Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies

Objectives : Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. Methods : In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. Results : We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. Conclusions : We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management:A PIONEER Analysis Based on Big Data

Background: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. Objective: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. Design, setting, and participants: From an initial cohort of &gt;100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). Outcome measurements and statistical analysis: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. Results and limitations: The most common comorbidities were hypertension (35–73%), obesity (9.2–54%), and type 2 diabetes (11–28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12–25%) and emergency department visits (10–14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. Conclusions: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. Patient summary: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.</p

From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding

In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project—Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation—with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population

Evaluation empirique d’approches basées sur les cas pour la génération d’alertes de pharmacovigilance à partir du Système National des Données de Santé (SNDS)

France has a large nationwide longitudinal database with claims and hospital data, the Système National des Données de Santé (French National healthcare database – SNDS), which currently covers almost the complete French population, from birth or immigration to death or emigration, and includes all reimbursed medical and paramedical encounters. Since SNDS systematically and prospectively captures drug dispensings, death, and events leading to hospital stays, it has a strong potential for drug assessment in real life settings. Following the worldwide withdrawal of rofecoxib in 2004, several initiatives aiming to develop and evaluate methodologies for drug safety monitoring on healthcare databases emerged. The EU-ADR alliance (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) and OMOP (Observational Outcomes Partnership) were respectively launched in Europe and in the Unites-States. These experiments demonstrated the usefulness of pharmacoepidemiological approaches in drug safety signal detection. However the SNDS had never been tested in this scope. The objective of this thesis was to empirically assess 3 case-based designs – case-population, case-control, and self-controlled case series – for drug-safety alert generation in the SNDS, taking as examples two health outcome of interest: upper gastrointestinal bleeding (UGIB) and acute liver injury (ALI).The overall project consisted of 4 main stages: (1) preparation of the data to fit the OMOP common data model and the selection of positive and negative drug controls for each outcome of interest; (2) analysis of the selected drug controls using the 3 case-based designs, testing several design variants (e.g. testing different risk windows, adjustment strategies, etc.); (3) comparison of design variant performances through the calculation of the area under the receiver operating characteristics curve (AUC), the mean square error (MSE) and the coverage probability; (4) the selection of the best design variant and its calibration for each health outcome of interest.Self-controlled case series showed the best performances in both outcomes, ALI and UGIB, with AUCs reaching respectively 0.80 and 0.94 and MSEs 0.07 and 0.12. For UGIB optimal performances were observed when adjusting for multiple drugs and using a risk window corresponding to the 30 first days of exposure. For ALI, optimal performances were also observed when adjusting for multiple drugs but using a risk window corresponding to the overall period covered by drug dispensings. Negative drug control implementation highlighted that a low systematic error seemed to be generated by the optimum variants in the SNDS but that protopathic bias and confounding by indication remained unaddressed issues.These results showed that self-controlled case series were well suited to detect drug safety alerts associated with UGIB and ALI in the SNDS in an accurate manner. A clinical perspective remains necessary to rule out potential false positive signals from residual confounding. The application in routine of such approaches extended to other outcomes of interest could result in substantial progress in pharmacovigilance in France.La France possède une large base de données nationale regroupant les données de liquidation de l’Assurance Maladie, de mortalité et des données hospitalières : le Système National des Données de Santé (SNDS). Celui-ci couvre actuellement la quasi-totalité de la population française de la naissance (ou immigration), au décès (ou émigration), en incluant tous les remboursements de frais médicaux ou paramédicaux. En recueillant de manière systématique et prospective les dispensations médicamenteuses, les événements hospitaliers et les décès, le SNDS est doté d’un fort potentiel pour l’évaluation du médicament en vie réelle. Suite au retrait mondial du rofecoxib en 2004, de nombreuses initiatives visant au développement et à l’évaluation de méthodologies adaptées aux bases de données populationnelles pour la surveillance des risques liés à l’usage du médicament ont vu le jour, en particulier le réseau EU-ADR en Europe (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) et OMOP (Observational Outcomes Partnership) aux États-Unis. Ces travaux ont démontré l’utilité des approches pharmaco-épidémiologiques pour la détection de signaux de pharmacovigilance. Cependant, le SNDS n’a jamais été testé dans cette optique. L’objectif de cette thèse était d’évaluer de manière empirique, 3 approches pharmaco épidémiologiques basées sur les cas pour la génération d’alerte(s) de pharmacovigilance dans le SNDS : étude cas-population, étude cas-témoins et séries de cas autocontrôlés. Ces approches ont été appliquées à deux événements médicaux d’intérêt récurrents en pharmacovigilance : l’hémorragie digestive haute (UGIB) et l’hépatite aigue (ALI). Le projet a été composé de 4 principales étapes : (1) le formatage des données selon les spécifications du modèle commun de données d’OMOP et la sélection des médicaments témoins positifs et négatifs pour chaque événement d'intérêt ; (2) l’analyse des médicaments témoins sélectionnés en utilisant les 3 approches basées sur les cas, en déclinant chaque approche selon plusieurs variantes (par exemple, en testant différentes fenêtres de risque, stratégies d'ajustement, etc.) ; (3) la comparaison des performances des variantes selon leur aire sous la courbe ROC (AUC), leur erreur quadratique moyenne (MSE) et leur probabilité de couverture ; (4) la sélection de la meilleure variante pour chaque événement d’intérêt et son étalonnage. Sur les 3 approches étudiées, c’est la série de cas autocontrôlés qui a montré les meilleures performances dans UGIB et ALI avec des AUC respectifs de 0,80 et 0,94 et des MSE de 0,07 et 0,12. Pour UGIB, les performances optimales ont été observées lorsque l'ajustement tenait compte des traitements concomitants et lorsque les 30 premiers jours d'exposition au médicament d’intérêt étaient utilisés comme fenêtre de risque. Pour ALI, les performances optimales ont été également obtenues lors de l'ajustement en fonction des traitements concomitants, mais en utilisant une fenêtre de risque correspondant à l’ensemble de la période couverte par les dispensations de médicament d’intérêt. L’utilisation de médicaments témoins négatifs a montré que l’erreur systématique résultant de l’application de l’approche et des paramètres optimaux dans le SNDS semblait faible, mais que les biais protopathiques et de confusion restaient présents. Au total, ces travaux ont montré que les séries de cas autocontrôlées sont à considérer comme une approche adaptée à la détection d’alertes de pharmacovigilance associées à ALI et à UGIB dans le SNDS. Un point de vue clinique demeure toutefois nécessaire pour écarter tout risque de faux positif résultant de potentiels biais résiduels. L’application d'une telle approche à d'autres événements d'intérêt et son utilisation en routine constitueraient des progrès substantiels en matière de pharmacovigilance en France

Empirical assessment of case-based designs for drug safety alert generation in the French National Healthcare System database (SNDS)

La France possède une large base de données nationale regroupant les données de liquidation de l’Assurance Maladie, de mortalité et des données hospitalières : le Système National des Données de Santé (SNDS). Celui-ci couvre actuellement la quasi-totalité de la population française de la naissance (ou immigration), au décès (ou émigration), en incluant tous les remboursements de frais médicaux ou paramédicaux. En recueillant de manière systématique et prospective les dispensations médicamenteuses, les événements hospitaliers et les décès, le SNDS est doté d’un fort potentiel pour l’évaluation du médicament en vie réelle. Suite au retrait mondial du rofecoxib en 2004, de nombreuses initiatives visant au développement et à l’évaluation de méthodologies adaptées aux bases de données populationnelles pour la surveillance des risques liés à l’usage du médicament ont vu le jour, en particulier le réseau EU-ADR en Europe (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) et OMOP (Observational Outcomes Partnership) aux États-Unis. Ces travaux ont démontré l’utilité des approches pharmaco-épidémiologiques pour la détection de signaux de pharmacovigilance. Cependant, le SNDS n’a jamais été testé dans cette optique. L’objectif de cette thèse était d’évaluer de manière empirique, 3 approches pharmaco épidémiologiques basées sur les cas pour la génération d’alerte(s) de pharmacovigilance dans le SNDS : étude cas-population, étude cas-témoins et séries de cas autocontrôlés. Ces approches ont été appliquées à deux événements médicaux d’intérêt récurrents en pharmacovigilance : l’hémorragie digestive haute (UGIB) et l’hépatite aigue (ALI). Le projet a été composé de 4 principales étapes : (1) le formatage des données selon les spécifications du modèle commun de données d’OMOP et la sélection des médicaments témoins positifs et négatifs pour chaque événement d'intérêt ; (2) l’analyse des médicaments témoins sélectionnés en utilisant les 3 approches basées sur les cas, en déclinant chaque approche selon plusieurs variantes (par exemple, en testant différentes fenêtres de risque, stratégies d'ajustement, etc.) ; (3) la comparaison des performances des variantes selon leur aire sous la courbe ROC (AUC), leur erreur quadratique moyenne (MSE) et leur probabilité de couverture ; (4) la sélection de la meilleure variante pour chaque événement d’intérêt et son étalonnage. Sur les 3 approches étudiées, c’est la série de cas autocontrôlés qui a montré les meilleures performances dans UGIB et ALI avec des AUC respectifs de 0,80 et 0,94 et des MSE de 0,07 et 0,12. Pour UGIB, les performances optimales ont été observées lorsque l'ajustement tenait compte des traitements concomitants et lorsque les 30 premiers jours d'exposition au médicament d’intérêt étaient utilisés comme fenêtre de risque. Pour ALI, les performances optimales ont été également obtenues lors de l'ajustement en fonction des traitements concomitants, mais en utilisant une fenêtre de risque correspondant à l’ensemble de la période couverte par les dispensations de médicament d’intérêt. L’utilisation de médicaments témoins négatifs a montré que l’erreur systématique résultant de l’application de l’approche et des paramètres optimaux dans le SNDS semblait faible, mais que les biais protopathiques et de confusion restaient présents. Au total, ces travaux ont montré que les séries de cas autocontrôlées sont à considérer comme une approche adaptée à la détection d’alertes de pharmacovigilance associées à ALI et à UGIB dans le SNDS. Un point de vue clinique demeure toutefois nécessaire pour écarter tout risque de faux positif résultant de potentiels biais résiduels. L’application d'une telle approche à d'autres événements d'intérêt et son utilisation en routine constitueraient des progrès substantiels en matière de pharmacovigilance en France.France has a large nationwide longitudinal database with claims and hospital data, the Système National des Données de Santé (French National healthcare database – SNDS), which currently covers almost the complete French population, from birth or immigration to death or emigration, and includes all reimbursed medical and paramedical encounters. Since SNDS systematically and prospectively captures drug dispensings, death, and events leading to hospital stays, it has a strong potential for drug assessment in real life settings. Following the worldwide withdrawal of rofecoxib in 2004, several initiatives aiming to develop and evaluate methodologies for drug safety monitoring on healthcare databases emerged. The EU-ADR alliance (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) and OMOP (Observational Outcomes Partnership) were respectively launched in Europe and in the Unites-States. These experiments demonstrated the usefulness of pharmacoepidemiological approaches in drug safety signal detection. However the SNDS had never been tested in this scope. The objective of this thesis was to empirically assess 3 case-based designs – case-population, case-control, and self-controlled case series – for drug-safety alert generation in the SNDS, taking as examples two health outcome of interest: upper gastrointestinal bleeding (UGIB) and acute liver injury (ALI).The overall project consisted of 4 main stages: (1) preparation of the data to fit the OMOP common data model and the selection of positive and negative drug controls for each outcome of interest; (2) analysis of the selected drug controls using the 3 case-based designs, testing several design variants (e.g. testing different risk windows, adjustment strategies, etc.); (3) comparison of design variant performances through the calculation of the area under the receiver operating characteristics curve (AUC), the mean square error (MSE) and the coverage probability; (4) the selection of the best design variant and its calibration for each health outcome of interest.Self-controlled case series showed the best performances in both outcomes, ALI and UGIB, with AUCs reaching respectively 0.80 and 0.94 and MSEs 0.07 and 0.12. For UGIB optimal performances were observed when adjusting for multiple drugs and using a risk window corresponding to the 30 first days of exposure. For ALI, optimal performances were also observed when adjusting for multiple drugs but using a risk window corresponding to the overall period covered by drug dispensings. Negative drug control implementation highlighted that a low systematic error seemed to be generated by the optimum variants in the SNDS but that protopathic bias and confounding by indication remained unaddressed issues.These results showed that self-controlled case series were well suited to detect drug safety alerts associated with UGIB and ALI in the SNDS in an accurate manner. A clinical perspective remains necessary to rule out potential false positive signals from residual confounding. The application in routine of such approaches extended to other outcomes of interest could result in substantial progress in pharmacovigilance in France

Cohort Event Monitoring of Adverse Reactions to COVID-19 Vaccines in Seven European Countries: Pooled Results on First Dose

Introduction: COVID-19 vaccines were rapidly authorised, thus requiring intense post-marketing re-evaluation of their benefit-risk profile. A multi-national European collaboration was established with the aim to prospectively monitor safety of the COVID-19 vaccines through web-based survey of vaccinees. Methods: A prospective cohort event monitoring study was conducted with primary consented data collection in seven European countries. Through the web applications, participants received and completed baseline and up to six follow-up questionnaires on self-reported adverse reactions for at least 6 months following the first dose of COVID-19 vaccine (Netherlands, France, Belgium, UK, Italy) and baseline and up to ten follow-up questionnaires for one year in Germany and Croatia. Rates of adverse reactions have been described by type (solicited, non-solicited; serious/non-serious; and adverse events of special interest) and stratified by vaccine brand. We calculated the frequency of adverse reaction after dose 1 and prior to dose 2 among all vaccinees who completed at least one follow-up questionnaire. Results: Overall, 117,791 participants were included and completed the first questionnaire in addition to the baseline: 88,196 (74.9%) from Germany, 27,588 (23.4%) from Netherlands, 984 (0.8%) from France, 570 (0.5%) from Italy, 326 (0.3%) from Croatia, 89 (0.1%) from the UK and 38 (0.03%) from Belgium. There were 89,377 (75.9%) respondents who had received AstraZeneca vaccines, 14,658 (12.4%) BioNTech/Pfizer, 11,266 (9.6%) Moderna and 2490 (2.1%) Janssen vaccines as a first dose. Median age category was 40-49 years for all vaccines except for Pfizer where median age was 70-79 years. Most vaccinees were female with a female-to-male ratio of 1.34, 1.96 and 2.50 for AstraZeneca, Moderna and Janssen, respectively. BioNtech/Pfizer had slightly more men with a ratio of 0.82. Fatigue and headache were the most commonly reported solicited systemic adverse reactions and injection-site pain was the most common solicited local reaction. The rates of adverse events of special interest (AESIs) were 0.1-0.2% across all vaccine brands. Conclusion: This large-scale prospective study of COVID-19 vaccine recipients showed, for all the studied vaccines, a high frequency of systemic reactions, related to the immunogenic response, and local reactions at the injection site, while serious reactions or AESIs were uncommon, consistent with those reported on product labels. This study demonstrated the feasibility of setting up and conducting cohort event monitoring across multiple European countries to collect safety data on novel vaccines that are rolled out at scale in populations which may not have been included in pivotal trials

Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies

OBJECTIVES: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. METHODS: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. RESULTS: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. CONCLUSIONS: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR

Safety of COVID-19 Vaccines Among the Paediatric Population: Analysis of the European Surveillance Systems and Pivotal Clinical Trials

Background and objectives: The European Medicine Agency extended the use of Comirnaty, Spikevax, and Nuvaxovid in paediatrics; thus, these vaccines require additional real-world safety evidence. Herein, we aimed to monitor the safety of COVID-19 vaccines through Covid-19 Vaccine Monitor (CVM) and EudraVigilance surveillance systems and the published pivotal clinical trials. Methods: In a prospective cohort of vaccinees aged between 5 and 17 years, we measured the frequency of commonly reported (local/systemic solicited) and serious adverse drug events (ADRs) following the first and second doses of COVID-19 vaccines in Europe using data from the CVM cohort until April 2022. The results of previous pivotal clinical trials and data in the EudraVigilance were also analysed. Results: The CVM study enrolled 658 first-dose vaccinees (children aged 5-11 years; n = 250 and adolescents aged 12-17 years; n = 408). Local/systemic solicited ADRs were common, whereas serious ADRs were uncommon. Among Comirnaty first and second dose recipients, 28.8% and 17.1% of children and 54.2% and 52.2% of adolescents experienced at least one ADR, respectively; injection-site pain (29.2% and 20.7%), fatigue (16.1% and 12.8%), and headache (22.1% and 19.3%) were the most frequent local and systemic ADRs. Results were consistent but slightly lower than in pivotal clinical trials. Reporting rates in Eudravigilance were lower by a factor of 1000. Conclusions: The CVM study showed high frequencies of local solicited reactions after vaccination but lower rates than in pivotal clinical trials. Injection-site pain, fatigue, and headache were the most commonly reported ADRs for clinical trials, but higher than spontaneously reported data

Contribution of cryopreservation in a mandatory SET policy: analysis of 5 years of application of law in an academic IVF center.

To analyse treatment outcomes after SET law enforcement and to evaluate the contribution of cryopreservation in a SET policy.Journal ArticleSCOPUS: cp.jinfo:eu-repo/semantics/publishe

From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding

In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population