionnaires were completed by 156 patients with confirmed SFN. The average annual total health care and societal cost (€, 2020) was calculated at patient, SFN adult population, and societal level. The average annual healthcare, patient and family, and productivity costs per patient with a Pain Impact Numerical Rating Scale of 0 to 3 (mild), 4 to 6 (moderate), and 7 to 10 (severe) were calculated by using the cost questionnaire data. Quality of life was determined by the EuroQol 5D utility scores. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale. Associations of all costs were analyzed using linear regression analyses. At the patient level, the average annual SFN healthcare and societal cost of SFN was €3614 (95% confidence interval [CI] €3171-€4072) and €17,871 (95% CI €14,395-€21,480). At the SFN population level, the average healthcare costs were €29.8 (CI €26.4-€34.2) million, and on a societal level, these were €147.7 (CI 120.5-176.3) million. Severe pain was associated with significant lower Qol and higher depression scores, higher healthcare, patient and family, and productivity costs ( P < 0.001).

The added value of risk assessment and subsequent targeted treatment for epileptic seizures after stroke: An early-HTA analysis.

INTRODUCTION: The development of post-stroke epilepsy (PSE) is related to a worse clinical outcome in stroke patients. Adding a biomarker to the clinical diagnostic process for the prediction of PSE may help to establish targeted and personalized treatment for high-risk patients, which could lead to improved patient outcomes. We assessed the added value of a risk assessment and subsequent targeted treatment by conducting an early Health Technology Assessment. METHODS: Interviews were conducted with four relevant stakeholders in the field of PSE to obtain a realistic view of the current healthcare and their opinions on the potential value of a PSE risk assessment and subsequent targeted treatment. The consequences on quality of life and costs of current care of a hypothetical care pathway with perfect risk assessment were modeled based on information from a literature review and the input from the stakeholders. Subsequently, the maximum added value (the headroom) was calculated. Sensitivity analyses were performed to test the robustness of this result to variation in assumed input parameters, i.e. the accuracy of the risk assessment, the efficacy of anti-seizure medication (ASM), and the probability of patients expected to develop PSE. RESULTS: All stakeholders considered the addition of a predictive biomarker for the risk assessment of PSE to be of value. The headroom amounted to €12,983. The sensitivity analyses demonstrated that the headroom remained beneficial when varying the accuracy of the risk assessment, the ASM efficacy, and the number of patients expected to develop PSE. DISCUSSION: We showed that a risk assessment for PSE development is potentially valuable. This work demonstrates that it is worthwhile to undertake clinical studies to evaluate biomarkers for the prediction of patients at high risk for PSE and to assess the value of targeted prophylactic treatment.

An investigation of age dependency in Dutch and Chinese values for EQ-5D-Y.

Aims: The primary aim was to explore the age dependency of health state values derived via trade-offs between health-related quality of life (HRQoL) and life years in a discrete choice experiment (DCE). The secondary aim was to explore if people weigh life years and HRQoL differently for children, adolescents, adults, and older adults. Methods: Participants from the general population of the Netherlands and China first completed a series of choice tasks offering choices between two EQ-5D-Y states with a given lifespan. The choice model captured the value of a year in full health, disutility determined by EQ-5D-Y, and a discount rate. Next, they received a slightly different choice task, offering choices between two lives that differed in HRQoL and life expectancy but produced the same number of quality-adjusted life years (QALYs). Participants were randomly assigned to fill out the survey for three or four age frames: a hypothetical person of 10, 15, 40, and 70 years (the last one only applicable to China) to allow the age dependency of the responses to be explored. Results: A total of 1,234 Dutch and 1,818 Chinese people administered the survey. Controlling for time preferences, we found that the agreement of health state values for different age frames was generally stronger in the Netherlands than in China. We found no clear pattern of differences in the QALY composition in both samples. The probability distribution over response options varied most when levels for lifespan or severity were at the extremes of the spectrum. Conclusion/discussion: The magnitude and direction of age effects on values seemed dimension- and country specific. In the Netherlands, we found a few differences in dimension-specific weights elicited for 10- and 15-year-olds compared to 40-year-olds, but the overall age dependency of values was limited. A stronger age dependency of values was observed in China, where values for 70-year-olds differed strongly from the values for other ages. The appropriateness of using existing values beyond the age range for which they were measured needs to be evaluated in the local context.

The value of correctly diagnosing axial spondyloarthritis for patients and society.

OBJECTIVE: To demonstrate the value of diagnosing axSpA, by comparing health and costs associated with available diagnostic algorithms and perfect diagnosis. METHODS: Using data from SPACE and other cohorts, a model was developed to estimate health (quality-adjusted life-years, QALYs) and costs (healthcare consumption and work productivity losses) of different diagnostic algorithms for axSpA amongst patients with low back pain referred to a rheumatologist, over a 60-year horizon. The model combined a decision-tree (diagnosis) with a state-transition model (treatment). The three algorithms (Berlin [BER, highest specificity], Modification 1 [M1; less strict inflammatory back pain (IBP) criterion] and Modification 2 [M2; IBP not mandatory as entry criterion, highest sensitivity]) were compared. Changes in sensitivity/specificity were explored and the value of perfect diagnosis was investigated. RESULTS: For each correctly diagnosed axSpA patient, up to 4.7 QALYs and €60,000 could be gained/saved, considering a societal perspective. Algorithm M2 resulted in more health and lower costs per patient (24.23 QALYs; €157,469), compared to BER (23.96 QALYs; €159,423) and M1 (24.15 QALYs; €158,417). Hypothetical improvements in M2 sensitivity resulted in slightly more value compared to improvements in specificity. Perfect diagnosis can cost €7,500 per patient and still provide enough value. CONCLUSION: Correct diagnosis of axSpA results in substantial health and cost benefits for patients and society. Not requiring IBP as mandatory for diagnosis of axSpA (algorithm M2) provides more value and would be preferable. A considerably more expensive diagnostic algorithm with better accuracy than M2 would still be considered good value for money.

Oral Azacitidine for Maintenance Treatment of Acute Myeloid Leukaemia After Induction Therapy: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

The National Institute for Health and Care Excellence (NICE) invited the manufacturer (Celgene) of oral azacitidine (ONUREG), as part of the Single Technology Appraisal (STA) process, to submit evidence for the clinical effectiveness and cost-effectiveness of oral azacitidine for maintenance treatment of acute myeloid leukaemia (AML) after induction therapy compared with watch-and-wait plus best supportive care (BSC) and midostaurin. Kleijnen Systematic Reviews Ltd, in collaboration with Maastricht University Medical Centre+, was commissioned to act as the independent Evidence Review Group (ERG). This paper summarises the company submission (CS), presents the ERG's critical review on the clinical and cost-effectiveness evidence in the CS, highlights the key methodological considerations and describes the development of the NICE guidance by the Appraisal Committee. In the QUAZAR AML-001 trial, oral azacitidine significantly improved overall survival (OS) versus placebo: median OS gain of 9.9 months (24.7 months versus 14.8 months; hazard ratio (HR) 0.69 (95% CI 0.55-0.86), p < 0.001). The median time to relapse was also better for oral azacitidine, and the incidences of TEAEs were similar for the two arms. The company excluded two of the comparators listed in the scope, low-dose cytarabine and subcutaneous azacitidine, informed only by clinical expert opinion, leaving only best supportive care (BSC) and midostaurin for the FLT3-ITD and/or FLT3-TKD (FLT3 mutation)-positive subgroup. An ITC comparing oral azacitidine to midostaurin as maintenance therapy in the appropriate subgroup demonstrated that the OS and relapse-free survival (RFS) HRs were favourable for oral azacitidine when compared with midostaurin. However, in the only available trial of midostaurin as maintenance treatment in AML that was used for this ITC, subjects were not randomised at the maintenance phase, but at induction, which posed a substantial risk of bias. The revised and final probabilistic incremental cost-effectiveness ratio (ICER) presented by the company, including a commercial arrangement, was £32,480 per quality-adjusted life year (QALY) gained for oral azacitidine versus watch-and-wait plus BSC. Oral azacitidine was dominant versus midostaurin in the FLT-3 subgroup. The ERG's concerns included the approach of modelling haematopoietic stem cell transplantation (HSCT), the generalisability of the population and the number of cycles of consolidation therapy pre-treatment in the QUAZAR AML-001 trial to UK clinical practice, and uncertainty in the relapse utility. The revised and final ERG base case resulted in a similar probabilistic ICER of £33,830 per QALY gained versus watch-and-wait plus BSC, but with remaining uncertainty. Oral azacitidine remained dominant versus midostaurin in the FLT-3 subgroup. After the second NICE appraisal committee meeting, the NICE Appraisal Committee recommended oral azacitidine (according to the commercial arrangement), within its marketing authorisation, as an option for maintenance treatment for AML in adults who are in complete remission, or complete remission with incomplete blood count recovery, after induction therapy with or without consolidation treatment, and cannot have or do not want HSCT.

Budget Impact of Three Improvement Targets for Compression Therapy for Patients with Deep Venous Thrombosis in The Netherlands.

BACKGROUND AND OBJECTIVE: Compression therapy following deep venous thrombosis in the Netherlands is suboptimal. We assessed the budget impact of targeted care improvements. METHODS: We calculated the per-patient and population healthcare resource use and costs concerning 26,500 new patients each year in the Netherlands for the current pathways in region North Holland (divided into two parts: NH-A and NH-B) and region Limburg. Next, we assessed the impact of three improvement targets: optimizing initial compression therapy, early consultation of an occupational therapist, and tailored duration of elastic compression stocking therapy. Inputs were based on interview (n = 30) and survey data (n = 114), literature, and standard prices. The robustness of the results was tested by sensitivity analyses. RESULTS: The current per-patient costs for a 2-year episode were €1046 (NH-A), €947 (NH-B), and €1256 (Limburg). The improvements led to direct savings for region Limburg (€4.7 million). Population costs increased in the first year for NH-A (+ €3.5 million) and NH-B (+ €6.4 million), and decreased in the second and third year resulting in a cost reduction for NH-A (- €2.2 million) but not for NH-B (+ €0.6 million). Workload for occupational therapists and internists in North Holland increased, and workload for home care nurses decreased in all regions. CONCLUSIONS: This study provides a detailed insight into current costs and healthcare resource use associated with compression therapy and the potential impact of implementing three improvement targets. We showed that the improvements resulted in considerable cost savings within 3 years after implementation for region NH-A and Limburg.

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations.

Uncertainty assessment is a cornerstone in model-based health economic evaluations (HEEs) that inform reimbursement decisions. No comprehensive overview of available uncertainty assessment methods currently exists. We aimed to review methods for uncertainty assessment for use in model-based HEEs, by conducting a snowballing review. We categorised all methods according to their stage of use relating to uncertainty assessment (identification, analysis, communication). Additionally, we classified identification methods according to sources of uncertainty, and subdivided analysis and communication methods according to their purpose. The review identified a total of 80 uncertainty methods: 30 identification, 28 analysis, and 22 communication methods. Uncertainty identification methods exist to address uncertainty from different sources. Most identification methods were developed with the objective to assess related concepts such as validity, model quality, and relevance. Almost all uncertainty analysis and communication methods required uncertainty to be quantified and inclusion of uncertainties in probabilistic analysis. Our review can help analysts and decision makers in selecting uncertainty assessment methods according to their aim and purpose of the assessment. We noted a need for further clarification of terminology and guidance on the use of (combinations of) methods to identify uncertainty and related concepts such as validity and quality. A key finding is that uncertainty assessment relies heavily on quantification, which may necessitate increased use of expert elicitation and/or the development of methods to assess unquantified uncertainty.

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands.

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Filgotinib for Treating Moderately to Severely Active Ulcerative Colitis: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

The National Institute for Health and Care Excellence invited the manufacturer (Galapagos) of filgotinib (Jyseleca®), as part of the Single Technology Appraisal process, to submit evidence for the clinical effectiveness and cost effectiveness of filgotinib for treating moderately to severely active ulcerative colitis in adults who have had an inadequate response, loss of response or were intolerant to a previous biologic agent or conventional therapy. Kleijnen Systematic Reviews Ltd, in collaboration with Maastricht University Medical Centre+, was commissioned to act as the independent Evidence Review Group. This paper summarises the company submission, presents the Evidence Review Group's critical review on the clinical and cost-effectiveness evidence in the company submission, highlights the key methodological considerations and describes the development of the National Institute for Health and Care Excellence guidance by the Appraisal Committee. The company submission included one relevant study for the comparison of filgotinib versus placebo: the SELECTION trial. As there was no head-to-head evidence with any of the comparators, the company performed two separate network meta-analyses, one for the biologic-naïve population and one for the biologic-experienced population, and for both the induction and maintenance phases. The Evidence Review Group questioned the validity of the maintenance network meta-analysis because it assumed all active treatments to be comparators in this phase, which is not in line with clinical practice. The economic analysis used a number of assumptions that introduced substantial uncertainty, which could not be fully explored, for instance, the assumption that a risk of loss of response would be independent of health state and constant over time. Company and Evidence Review Group results indicate that at its current price, and disregarding confidential discounts for comparators and subsequent treatments, filgotinib dominates some comparators (golimumab and adalimumab in the company base case, all but intravenous and subcutaneous vedolizumab in the Evidence Review Group's base case) in the biologic-naïve population. In the biologic-experienced population, filgotinib dominates all comparators in both the company and the Evidence Review Group's base case. Results should be interpreted with caution as some important uncertainties were not included in the modelling. These uncertainties were mostly centred around the maintenance network meta-analysis, loss of response, health-related quality-of-life estimates and modelling of dose escalation. The National Institute for Health and Care Excellence recommended filgotinib within its marketing authorisation, as an option for treating moderately to severely active ulcerative colitis in adults when conventional or biological treatment cannot be tolerated, or if the disease has not responded well enough or has stopped responding to these treatments, and if the company provides filgotinib according to the commercial arrangement.

The value of maintaining cognition in patients with mild cognitive impairment: The innovation headroom and potential cost-effectiveness of roflumilast.

INTRODUCTION: Early health-technology assessment can support discussing scarce resource allocation among stakeholders. We explored the value of maintaining cognition in patients with mild cognitive impairment (MCI) by estimating: (1) the innovation headroom and (2) the potential cost effectiveness of roflumilast treatment in this population. METHODS: The innovation headroom was operationalized by a fictive 100% efficacious treatment effect, and the roflumilast effect on memory word learning test was assumed to be associated with 7% relative risk reduction of dementia onset. Both were compared to Dutch setting usual care using the adapted International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model. RESULTS: The total innovation headroom expressed as net health benefit was 4.2 (95% bootstrap interval: 2.9-5.7) quality-adjusted life years (QALYs). The potential cost effectiveness of roflumilast was k€34 per QALY. DISCUSSION: The innovation headroom in MCI is substantial. Although the potential cost effectiveness of roflumilast treatment is uncertain, further research on its effect on dementia onset is likely valuable.

Headroom Analysis for Early Economic Evaluation: A Systematic Review.

OBJECTIVES: The headroom analysis is an early economic evaluation that quantifies the highest price at which an intervention may still be cost effective. Currently, there is no comprehensive review on how it is applied. This study investigated the application of the headroom analysis, specifically (1) how the headroom analysis is framed (2) the analytical approach and sources of evidence used, and (3) how expert judgement is used and reported. METHODS: A systematic search was conducted in PubMed, Embase, Web of Science, EconLit, and Google Scholar on 28 April 2022. Studies were eligible if they reported an application of the headroom analysis. Data were presented in tabular form and summarised descriptively. RESULTS: We identified 42 relevant papers. The headroom analysis was applied to medicines (29%), diagnostic or screening tests (29%), procedures, programmes and systems (21%), medical devices (19%), and a combined test and device (2%). All studies used model-based analyses, with 40% using simple models and 60% using more comprehensive models. Thirty-three percent of the studies assumed perfect effectiveness of the health technology, while 67% adopted realistic assumptions. Ten percent of the studies calculated an effectiveness-seeking headroom instead of a cost-seeking headroom. Expert judgement was used in 71% of the studies; 23 studies (55%) used expert opinion, 6 studies (14%) used expert elicitation, and 1 study (2%) used both. CONCLUSIONS: Because the application of the headroom analysis varies considerably, we recommend its appropriate use and clear reporting of analytical approaches, level of evidence available, and the use of expert judgement.

Fenfluramine for Treating Dravet Syndrome: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

Fenfluramine, tradename Fintepla®, was appraised within the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process as Technology Appraisal 808. Within the STA process, the company (Zogenix International) provided NICE with a written submission and a mathematical health economic model, summarising the company's estimates of the clinical effectiveness and cost-effectiveness of fenfluramine for patients with Dravet syndrome (DS). This company submission (CS) was reviewed by an evidence review group (ERG) independent of NICE. The ERG, Kleijnen Systematic Reviews in collaboration with Maastricht University Medical Centre, produced an ERG report. This paper presents a summary of the ERG report and the development of the NICE guidance. The CS included a systematic review of the evidence for fenfluramine. From this review the company identified and presented evidence from two randomised trials (Study 1 and Study 1504), an open-label extension study (Study 1503) and 'real world evidence' from a prospective and retrospective study. Both randomised trials were conducted in patients up to 18 years of age with DS, whose seizures were incompletely controlled with previous anti-epileptic drugs. A Bayesian network meta-analysis was performed to compare fenfluramine with cannabidiol plus clobazam. There was no evidence of a difference between any doses of fenfluramine and cannabidiol in the mean convulsive seizure frequency (CSF) rate during treatment. However, fenfluramine increased the number of patients achieving ≥ 50% reduction in CSF frequency from baseline compared to cannabidiol. The company used an individual-patient state-transition model (R version 3.5.2) to model cost-effectiveness of fenfluramine. The CSF and convulsive seizure-free days were estimated using patient-level data from the placebo arm of the fenfluramine registration studies. Subsequently, a treatment effect of either fenfluramine or cannabidiol was applied. Utility values for the economic model were obtained by mapping Pediatric Quality of Life Inventory data from the registration studies to EuroQol-5D-3L Youth (EQ-5D-Y-3L). The company included caregiver utilities in their base-case, as the severe needs of patients with DS have a major impact on parents and caregivers. There were several key issues. First, the company included caregiver utilities in the model in a way that when patients in the economic model died, the corresponding caregiver utility was also set to zero. Second, the model was built in R statistical software, resulting in transparency issues. Third, the company assumed the same percentage reduction for convulsive seizure days as was estimated for CSF. Fourth, during the final appraisal committee meeting, influential changes were made to the model that were not in line with the ERG's preferences (but were accepted by the appraisal committee). The company's revised and final incremental cost effectiveness ratio (ICER) in line with committee preferences resulted in fenfluramine dominating cannabidiol. Fenfluramine was recommended as an add-on to other antiepileptic medicines for treating seizures associated with DS in people aged 2 years and older in the National Health Service (NHS).