Search

Kirsty Andresen,1,2 Marina Hinojosa-Campos,3 Bélène Podmore,1– 3 Myriam Drysdale,4 Nawab Qizilbash,1– 3 Marianne Cunnington4,5 1OXON Epidemiology, London, UK; 2London School of Hygiene and Tropical Medicine, London, UK; 3OXON Epidemiology, Madrid, Spain; 4GlaxoSmithKline, Middlesex, UK; 5Analysis Group, London, UKCorrespondence: Kirsty Andresen, Email Kirsty.andresen@oxonepi.comIntroduction: Regulatory guidance encourages transparent reporting of information on the quality and validity of electronic health record data being used to generate real-world benefit-risk evidence for vaccines and therapeutics. We aimed to provide an overview of the availability of validated diagnostic algorithms for selected safety endpoints for Coronavirus disease 2019 (COVID-19) vaccines and therapeutics in the context of the emerging pandemic prior to December 2020.Methods: We reviewed the literature up to December 2020 to identify validation studies for various safety events of interest, including myocardial infarction, arrhythmia, myocarditis, acute cardiac injury, vasculitis/vasculopathy, venous thromboembolism, stroke, respiratory distress syndrome (RDS), pneumonitis, cytokine release syndrome (CRS), multiple organ dysfunction syndrome, and renal failure. We included studies published between 2015 and 2020 that were considered high quality assessed with QUADAS and that reported positive predictive values (PPVs).Results: Out of 43 identified studies, we found that diagnostic algorithms for cardiovascular outcomes were supported by the highest number of validation studies (n=17). Accurate algorithms are available for myocardial infarction (median PPV 80%; IQR 22%), arrhythmia (PPV range > 70%), venous thromboembolism (median PPV: 73%) and ischaemic stroke (PPV range ≥ 85%). We found a lack of validation studies for less common respiratory and cardiac safety outcomes of interest (eg, pneumonitis and myocarditis), as well as for COVID-specific complications (CRS, RDS).Conclus

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.

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., 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.