Andrea Manca

Background and aims: Long‐term strong opioid use for chronic non‐malignant pain (CNMP) represents a significant public health challenge, associated with reduced health‐related quality of life (HRQoL) and increased mortality. The Improving the Wellbeing of people with Opioid Treated CHronic pain (I‐WOTCH) trial demonstrated that a multicomponent intervention supporting opioid withdrawal in people with CNMP successfully reduced opioid consumption compared with best usual care (BUC), without adversely affecting perceived pain levels. This study aimed to assess the value‐for‐money of the strategies tested in the I‐WOTCH trial, from the perspective of the United Kingdom National Health Service (NHS) and Personal and Social Services. Design, setting and participants: Within‐trial cost‐consequence (CCA) and model‐based cost‐effectiveness analyses (CEA), over one year and lifetime horizons, respectively. Data were sourced from the I‐WOTCH trial, which recruited 608 participants from 191 primary care centres in England between May 2017 and January 2019 and followed them up for up to 12 months. Participants received either BUC or 3 group sessions (lasting one day each) led by a trained intervention nurse and by a lay person with CNMP and lived experience with opioid tapering. These sessions, designed to develop self‐management skills, were supplemented by ongoing one‐to‐one support from nursing and lay personnel during follow‐up. We used a probabilistic state‐transition model to predict expected quality‐adjusted life years (QALYs) and costs (in UK £) of each strategy over the lifetime of an individual. Model input parameters were derived mostly from the I‐WOTCH trial. Opioid‐related excess mortality and fracture rates were extracted from the literature. Measurements: Individual‐level healthcare resource use and generic HRQoL [measured using the EuroQOL five dimensions questionnaire (Equation 5D)] were collected at baseline, 4, 8 and 12 months, and analysed to estimate total costs and health consequences for the I‐WOTCH and BUC groups. Findings: I‐WOTCH had higher costs and similar HRQoL outcomes compared with BUC at 12 months. When accounting for long‐term reductions in mortality and morbidity from decreased exposure to strong opioids, our long‐term model predicted I‐WOTCH to yield higher costs and higher QALYs than BUC over the patient's lifetime, resulting in a base case incremental cost‐effectiveness ratio of £29 543/QALY (2019 prices). Results were sensitive to structural assumptions in the model (i.e. cohort starting age, treatment effect weaning rate). Our probabilistic sensitivity analysis found I‐WOTCH to have a 50% probability of being cost‐effective compared with BUC for willingness‐to‐pay thresholds ranging between 0 and £100 000/QALY. Conclusions: The Improving the Wellbeing of people with Opioid Treated CHronic pain intervention supporting opioid withdrawal in people with chronic non‐malignant pain appears to be cost‐effective on average, but further research is warranted to reduce current levels of decision uncertainty.

Background and Aims: Lower income European countries (LIECs) have more limited financial resources to cover high‐cost technologies in rare diseases than higher income European countries (HIECs). Our study explores how treatment recommendations in myelodysplastic syndrome (MDS) can be supported in LIECS by transferring real‐world evidence (RWE) generated by target trial emulation (TTE) method in HIECs. Method: In the HTx project transferability aspects of the MDS case study were considered upfront. HTA agency consortium partners set expectations for the MDS case study team on how to integrate the new TTE methodology into the routine work of HTA bodies. In consecutive workshops consortium members and external HTA experts identified the main challenges of transferring evidence generated by TTE method to LIECs and made conclusions on how to overcome these challenges. Results: The lack of local real‐world data before making reimbursement decisions is an important challenge to apply the TTE method to LIECs. Differences in patient pathways and comparator technologies, limited expertise and resources for adapting international HTA methods are significant barriers of transferring RWE from other countries. Still, transferring RWE to LIECs from other countries based on the TTE methodology represents an improvement to the current standard HTA methods, especially if joint clinical assessment provides the unbiased judgement on the relative effectiveness of orphan medicines. The TTE approach also provides an opportunity to LIECs to judge the value of high‐cost technologies for different patient subgroups. However, HTA professionals in LIECs need training about advanced methodologies. Conclusion: This is the first study to explore how RWE generated by the TTE method can be transferred to optimize treatment decisions of patients with a rare disease in countries with limited HTA capacities. Five general concluding statements were made on the novelty of the TTE method and on how to overcome main challenges of transferring TTE results to HTA systems in LIECs.

Introduction The implantable cardioverter defibrillator (ICD) is a cardiac device recommended for use to prevent the occurrence of sudden cardiac death (SCD) in post-myocardial infarction (MI) patients with reduced left ventricular ejection fraction (LVEF). The evidence informing this guidance comes from landmark trials that are now more than 20 years old. The risk-benefit profile of ICD for the contemporary target population may have changed substantially since then, which raises the question of whether there is evidence for sparing patients a procedure associated with potentially severe complications and high healthcare costs. A main part of the PRevention Of sudden cardiac death aFter myocardial Infarction by Defibrillator implantation (PROFID) project is the PROFID EHRA trial, which is supported by the European Heart Rhythm Association. PROFID EHRA is a European Union-funded, prospective, randomised, multi-centre, non-inferiority study designed to compare optimal medical therapy (OMT) alone to ICD with OMT, for post-MI patients with reduced LVEF. The study also describes economic evaluation methods to quantify the cost and health implications of using OMT alone in place of ICD implantation plus OMT in this group of patients. Methods and analysis The economic evaluation has been designed to conduct a pre-trial cost-effectiveness analysis (CEA) prior to the availability of trial data, followed by a within-trial cost-consequences analysis (CCA) and a long-term post-trial CEA, conducted from the National Health Service and Personal Social Service perspective in England. The pre-trial CEA uses simulation modelling informed by available evidence to assess the lifetime costs and quality-adjusted life years of OMT alone and ICD+OMT in post-MI patients with reduced LVEF at risk of SCD, as defined in the PROFID EHRA trial. The within-trial CCA is intended to summarise the health-related quality of life (HRQoL), healthcare resource use and associated costs observed during the PROFID EHRA trial follow-up period. The post-trial CEA updates the pre-trial model by incorporating contemporary evidence about the HRQoL and costs observed during the trial and the occurrence of those events and outcomes accruing during the trial follow-up period and projecting them into the expected lifetime of the patients. Sensitivity analyses are performed to assess the robustness of the CEA results with respect to both model assumptions and uncertainty in the value of the model input parameters. Finally, a value of information analysis will identify the key drivers of uncertainty surrounding the model conclusions regarding the optimal treatment strategy, establishing if further research may be required. Ethics and dissemination The PROFID EHRA trial, under legal sponsorship of Charité—Universitätsmedizin Berlin, Germany, received its first ethics approval by the Medicine Research Ethics Committee of the La Paz University Hospital in Madrid, Spain (reference number LHS-2019-0209). Before including patients, for all participating study centres, the required local, central and/or national ethical approval has to be obtained. As of the date 13 November 2025, at least one participating study centre in the following countries has received ethical approvals from relevant ethics committees: Austria, Belgium, Czech Republic, Denmark, France, Germany, Great Britain, Hungary, Israel, the Netherlands, Poland and Spain. Results will be shared with the general public through various media channels and additionally with healthcare professionals and the scientific community through scientific meetings, conferences and publications. Trial registration number NCT05665608.

Introduction: RaCeR 2 is a pragmatic multicentre, open-label, randomised controlled trial, with full economic evaluation. The primary aim is to assess whether individualised (early) patient-directed rehabilitation (EPDR) results in less shoulder pain and disability at 12 weeks postrandomisation following surgical repair of full-thickness tears of the rotator cuff of the shoulder compared with the current standard (delayed) rehabilitation. This paper provides the protocol for the RaCeR 2 health economic evaluation. Methods and analysis: The health economic analysis of RaCeR 2 is made up of three phases: (1) development of an initial state-transition model structure, (2) within-trial cost consequence analysis and (3) long-term model-based cost-effectiveness analysis (CEA) from the National Health Service and Personal Social Service perspective in England. Descriptive statistics (eg, mean, standard deviation, 95% confidence intervals and minimum and maximum values) will be reported for within-trial resource use, costs and health-related quality of life (HRQoL). Health state-specific costs and HRQoL will be estimated using regression model approaches and used to inform a state-transition simulation model designed to quantify the long-term costs and quality-adjusted life years (QALYs) experienced by patients over the model’s time horizon. Where appropriate, final CEA model results will be reported as cost per QALY gained for individualised EPDR versus standard (delayed) rehabilitation. Model assumptions and overall parameter uncertainty will be tested using probabilistic sensitivity analysis and scenario analyses. All regression analyses will be adjusted for baseline participant demographic and symptomatic characteristics. Ethics and dissemination: A favourable ethical review was granted by London-Stanmore Research Ethics Committee (23/LO/0195) on 13 April 2023. Findings will be disseminated in peer-reviewed journals, at scientific conferences, and via the study website. Trial registration number: NCT11499185

Background: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients’ observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. Methods: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. Results: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. Limitations: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. Conclusions: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. Implications: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. Highlights: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies. ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes. Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions. Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment–like decision making.

Introduction Despite the high number of operations and surgical advancement, rehabilitation after rotator cuff repair has not progressed for over 20 years. The traditional cautious approach might be contributing to suboptimal outcomes. Our aim is to assess whether individualised (early) patient-directed rehabilitation results in less shoulder pain and disability at 12 weeks after surgical repair of full-thickness tears of the rotator cuff compared with current standard (delayed) rehabilitation. Methods and analysis The rehabilitation after rotator cuff repair (RaCeR 2) study is a pragmatic multicentre, open-label, randomised controlled trial with internal pilot phase. It has a parallel group design with 1:1 allocation ratio, full health economic evaluation and quintet recruitment intervention. Adults awaiting arthroscopic surgical repair of a full-thickness tear are eligible to participate. On completion of surgery, 638 participants will be randomised. The intervention (individualised early patient-directed rehabilitation) includes advice to the patient to remove their sling as soon as they feel able, gradually begin using their arm as they feel able and a specific exercise programme. Sling removal and movement is progressed by the patient over time according to agreed goals and within their own pain and tolerance. The comparator (standard rehabilitation) includes advice to the patient to wear the sling for at least 4 weeks and only to remove while eating, washing, dressing or performing specific exercises. Progression is according to specific timeframes rather than as the patient feels able. The primary outcome measure is the Shoulder Pain and Disability Index total score at 12-week postrandomisation. The trial timeline is 56 months in total, from September 2022. Trial registration number ISRCTN11499185.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two‐stage network meta‐regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non‐randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta‐regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re‐analysis of a network of studies comparing three drugs for relapsing–remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non‐randomized evidence.

In network meta‐analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non‐randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta‐regression (NMR) models allowing for cross‐design and cross‐format synthesis. The models integrate a three‐level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias‐adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing–remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta‐regression showed that intervention efficacy decreases with increasing participant age. We also re‐analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re‐analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within‐study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics.

Background: Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. Objectives: Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). Methods: We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as “treat none” or “treat all patients with a specific treatment” strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. Results: We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the “treat patients according to the prediction model” strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. Conclusions: This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. Highlights: Decision curve analysis is extended into a (network) meta-analysis framework. Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials. Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined. This extension of decision curve analysis can be applied to (network) meta-analysis–based prediction models to evaluate their use to aid treatment decision making.

Objectives: Wearable digital health technologies (DHTs) have the potential to improve chronic kidney disease (CKD) management through patient engagement. This study aimed to investigate and elicit preferences of individuals with CKD toward wearable DHTs designed to support self-management of their condition.Methods: Using the results of our review of the published literature and after conducting qualitative patient interviews, five-choice attributes were identified and included in a discrete-choice experiment. The design consisted of 10-choice tasks, each comprising two hypothetical technologies and one opt-out scenario. We collected data from 113 adult patients with CKD stages 3–5 not on dialysis and analyzed their responses via a latent class model to explore preference heterogeneity.Results: Two patient segments were identified. In all preference segments, the most important attributes were the deviceappearance,format,andtypeof information provided. Patients within the largest preference class (70 percent) favored information provided in any format except the audio, while individuals in the other class preferred information in text format. In terms of the style of engagement with the device, both classes wanted a device that provides options rather than telling them what to do.Conclusions: Our analysis indicates that user preferences differ between patient subgroups, supporting the case for offering a different design of the device for different patients’ strata, thus moving away from a one-size-fits-all service provision. Furthermore, we showed how to leverage the information from user preferences early in the R&D process to inform and support the provision of nuanced person-centered wearable DHTs.