key: cord-0256561-qwsv9msh authors: SEANEHIA, J.; LAMPURE, A.; GORDEJUELA, R.; AMDOUNI, B.; MANZIONE, P.; MAMMAS, K. title: An observational analysis of patient recruitment in clinical trials in France using real-word database PMSI.French patient recruitment in clinical trials using PMSI date: 2022-04-10 journal: nan DOI: 10.1101/2022.04.05.22273463 sha: 4087a5caa593e481b8c142ccc42d1fb2ccf7b754 doc_id: 256561 cord_uid: qwsv9msh Background: France has significant clinical research and development potential, however, struggles in comparison to neighbouring countries. A significant reason is the difficulty to recruit patients, thus causing delays in the availability of new therapies to market. IQVIA uses Health Insurance Claims Data among other data assets, to better locate patients for trials based on the potential of hospitals. Objective: The aim of the study was to monitor whether an increased number of patients enrolled in clinical trials in France was observed when PMSI data supported patient recruitment, as well as describing clinical trial landscape worldwide and in Europe. Methods: We used data from ClinicalTrials.gov and Citeline to describe the clinical trial landscape in Europe between 2010 and 2019. We also looked at the IQVIA internal clinical trial tracker, Clinical Trial Management System (CTMS) to describe IQVIA-run trials and their performance after matching trials supported with PMSI data in France. We compared the average number of enrolled patients per site in PMSI and non-PMSI supported trials according to the study phase, using a Student t-test. Results: Results suggest that the support of PMSI on the average number of enrolled patients per site, when comparing at similar trial phase level, shows a positive trend especially for phase 4 studies (11.0 with PMSI vs 9.3 without PMSI, p=0.67), and for phases 3b, 3 and 1, when compared to non-PMSI supported studies. Conclusions: The findings of this study suggest that PMSI use has the potential to increase patient recruitment into clinical trials run in France, rendering France more attractive in its exploitation of the clinical research potential. Optimising patient recruitment has a direct impact on the availability and timeliness of innovative therapies to market for French patients. France's place in the commercial-sponsored clinical trial space has been reducing over a number of years according to the National Doctor's Academy Working Group, 1 despite a high concentration of highly qualified healthcare personnel with strong research capabilities. Some of the reasons for this trend as outlined by the working group, include strict regulations by government bodies, administrative bottlenecks and challenges in recruiting patients for trials in the expected timelines. The working group provided a range of recommendations, one of which is to reduce the burden and costs inefficiencies in patient recruitment. Improving these efficiencies can be achieved using data-driven patient recruitment means instead of the classic CRO approach, of relying mainly on declarative potential by investigators during outreach activities. Analyses of sites' enrolment performance vs estimates received from sites during feasibility outreach show that in feasibility assessments, most investigators overestimate their enrollment potential. Based on these overestimations, sites struggle to meet their enrolment targets upon activation, with some remaining non-enrollers throughout the course of the study. Sites struggling with recruitment delays or difficulty finding subjects impacts the power of studies in their ability to demonstrate significant effects. 2 Studies taking longer to run or getting discontinued, 3 are not only a source of cost and time inefficiencies, but consequently impact the public's access to innovative therapies. Even though, several hurdles still impede the patient recruitment process, there has been significant innovation in improving patient recruitment. Targeting these efficiencies in scattered data sources is the platform developed by Mudarankathan et al, 4 the Curated Cancer Clinical Outcomes Databases (C3OD) used to centralise the various data and facilitate its access. This platform is intended to speed patient . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint recruitment in oncology studies. Other recent efforts have succeeded in improving efficiencies in patient recruitment without limiting it to therapeutic area. One of such hospital specific information systems is the DARWIN Cohort Management System (DCMS) which identifies patients for genotype matched studies as described by Eubank et al. 5 Achieving efficiencies in patient identification and enrolment are underpinned by data and studies like Ni et al, 6 explore this potential by using natural language processing and machine learning techniques to match patients to trials. These patient selection approaches contribute to recruitment acceleration and enable rapid generation of valuable scientific research and outcomes to meet public health needs. However, these approaches are limiting when applied to hospitals that lack the patient potential. Opening sites with no patient potential remain costly and inefficient. IQVIA addresses this gap with a data driven approach in site selection. This task undertaken by a global team relies on local data sources, in-house technology, country-specific expertise, and robust analytics to provide insights on how best to meet this need and ensure that these often-multi-country trials are executed efficiently at the local level. One of such local data sources used in IQVIA is Health Insurance Claims Data. Similar to all industrialised countries who possess structured payment systems for healthcare costs management, 7 Malades, [GHM]), representing the average cost of managing these kinds of patients according to their diagnosis, comorbidities and treatment procedures during their hospital stay (Groupe Homogène de Séjour, [GHS] ). 10 This system allows hospitals to receive payment corresponding to their activity and healthcare services delivered. As the primary goal of the data collected with the PMSI system is to ensure hospital payments, patient data is anonymised and centralised at the national level, with the governing body in charge of hospital data, Agence Technique de l'Information sur l'Hospitalisation (ATIH) overseeing the process. 11 The current exploitation of this data goes beyond its primary purpose of hospital budget allocation, nevertheless, its use is seen in several medical research fields, and Boudemaghe et al. 8 outline a list of such research areas, which span various disease indications. 12, 13, 14 In 2019 the ATIH granted the R&D Business Unit within IQVIA, access to PMSI. The rigor that accompanies the authorisation to this database is ensured via a double authentication system, which guarantees patient data protection and governance. This process is managed by the body, Centre d'accès sécurisé aux données (CASD). 15 This database's coverage is extensive, and records patients' characteristics and comorbidities. The following information is available in PMSI database: 1) hospital data (entity identifier (Finess), geographical/legal entity identifier), 2) patient administrative information (age, gender, patient identifier), 3) patient medical information (primary diagnosis, related diagnosis and associated significant diagnosis (ICD10 codes), procedures, medical devices and drugs ("liste-en-sus")), and 4) The above-mentioned strengths of the French Claims data, as well as its ability to bridge the gap in the French clinical trial space, could help France become more attractive for industry-sponsored trials. In this study, we monitored whether an increased number of patients enrolled in clinical trials in France was observed when PMSI data supported patient recruitment, as well as describing clinical trial landscape worldwide and in Europe. First, the objective was to describe patient enrolment in IQVIA clinical trials, worldwide over the period 2010-2019, and focus into Europe, to compare contribution of the main European countries in the industry-sponsored clinical trial space (the latter accounting for 50% of trials in France). 16 Secondly, the objective was to monitor in France, the average number of patients enrolled by site in trials supported by PMSI data vs those not supported by PMSI data, according to the trial phase. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. We investigated the European clinical trials activity using two large clinical trial databases, ClinicalTrials.gov, 17 and Trialtrove from Citeline, 18 To test and validate the assumption that patient potential at hospitals based on their density, positively impact clinical research and development in France, the studies were then matched with those supported with PMSI data, following an extraction from an internal data repository of all studies supported by the R&D Business Unit. We performed descriptive analysis and hypothesis tests to monitor whether an increased number of subjects enrolled per site is observed in studies with PMSI support versus those without. Student's t-test was used to compare the means of patients enrolled per site, in studies supported by PMSI data vs those without, stratified by phase due to different methodologies involved. Data extracted from CTMS covered the period ranging from January 2009 till January 2021. During this period, 13,402 clinical trials at 1,186,396 sites were tracked in . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint To understand whether an increased number of patients enrolled is observed in trials supported with PMSI data, this analysis focused on studies and sites, both present in CTMS and PMSI databases, accounting for 47% of the available records in CTMS. We considered this as the reference database as it is used to derive the experiments described in this study. In addition, we performed quality control on the available data and records with missing values were excluded from the analysis. IQVIA's activity covered 10% of the French clinical trial market over the past 10 years, after analysis of the trial share of CT.gov and Citeline recorded trials attributed . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint The number of patients enrolled in clinical trials supported with PMSI data was investigated when the analysis was limited to studies supported in 2017 and 2018, and when analyses were carried out by phase. The timeline was limited to 2018 as studies supported after 2018 were mostly meant to recruit in 2020, a year which saw most clinical activity reduced or halted due to Covid-related-activity. The results seen from IQVIA's access to the PMSI data and its positive association with recruitment especially in phase 1,3, 3b and 4 could be useful in drafting recommendations to health authorities to guide their policy decision-making in a data-driven manner. This potential will help maintain access to this data which brings innovative therapies to Limitations PMSI provides information about patient volume, but this is only one of the parameters considered for site identification and site selection. There are additional important factors (e.g. clinical research experience, quality scores, etc) taken into consideration in the site selection process other than patient volume, which could dilute the impact that PMSI data has on the final site list. In this study we monitor 10 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint years of the clinical trial landscape, but we have only two and a half years of PMSI data availability. Finally, PMSI analytics only supports a fraction of IQVIA-awarded studies at that period. In addition, the low sample size and the lack of statistical power in the analyses does not allow us to perform an analysis at therapeutic area level. It could be interesting in a few years, with larger time coverage and sample size, to perform this additional analysis. Indeed, we could then assume that depending on the indication, PMSI support might be more necessary for some studies than other. CT.gov and Citeline databases enabled the investigation of the global trend in the clinical trial landscape. However, CT.gov and Citeline databases only provide information at the study level, unlike CTMS, they did not allow any comparison at the more granular site level regarding site activation dates, number of subjects enrolled by site, etc, which was the objective in this article. These two major databases have good coverage of past and ongoing clinical studies, whereas CTMS data is missing considerable data at certain data points possibly because these are secondary to the activity of the clinical team. As clinical trials are the main drivers in advancing innovation in patient care and treatment pathway, optimising patient recruitment has a direct impact on the availability and timeliness of innovative therapies to market. PMSI data allows this possibility, though the evidence so far shows this positive trend in phases 1,3, 3b and 4 studies. This knowledge on the phase-specific impacts could help in focusing on these specific trials, while looking at other solutions of patient recruitment improvement for the remaining phase. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint La place de la France dans les essais cliniques à promotion industrielle Recruiting minorities into clinical trials: toward a participant-friendly system Patient recruitment: US perspective A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials Automated eligibility screening and monitoring for genotype-driven precision oncology trials Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department Paris; the author/funder, who has granted medRxiv a license to display the preprint in perpetuity Data Resource Profile: The French National Uniform Hospital Discharge Data Set Database (PMSI) Ministère des solidarités et de la santé. Financement des établissements de santé ATIH. Manuel des groupes homogènes de malades. ATIH Comparison of the short-term risk of bleeding and arterial thromboembolic events in nonvalvular atrial fibrillation patients newly treated with dabigatran or rivaroxaban versus vitamin K antagonists: a French nationwide propensity-matched cohort study Comparison of the characteristics, morbidity, and mortality of COVID-19 and seasonal influenza: a nationwide, population-based retrospective cohort study. The Lancer Respiratory Medicine Syndrome d'hypersensibilité médicamenteuse en pratique interniste : pièges diagnostique et thérapeutique.Huit observations. La Revue de Médecine Interne Les sources de données déjà disponibles au CASD Differential Globalization of Industry-and Non-Industry-Sponsored Clinical Trials International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity We are grateful to our colleagues for their helpful feedback and perspectives on the paper especially Johanna Van Caneghem, Geoffray Bizouard for his PMSI expertise and Cyrille Leroy. The author RG has changed affiliations since his contributions to the paper. He now works in the EMEA Commercial Effectiveness Services, Consulting Services in Madrid, Spain. The copyright holder for this preprint this version posted April 10, 2022 https://www.abpi.org.uk/publications/clinical-trials-how-the-uk-is-researching-medicinesof-the-future/.. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 10, 2022. ; https://doi.org/10.1101/2022.04.05.22273463 doi: medRxiv preprint