bluuebunny/crossref_metadata_2025_split huggingface dataset:
OP129 A Cost-Utility Analysis Of Denosumab (Prolia®) For Treating Osteoporosis In Postmenopausal Women: A Swiss Healthcare Payer Perspective
Authors: Stringer Danielle , Moshi Magdalena Ruth , Nicolopoulos Konstance , Jenal Mathias , Vreugdenburg Thomas , Ma Ning 
Published: December 2023

IntroductionOsteoporosis is characterized by decreased bone mass and density, increasing skeletal fragility, and the risk of fragility fracture. Fragility fractures are associated with a high economic burden. Denosumab (Prolia®) is a pharmacological therapy used to treat osteoporosis and reduce the risk of fragility fracture. This study aimed to assess the cost effectiveness of denosumab, compared with other pharmacological therapies (oral bisphosphonates, intravenous [IV] ibandronate, zoledronate, raloxifene, and bazedoxifene) and no treatment, for treating postmenopausal women with osteoporosis.MethodsA discrete event simulation model was developed using a lifetime time horizon. A Swiss healthcare payer perspective was adopted. Time-to-fracture distributions were derived from Swiss-specific Fracture Risk Assessment Tool (FRAX®) probabilities. Reductions in the risk of vertebral and nonvertebral fractures due to treatment were informed by a Bayesian network meta-analysis. Cost-effectiveness frontier analysis was utilized. Pairwise incremental cost-effectiveness ratios (ICERs) between denosumab and each comparator were also estimated. Sensitivity analyses were conducted to identify key drivers and explore the overall certainty of findings.ResultsAt a hypothetical willingness-to-pay (WTP) threshold of CHF100,000 (EUR101,630), IV ibandronate was the most cost-effective therapy in women aged 60 years who had a very high risk of fracture, and in women aged 70 or 80 years of any risk level. In women aged 60 years with a lower risk level, zoledronate was the most cost-effective option. Nevertheless, ICERs from pairwise comparisons between denosumab and some comparators (no treatment, bazedoxifene, raloxifene, and/or zoledronate depending on the cohort’s age and risk profile) were below the hypothetical WTP threshold. Higher intervention costs, smaller reductions in the risk of hip fracture, and shorter duration of residual benefit associated with denosumab contributed to the high ICER values seen in pairwise comparisons with oral bisphosphonates (as a class) and IV ibandronate.ConclusionsThe present evaluation supported the cost effectiveness of denosumab against some, but not all, comparators. Nevertheless, these results should be interpreted cautiously in light of uncertainty in the true effect of treatments on fracture risk.
OP133 COVIDIAGNOSTIX: Health Technology Assessment For COVID-19 Serological Tests As Companion Diagnostics To Vaccination
Authors: Tomaiuolo Rossella , Resta Chiara Di , Derrico Pietro , Ritrovato Matteo , Banfi Giuseppe 
Published: December 2023

IntroductionIn scenarios of vaccine scarcity or the context of organizational complexity, it is necessary to define prioritization strategies for allocating vaccine in compliance with the criteria of equity and efficiency of health resources. The COVIDIAGNOSTIX project, based on health technology assessment (HTA), assessed the role of SARS-CoV-2 serological tests as a companion diagnostic in the definition of strategies for vaccine administration. To guarantee evidence support for health policy choices, two different strategies were analyzed: one based on administering the vaccine booster dose to the entire population (VACCINE strategy) and the other based on allocation criteria (TEST&VACCINE strategy).MethodsAn Italian multidisciplinary team conducted a decision-oriented HTA using a combination of the EUnetHTA Core Model and the multicriteria decision analysis model based on the Analytic Hierarchy Process.Moreover, the Department of HTA method was integrated with the Susceptible-Exposed-Infectious-Recovered model, appropriate modelling techniques, simulation, and quantification of uncertainty that considered the ability to reduce deaths and to contain the pandemic. After identifying the evaluation elements and the decision-making structure, the weights of the evaluation areas and key performance indicators were calculated. This is a constituent part of the mathematical model of data processing, as the Analytic Hierarchy Process was based on a structured questionnaire that compared the relative importance of the two elements on a qualitative scale (1=equal importance; 9=more important).ResultsThe processing of the scores attributed to the key performance indicators concerning all the evaluation domains resulted in a performance of 94 percent for the TEST&VACCINE strategy and 84 percent for the VACCINE strategy. The TEST&VACCINE strategy was the most advantageous in various scenarios due to the greater speed of response from an operational and economic point of view.ConclusionsThe assessment schemes defined by COVIDIAGNOSTIX (i.e., technologies, intended use, and settings), which highlight the characteristics that differentiate the tests from each other and guarantee a timely and appropriate evaluation, can be adapted to respond to similar health policy management situations.
OP134 Mapping The Health Of The Nation Outcomes Scale To The EQ-5D Using The Pharmacotherapy Monitoring And Outcome Survey
Authors: Kleijburg Anne , Wijnen Ben , Hollander Wouter Den , Evers Silvia , investigators PHAMOUS , Kroon Hans , Lokkerbol Joran 
Published: December 2023

IntroductionIn clinical practice, health outcomes relevant for economic evaluations, such as health-related quality of life (i.e., utilities), are not always available. However, algorithms can be developed to map available outcome measures to utilities. In the Netherlands, the Health of the Nations Outcomes Scores (HoNOS) is a physician-reported, disease-specific outcome measure frequently used for patients with severe mental illness for which no mapping algorithms for utilities currently exist. The aim of this study was to develop an algorithm to map responses on the HoNOS to EQ-5D utilities for use in economic evaluations.MethodsA dataset was obtained from the Pharmacotherapy Monitoring and Outcome Survey cohort study containing data from patients with psychotic disorders. The dataset contains EQ-5D-3L domain and HoNOS scores, the age and sex of patients, and additional demographics. Correlation between the EQ-5D-3L and HoNOS was evaluated. To derive mapping functions, least absolute shrinkage and selection operator (LASSO) regression and random forest algorithms were applied with various predictor variables using a machine learning approach, whereby data were split into separate training and test sets. Cross-validation was then used to compare the performance of different models using R-squared and the root mean square error (RMSE).ResultsA total of 2,111 patients were included in the study. Spearman’s correlation coefficients indicated a weak to moderate negative correlation of -0.31. Based on model performance metrics, LASSO models outperformed random forest models on the training set, where the model including all individual HoNOS items and the age and sex of patients showed the best overall performance with an RMSE of 0.237 and an R-squared of 0.218. When applied to the test set, this resulted in an R-squared of 0.233 and a mean absolute error of 0.177.ConclusionsThe HoNOS can be mapped onto EQ-5D-3L utilities with moderate predictive accuracy. The reported mapping algorithm may be sufficient to predict overall population utility scores for use in health economic evaluations but lacks accuracy for individual patient predictions.
OP135 Machine Learning And Cancer Registry: Evaluation Of The Effectiveness Of Case Coding
Authors: Viscosi Carmelo Ettore , Di Prima Alessia Anna , Torrisi Antonina , Torrisi Antonietta Alfia , Ferrante Margherita , Ragusa Rosalia 
Published: December 2023

IntroductionMachine learning (ML) algorithms are computational procedures that use pattern recognition and inference by learning from previously categorized documents to predict the category to which a new document belongs. The role of machine learning within cancer registries remains unclear given the lack of in-depth testing and guidance from health technology assessment (HTA) agencies. We evaluated the effectiveness of coding new cases through machine learning at the Integrated Cancer Registry.MethodsThe Integrated Cancer Registry covers the eastern area of Sicily in Italy, which has an annual average incidence of about 10,000 cases of malignant neoplasm. Potential new cancer cases were retrieved from pathology services and processed by pathologists who confirmed the neoplastic nature of supposed cases and specified the morphological type and location of the tumors. The current method involves identification by reading the free-text report when International Classification Diseases for Oncology information was not provided. We used the new Microsoft ML.Net Library, a framework developed in response to the challenge of facilitating machine learning pipeline utilization in large software applications. A total of 1,050,952 free-text pathology reports published from 2003 to 2018 were selected separately from all Sicilian pathology services and uploaded to machine learning software that explored eight binary classification algorithms.ResultsWe evaluated each algorithm’s performance by calculating metrics (the number of true positives, true negatives, false positives, and false negatives) from the classification procedure applied to the test dataset. The metrics used were accuracy, F1 score, and area under the receiver operating characteristic curve. With a test set of around 210,000 text diagnoses, each algorithm reached an F1 score of up to 95 percent.ConclusionsMachine learning algorithms capture relevant information about tumors from free-text pathology reports, optimizing the process and reducing waste. With the help of machine learning systems, cancer registries can provide more timely data for research and evaluation of all types of new cancer technologies (drugs, devices, radiology and radiotherapy equipment, diagnostic devices, robotic surgery, and vaccines).
OP137 A Qualitative Exploration Of The National Institute For Health And Care Excellence’s Impact On International Health Technology Assessment
Authors: Henderson Nadine , Brassel Simon , Oliver Edward , Gibbons Ilana , Allen Rachel , Largeron Nathalie , Garau Martina 
Published: December 2023

IntroductionThe National Institute For Health And Care Excellence (NICE) is widely acknowledged as a seminal health technology assessment (HTA) body, known for its transparent and accountable approach to decision-making. This research aimed to investigate the impact of NICE methodology and decisions on international HTA bodies. We sought to identify direct and indirect factors that may influence an international HTA body’s methods or outcomes. To the best of our knowledge, this is the first research to use a qualitative approach to understand the influence of NICE on other HTA bodies.MethodsWe conducted 13 semi-structured qualitative interviews with HTA and market access experts from industry and academia from nine countries (Brazil, Israel, Italy, Japan, Poland, Saudi Arabia, South Korea, Sweden, and the United Arab Emirates). The interview script was organized into three main sections: comparing NICE methods and processes with other HTA bodies; the impact of specific NICE decisions; and Likert scale questions (to allow for comparability of opinions).ResultsMost interviewees believed their local HTA body would consider NICE’s decision when evaluating a medicine. However, the way and extent to which NICE influences HTA varied across countries. The most common means of considering a NICE decision was as background information or context for an HTA evaluation. Generally, interviewees suggested that negative NICE decisions had more impact on local decision-making than positive decisions. Nine of the 13 interviewees agreed or strongly agreed that their country’s HTA body considers the decisions of other HTA bodies in their decision-making process. Eleven of the 13 interviewees agreed or strongly agreed that the development of their country’s HTA body methods and processes was influenced by NICE.ConclusionsNICE is perceived to be a seminal HTA body, with continued influence on HTA agencies in other countries. However, the mechanisms and extent of this influence varies considerably between countries. We suggest that implicit factors are likely to contribute more to NICE’s influence than individual decisions. Nevertheless, further research is needed to reveal these factors and increase efficiency in international HTA decision-making processes.
OP142 Progression Analysis Versus Traditional Methods To Quantify Slowing Of Disease Progression In Alzheimer’s Disease
Authors: Jönsson Linus , Ivkovic Milana , Handels Ron , Gustavsson Anders , León Teresa , Hahn-Pedersen Julie Hviid , Raket Lars Lau 
Published: December 2023

IntroductionNew statistical methodology, known as progression models for repeated measures (PMRM), can estimate the slowing of progression (percentage slowing or time delay) of Alzheimer’s disease from trial data on disease-modifying therapies. We compared the PMRM methodology with mixed models for repeated measures (MMRM) and Cox time-to-event analysis on simulated trial data with respect to their power and interpretability of estimates.MethodsTwo novel models were included: PMRM (estimating slowing of progression and allowing different rates across visits) and proportional-slowing PMRM. Clinical Dementia Rating (CDR) Sum of Boxes score and progression to dementia as assessed by CDR global score were the primary outcomes for MMRM/PMRM and the Cox model, respectively. Subject-level placebo arm trajectories were jointly simulated based on estimated CDR mean trajectories and joint temporal correlation structure of 538 amyloid-positive patients with mild cognitive impairment who met typical disease-modifying trial inclusion criteria from the Alzheimer’s Disease Neuroimaging Initiative study. Active arm trajectories were simulated to show an average 20 percent slowing of disease progression, compared with placebo, at each visit. We conducted 1,000 simulations across multiple scenarios, varying the number of patients per arm (200 to 700) and clinical trial duration (18 to 36 months).ResultsThe power of PMRM models was greater than that of MMRM, and much greater than that of the Cox model whose power never exceeded 45 percent. PMRM models accurately estimated the underlying treatment effect (median 20% slowed progression, which translated to a delay in progression of 5 and 7 months at trial durations of 24 and 36 months, respectively), unlike quantifications of the MMRM (median estimated 25% reduction in decline), and the Cox model (median estimated hazard ratio of 0.9).ConclusionsFor disease-modifying therapies, PMRM estimates may have a more intuitive clinical interpretation in terms of delayed progression than MMRM or Cox models and enable a description of the amount of time spent in less severe disease stages. Among all the methods studied, PMRM offered the best combination of interpretability and power.
OP144 Impact Of A Training Program For The General Population On Knowledge Of Aortic Valve Stenosis
Authors: Fernández-Barceló Carla , Vidal Bàrbara , Abbas Ismail , Gonzalez Paloma , Trilla Marc , Carreño Marta Sitges , Sampietro-Colom Laura 
Published: December 2023

IntroductionLimited knowledge of the symptomatology of aortic stenosis (AS) among the general population may delay diagnosis and have a major impact on morbidity and resource use. Training programs have often been advocated by the scientific community. The present study reported the results of an assessment of a training program for the general population.MethodsPatients who attended healthcare centers were asked to answer a questionnaire on their level of knowledge around AS. A cohort of patients without training (n=681) answered the questionnaire and a second cohort answered the questionnaire via phone 24 hours after training (n=197). Propensity score matching by sex and age was used to obtain a balanced sample between the two cohorts, giving a total study sample of 394 individuals (197 without training and 197 with training). A descriptive analysis was performed to compare differences in the level of knowledge between the two cohorts. Predictors of AS symptomatology were identified using multivariate logistic regression.ResultsThe trained cohort was more aware of AS disease than the untrained cohort (79% versus 31%, 95% confidence interval [CI]: 0.39, 0.56; p<0.001). They were also better at distinguishing the symptoms associated with AS (80% versus 43%, 95% CI: 0.28, 0.48, p<0.001) and were more aware of its severity (36% versus 12%; 95% CI: 0.16, 0.32, p<0.001). Moreover, the trained cohort were better at identifying symptoms that should make them consider visiting a doctor (76% versus 65%; 95% CI 0.02, 0.20, p<0.02). No differences were observed in level of concern regarding AS (8% versus 4%; 95% CI: -0.0046, 0.09, p=0.08).The trained people who were aware of AS (p=0.04) correctly classified AS as a valvular disease (p=0.025), would seek medical consultation when AS symptoms occurred (p=0.04), and were more likely to correctly detect AS symptoms.ConclusionsThe training program significantly improved the knowledge and awareness of AS in the general population. This can improve the timeliness of AS diagnosis, reducing the health and economic burden of AS for the healthcare system.
OP153 Access To Real-World Data For Use In Health Technology Assessment – Still Work To Be Done
Authors: Ritz Christian , Kim Hansoo 
Published: December 2023

IntroductionReal-world data (RWD) is an important source of evidence for health technology assessment (HTA). It is widely used to fill clinical trial data gaps and to inform risk-sharing agreements. HTA is mandatory in many jurisdictions as it is used for price negotiation between a manufacturer and a payer. HTA practitioners have so far had limited involvement in the debate surrounding access to RWD as regulators have primarily focused on scientific research and market authorization. This study examined the challenges of obtaining RWD for HTA decision-making that is beneficial at the population level when data sources are restricted to maintain the data integrity and rights of the public.MethodsTypes of RWD and processes for obtaining data were assessed for two jurisdictions (Australia and Denmark). Types of data considered were national registries, ongoing or completed cohorts, surveys at various universities, archived historical data, and medical claims data. The assessment was performed by analyzing a series of cases.ResultsThere were similarities and differences between the two jurisdictions. In both jurisdictions the process for obtaining data included an ethics application as well as data handling fees. Patients and clinicians had little to no say in what their data are used for. It can take up to six months to obtain data. Person identification numbers enable linking of different datasets. Population wide data are accessible in Denmark only through secure servers, whereas full data sets, such as prescription data, can be released for research in Australia. Public hospital data, such as electronic health records, are not easily obtained in Denmark. In Australia, public hospitals are run by individual states and, therefore, additional effort is required to access nationwide data.ConclusionsAccess to RWD for HTA is challenging in both Australia and Denmark. Improvements in the process of applying for data and linking different data sources for HTA purposes are still needed.
OP167 An Analysis Of Medication Exclusion Reports In The Health Technology Assessment Process For The Brazilian Unified Health System
Authors: da Costa Nathalia , Borges Stefani , Pires Wolney , Rodrigues Fernanda , Louly Priscila , Prado Clementina , Santos Vania 
Published: December 2023

IntroductionThe National Committee for Health Technology Incorporation of the Brazilian Public Health System (Conitec), which was created in 2011 by federal law, defines the criteria for incorporating and excluding health technologies in the Brazilian health system. For technology evaluation proposals a recommendation report is carried out, which finds the best scientific evidence about the efficacy, accuracy, effectiveness, and safety of the technology being analyzed. The report also provides a comparative economic evaluation of the technologies already in the health system to promote and protect public health, achieve the best allocation of resources, and reduce regional inequalities.MethodsThis exploratory, descriptive, and retrospective study aimed to identify the criteria considered for the exclusion of medicines within the Brazilian health system from 2012 to November 2022. Data were extracted from Conitec’s website into a specific extraction form and analyzed using descriptive statistics.ResultsDuring the evaluation period, 763 technology recommendations by Conitec were identified, of which 75 (10%) related to the exclusion of medicines. Several criteria were identified among the exclusion recommendations, including protocol updates, expiry or lack of registration in the National Health Surveillance Agency, and drugs whose clinical indication was not included in the list of drugs registered for treatments in the health system.ConclusionsAlthough there is no standardization of criteria in Conitec for recommendations on the exclusion of health technologies, this study contributes to a better understanding of these technologies. Disinvestment of technologies positively impacted the population because it resulted in better allocation of resources in the health system, and some of the excluded technologies presented a greater risk of interactions and undesirable adverse reactions.