Artificial-Intelligence Cloud-Based Platform to Support Shared Decision-Making in the Locoregional Treatment of Breast Cancer: Protocol for a Multidimensional Evaluation Embedded in the CINDERELLA Clinical Trial.

BACKGROUND: Shared decision-making (SDM) plays a crucial role in breast cancer care by empowering patients and reducing decision regret. Patient decision aids (PtDAs) are valuable tools for facilitating SDM, now available in digital and artificial intelligence (AI)-powered formats to offer increasingly personalized contents. The ongoing CINDERELLA clinical trial (ClinicalTrials.gov: NCT05196269) evaluates an innovative AI cloud-based approach using a web platform and a mobile application (CINDERELLA APProach) versus the conventional approach to support SDM in breast cancer patients undergoing locoregional treatment. This protocol outlines a trial-based multidimensional evaluation, encompassing economic, financial, implementability, and environmental considerations associated with the CINDERELLA APProach. METHODS: A within-trial cost-consequence and cost-utility analysis from a societal perspective will be performed using patient-level data on outcomes and resource use. The latter will be valued in monetary terms using country-specific unit costs or patient valuations. A budget impact analysis will be performed over 1 and 5 years from the budget holder perspectives. The CINDERELLA APProach implementability will be assessed through an evaluation of its usability, acceptability, organizational impact, and overall feasibility. The environmental impact will be quantitatively assessed across several dimensions, such as quantity, appropriateness, and emissions, supplemented by qualitative insights. Overall, data for the evaluation will be gathered from patient questionnaires, interviews with patients and managers, focus groups with healthcare professionals, and app electronic data. DISCUSSION: A thorough understanding of the broad consequences of the CINDERELLA APProach may foster its successful translation into real-world settings, hopefully benefiting breast cancer patients and clinical practice.

Sample size in multistakeholder Delphi surveys: at what minimum sample size do replicability of results stabilize?

BACKGROUND AND OBJECTIVE: The minimum sample size for multistakeholder Delphi surveys remains understudied. Drawing from three large international multistakeholder Delphi surveys, this study aimed to: 1) investigate the effect of increasing sample size on replicability of results; 2) assess whether the level of replicability of results differed with participant characteristics: for example, gender, age, and profession. METHODS: We used data from Delphi surveys to develop guidance for improved reporting of health-care intervention trials: SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) extension for surrogate end points (n = 175, 22 items rated); CONSORT-SPI [CONSORT extension for Social and Psychological Interventions] (n = 333, 77 items rated); and core outcome set for burn care (n = 553, 88 items rated). Resampling with replacement was used to draw random subsamples from the participant data set in each of the three surveys. For each subsample, the median value of all rated survey items was calculated and compared to the medians from the full participant data set. The median number (and interquartile range) of medians replicated was used to calculate the percentage replicability (and variability). High replicability was defined as ≥80% and moderate as 60% and <80% RESULTS: The average median replicability (variability) as a percentage of total number of items rated from the three datasets was 81% (10%) at a sample size of 60. In one of the datasets (CONSORT-SPI), a ≥80% replicability was reached at a sample size of 80. On average, increasing the sample size from 80 to 160 increased the replicability of results by a further 3% and reduced variability by 1%. For subgroup analysis based on participant characteristics (eg, gender, age, professional role), using resampled samples of 20 to 100 showed that a sample size of 20 to 30 resulted to moderate replicability levels of 64% to 77%. CONCLUSION: We found that a minimum sample size of 60-80 participants in multistakeholder Delphi surveys provides a high level of replicability (≥80%) in the results. For Delphi studies limited to individual stakeholder groups (such as researchers, clinicians, patients), a sample size of 20 to 30 per group may be sufficient.

The development of an archive of patient-reported outcome measures (PROMs) in oncology: The Italian PRO4All project.

BACKGROUND: Choosing the most adequate measure of patient-reported outcomes (PROs) for a specific medical condition is not straightforward. This study aimed to develop a comprehensive archive of patient-reported outcome measures (PROMs), observer-reported outcome measures (ObsROMs) and caregiver-reported outcome measures (CROMs) in oncology and identify their main characteristics and target outcome domains. MATERIALS AND METHODS: As part of the Italian PRO4All Project, we retrieved questionnaires through an extensive search of online databases. We developed a data extraction form to collect information on cancer type, questionnaire variant(s), recall period, and scoring system. We performed a content analysis of the questionnaires to assign each item a specific outcome domain according to a predefined 38-item taxonomy. RESULTS: A total of 386 PROMs (n = 356), ObsROMs (n = 13) and CROMs (n = 17) were identified and described; of these, 358 were also analyzed in their content. 47.3 % of the instruments were cancer type-specific, 45.1 % were generic for cancer and 7.9 % were developed for the general population but also recommended in oncology. The great majority (92.2 %) were patient-reported. In 50.3 % the recall period was "last week". The mean number of items per questionnaire was 22.0 (range: 1-130). 7794 items were assigned an outcome domain, the most frequent being emotional functioning/wellbeing (22 %), physical functioning (15.7 %), general outcomes (10.1 %) and delivery of care (8.9 %). CONCLUSIONS: There are a variety of patient and caregiver-reported measures in oncology. This archive can guide researchers and practitioners in selecting the most suitable measures and fostering a patient-centered approach in clinical trials, clinical practice, and regulatory activities.

Mapping Payment and Pricing Schemes for Health Innovation: Protocol of a Scoping Literature Review.

INTRODUCTION: Innovative pricing and payment/reimbursement schemes have been proposed as one part of the solution to the problem of patient access to new health technologies or to the uncertainty about their long-term effectiveness. As part of a Horizon Europe research project on health innovation next generation pricing and payment models (HI-PRIX), this protocol illustrates the conceptual and methodological steps related to a scoping review aiming at investigating nature and scope of pricing and payment/reimbursement schemes applied to, or proposed for, existing or new health technologies. METHODS: A scoping review of literature will be performed according to the PRISMA guidelines for scoping reviews (PRISMA-ScR) guidelines. The search will be conducted in three scientific databases (i.e., PubMed, Web of Science, and Scopus), over a 2010-2023 timeframe. The search strategy is structured around two blocks of keywords, namely "pricing and payment/reimbursement schemes," and "innovativeness" (of the scheme type or scheme use). A simplified search will be replicated in the gray literature. Studies illustrating pricing and payment/reimbursement schemes with a sufficient level of details to explain their characteristics and functioning will be deemed eligible to be considered for data synthesis. Pricing and payment/reimbursement schemes will be classified according to several criteria, such as their purpose, nature, governance, data collection needs, and foreseen distribution of risk. The results will populate a publicly available online tool, the Pay-for-Innovation Observatory. DISCUSSION: The findings of this review have the potential to offer a comprehensive toolkit with a variety of pricing and payment schemes to policymakers and manufacturers facing reimbursement and access decisions.

Associations Between Surrogate Markers and Clinical Outcomes for Nononcologic Chronic Disease Treatments.

Importance: Surrogate markers are increasingly used as primary end points in clinical trials supporting drug approvals. Objective: To systematically summarize the evidence from meta-analyses, systematic reviews and meta-analyses, and pooled analyses (hereafter, meta-analyses) of clinical trials examining the strength of association between treatment effects measured using surrogate markers and clinical outcomes in nononcologic chronic diseases. Data sources: The Food and Drug Administration (FDA) Adult Surrogate Endpoint Table and MEDLINE from inception to March 19, 2023. Study Selection: Three reviewers selected meta-analyses of clinical trials; meta-analyses of observational studies were excluded. Data Extraction and Synthesis: Two reviewers extracted correlation coefficients, coefficients of determination, slopes, effect estimates, or results from meta-regression analyses between surrogate markers and clinical outcomes. Main Outcomes and Measures: Correlation coefficient or coefficient of determination, when reported, was classified as high strength (r ≥ 0.85 or R2 ≥ 0.72); primary findings were otherwise summarized. Results: Thirty-seven surrogate markers listed in FDA's table and used as primary end points in clinical trials across 32 unique nononcologic chronic diseases were included. For 22 (59%) surrogate markers (21 chronic diseases), no eligible meta-analysis was identified. For 15 (41%) surrogate markers (14 chronic diseases), at least 1 meta-analysis was identified, 54 in total (median per surrogate marker, 2.5; IQR, 1.3-6.0); among these, median number of trials and patients meta-analyzed was 18.5 (IQR, 12.0-43.0) and 90 056 (IQR, 20 109-170 014), respectively. The 54 meta-analyses reported 109 unique surrogate marker-clinical outcome pairs: 59 (54%) reported at least 1 r or R2, 10 (17%) of which reported at least 1 classified as high strength, whereas 50 (46%) reported slopes, effect estimates, or results of meta-regression analyses only, 26 (52%) of which reported at least 1 statistically significant result. Conclusions and Relevance: Most surrogate markers used as primary end points in clinical trials to support FDA approval of drugs treating nononcologic chronic diseases lacked high-strength evidence of associations with clinical outcomes from published meta-analyses.

Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials.

BACKGROUND: Self-management is pivotal in addressing noncommunicable diseases, such as diabetes. The increased availability of digital behaviour change interventions (DBCIs) delivered through mobile health apps offers unprecedented opportunities to enhance self-management and improve health outcomes. However, little is known about the characteristics of DBCIs for diabetes that significantly impact glycaemic control. Therefore, our systematic review with meta-analysis aimed to summarize characteristics and behaviour change components in DBCIs for diabetes self-management and explore potential associations with metabolic outcomes. METHODS: A systematic search was conducted in PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scopus to identify randomized controlled trials published until November 2023. The main outcome variable was the change in the mean difference of HbA1c levels between baseline and follow-up across intervention and control groups. Random-effects meta-regression was used to explore variation in glycaemic control as a function of prespecified characteristics of study designs and app interventions. FINDINGS: A total of 57 studies was included in the analysis, showing a statistically significant percentage point reduction in HbA1c for the intervention group compared to the control arm (-0.36, 95% CI = -0.46 to -0.26, p < 0.001). The inclusion of "self-monitoring of behaviour" as a behaviour change technique (ß = -0.22, p = 0.04) and "taking medication" as a target behaviour (ß = -0.20, p = 0.05) was associated with improved metabolic outcomes. INTERPRETATION: Our analyses endorse the use of diabetes self-management apps, highlighting characteristics statistically associated with intervention effectiveness and guiding the design of more effective DBCIs. FUNDING: This project received funding from the European Union's Horizon 2020 programme.

Colonoscopy vs the Fecal Immunochemical Test: Which is Best?

Although there is no debate around the effectiveness of colorectal cancer screening in reducing disease burden, there remains a question regarding the most effective and cost-effective screening modality. Current United States guidelines present a panel of options that include the 2 most commonly used modalities, colonoscopy and stool testing with the fecal immunochemical test (FIT). Large-scale comparative effectiveness trials comparing colonoscopy and FIT for colorectal cancer outcomes are underway, but results are not yet available. This review will separately state the "best case" for FIT and colonoscopy as the screening tool of first choice. In addition, the review will examine these modalities from a health economics perspective to provide the reader further context about the relative advantages of these commonly used tests.

Surrogacy and the Valuation of ATMPs: Taking Our Place in the Evidence Generation/Assessment Continuum.

Medical technology is advancing rapidly, but established methods for health technology assessment are struggling to keep up. This challenge is particularly stark for the assessment of advanced therapy medicinal products-therapies often launched on the basis of single-arm studies powered to a surrogate primary endpoint. The most robust surrogacy methods investigate trial-level correlations between the treatment effect on the surrogate and the outcome of ultimate interest. However, these methods are often impossible with the evidence usually available for advanced therapy medicinal products at the time of the launch (randomized controlled trials are necessary for these advanced methods). Additionally, these surrogacy relationships are usually considered to be technology specific, adding uncertainty for any approach that primarily relies on historic data to estimate the surrogacy relationship for novel interventions such as advanced therapy medicinal products. The literature has already highlighted the need for early dialogue, staged assessment processes, and pricing arrangements that responsibly share the risk between the manufacturer and payer. However, it is our view that in addition to these critical developments, the modeling methods employed could also improve. Currently, health technology assessment practitioners typically either ignore the surrogate and simply extrapolate the endpoint of greatest patient relevance irrespective of the degree of maturity or assume historic surrogate relationships apply to the novel technology. In this opinion piece, we outline an additional avenue. By drawing on the understanding of the mechanism of action and insights generated earlier in the evidence generation/assessment continuum, cost-effectiveness modelers can make better use of the wider data available. These efforts are expected to reduce uncertainty at the time of the initial launch of pharmaceutical products and increase the value of subsequent data collection efforts.

Surrogate endpoints: a key concept in clinical epidemiology.

Surrogate endpoints are biomarkers or intermediate outcomes that are used as substitutes for clinical outcomes of interest, often to expedite research or decision-making. In contrast, patient-important (or patient-centered) outcomes are health outcomes that are of direct relevance and importance to patients themselves; clinical trials may have measured the impact of the intervention on other endpoints related to, but different from, those of primary importance to patients. This article aims to elaborate on the use and understanding of surrogate endpoints. There should be a well-understood and scientifically grounded relationship between the surrogate (replacement) and the patient-important (target) endpoint it is intended to represent. It should be biologically plausible that changes in the surrogate will consistently and predictably reflect changes in the patient-important endpoint. The surrogate endpoint should show a threshold effect, meaning that a specific change (or state) in the surrogate with an intervention (relative to the comparator) is associated with a predictable (change in the) patient-important outcome. This helps establish a meaningful cutoff or target for the treatment effect on the surrogate endpoint. While surrogate endpoints offer advantages in certain situations, it is important to remember that their use requires careful validation to ensure they reliably predict the true clinical outcome. The validity of "surrogate endpoints" should be supported by robust scientific evidence and rigorous evaluation before these can be considered and labeled as surrogate endpoints.

A framework for the definition and interpretation of the use of surrogate endpoints in interventional trials.

Background: Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials. Methods: As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials. Findings: Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)-thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators. Interpretation: There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence. Funding: SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded.

The Assessment of Patient-Reported Outcomes for the Authorisation of Medicines in Europe: A Review of European Public Assessment Reports from 2017 to 2022.

OBJECTIVES: Health regulators have progressively increased their attention and focus on patient-reported outcomes (PROs), driven by the diffusion of a patient-centred approach to the drug development process. This study investigates the consideration of PROs and their measures (PROMs) in the authorisation of medicines in Europe. METHODS: All medicines for human use authorised or refused by the European Medicines Agency (EMA) in the period 2017-2022 were identified, and corresponding European Public Assessment Reports (EPARs) were downloaded for review. Medicine and PROs/PROM characteristics were systematically recorded. A multivariate logistic regression was performed to identify variables associated with the use of patient-reported evidence in EPARs. RESULTS: Overall, 497 EPARs of authorised medicines and 19 EPARs of refused medicines were analysed; of these, 240 (48.3%) and 10 (52.6%), respectively, reported any use of PROs/PROMs (p = 0.710). For authorised medicines, the likelihood of using PROs/PROMs was negatively affected by generic (OR = 0.01, p < 0.001) and biosimilar status (OR = 0.46, p = 0.013) and positively affected by orphan status (OR = 1.41, p = 0.177). The use of PROMs (50.6% in 2017 vs 47.9% in 2022) did not show a clear pattern over the 6-year period considered (p = 0.758) and was particularly uncommon in some therapeutic areas (e.g., 15.2% in infectious diseases). A total of 816 dyads of PROs/PROMs were identified. On average each EPAR considered 1.6 (range: 0-14) instruments. Patient-reported outcomes were typically secondary (53.3%) and exploratory endpoints (18.8%); in one-third of cases (32.5%), they assessed generic quality of life. Among the PROMs, 227 (27.8%) targeted general population; EQ-5D (11.0%), SF-36/SF-12 (5.9%) and EORTC QLQ-C30 (5.6%) were the instruments most frequently used. CONCLUSIONS: This study suggests PROs/PROMs are considered in less than half of total medicine assessments and even more rarely in some disease areas. The adoption of PROs is key in EMA strategy to 2025 and would be facilitated by consensus development on their measures and optimisation of data collection.

Evaluating the ability of an artificial-intelligence cloud-based platform designed to provide information prior to locoregional therapy for breast cancer in improving patient's satisfaction with therapy: The CINDERELLA trial.

BACKGROUND: Breast cancer therapy improved significantly, allowing for different surgical approaches for the same disease stage, therefore offering patients different aesthetic outcomes with similar locoregional control. The purpose of the CINDERELLA trial is to evaluate an artificial-intelligence (AI) cloud-based platform (CINDERELLA platform) vs the standard approach for patient education prior to therapy. METHODS: A prospective randomized international multicentre trial comparing two methods for patient education prior to therapy. After institutional ethics approval and a written informed consent, patients planned for locoregional treatment will be randomized to the intervention (CINDERELLA platform) or controls. The patients in the intervention arm will use the newly designed web-application (CINDERELLA platform, CINDERELLA APProach) to access the information related to surgery and/or radiotherapy. Using an AI system, the platform will provide the patient with a picture of her own aesthetic outcome resulting from the surgical procedure she chooses, and an objective evaluation of this aesthetic outcome (e.g., good/fair). The control group will have access to the standard approach. The primary objectives of the trial will be i) to examine the differences between the treatment arms with regards to patients' pre-treatment expectations and the final aesthetic outcomes and ii) in the experimental arm only, the agreement of the pre-treatment AI-evaluation (output) and patient's post-therapy self-evaluation. DISCUSSION: The project aims to develop an easy-to-use cost-effective AI-powered tool that improves shared decision-making processes. We assume that the CINDERELLA APProach will lead to higher satisfaction, better psychosocial status, and wellbeing of breast cancer patients, and reduce the need for additional surgeries to improve aesthetic outcome.

Implementing shared decision-making interventions in breast cancer clinical practice: a scoping review.

BACKGROUND: Shared decision-making (SDM) is a collaborative process whereby patients and clinicians jointly deliberate on the best treatment option that takes into account patients' preferences and values. In breast cancer care, different treatment options have become available to patients in the last decade. Various interventions, including patient decision aids (PtDAs), have been designed to promote SDM in this disease area. This study aimed at investigating the factors that influence the successful adoption and implementation of SDM interventions in real-world healthcare delivery settings. METHODS: A scoping review of scientific and grey literature was conducted for the period 2006-2021 to analyse the support for SDM interventions and their adoption in breast cancer clinical practice. The interpretation of findings was based on the Practical, Robust Implementation and Sustainability Model (PRISM) for integrating research findings into practice. RESULTS: Overall, 19 studies were included for data synthesis, with more than 70% published since 2017. The availability of SDM tools does not automatically translate into their actual use in clinical settings. Factors related to users' co-creation, the clinical team's attitude and knowledge, organisational support and regulatory provisions facilitate the adoption of SDM interventions. However, overlooking aspects such as the re-organisation of care pathways, patient characteristics, and assigning of resources (human, financial, and facilities) can hinder implementation efforts. CONCLUSIONS: Compared to the mounting evidence on the efficacy of SDM interventions, knowledge to support their sustained implementation in daily care is still limited, albeit results show an increasing interest in strategies that facilitate their uptake in breast cancer care over time. These findings highlight different strategies that can be used to embed SDM interventions in clinical practice. Future work should investigate which approaches are more effective in light of organisational conditions and external factors, including an evaluation of costs and healthcare system settings.

Implementing preconception expanded carrier screening in a universal health care system: A model-based cost-effectiveness analysis.

PURPOSE: The limited evidence available on the cost-effectiveness (CE) of expanded carrier screening (ECS) prevents its widespread use in most countries, including Italy. Herein, we aimed to estimate the CE of 3 ECS panels (ie, American College of Medical Genetics and Genomics [ACMG] Tier 1 screening, "Focused Screening," testing 15 severe, highly penetrant conditions, and ACMG Tier 3 screening) compared with no screening, the health care model currently adopted in Italy. METHODS: The reference population consisted of Italian couples seeking pregnancy with no increased personal/familial genetic risk. The CE model was developed from the perspective of the Italian universal health care system and was based on the following assumptions: 100% sensitivity of investigated screening strategies, 77% intervention rate of at-risk couples (ARCs), and no risk to conceive an affected child by risk-averse couples opting for medical interventions. RESULTS: The incremental CE ratios generated by comparing each genetic screening panel with no screening were: -14,875 ± 1,208 €/life years gained (LYG) for ACMG1S, -106,863 ± 2,379 €/LYG for Focused Screening, and -47,277 ± 1,430 €/LYG for ACMG3S. ACMG1S and Focused Screening were dominated by ACMG3S. The parameter uncertainty did not significantly affect the outcome of the analyses. CONCLUSION: From a universal health care system perspective, all the 3 ECS panels considered in the study would be more cost-effective than no screening.