Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences

Abstract

Aim: This case study aimed to investigate the process of integrating resources of multiple biobanks and health-care registers, especially addressing data permit application, time schedules, co-operation of stakeholders, data exchange and data quality. Methods: We investigated the process in the context of a retrospective study: Pharmacogenomics of antithrombotic drugs (PreMed study). The study involved linking the genotype data of three Finnish biobanks (Auria Biobank, Helsinki Biobank and THL Biobank) with register data on medicine dispensations, health-care encounters and laboratory results. Results: We managed to collect a cohort of 7005 genotyped individuals, thereby achieving the statistical power requirements of the study. The data collection process took 16 months, exceeding our original estimate by seven months. The main delays were caused by the congested data permit approval service to access national register data on health-care encounters. Comparison of hospital data lakes and national registers revealed differences, especially concerning medication data. Genetic variant frequencies were in line with earlier data reported for the European population. The yearly number of international normalised ratio (INR) tests showed stable behaviour over time. Conclusions: A large cohort, consisting of versatile individual-level phenotype and genotype data, can be constructed by integrating data from several biobanks and health data registers in Finland. Co-operation with biobanks is straightforward. However, long time periods need to be reserved when biobank resources are linked with national register data. There is a need for efforts to define general, harmonised co-operation practices and data exchange methods for enabling efficient collection of data from multiple sources.

Pharmacogenetics of warfarin and healthcare costs – Real‐world data analysis

“…Genotyping of the participants had been carried out by using a custom Axiom FinnGen1 array and with non‐custom genotyping arrays 11 . The imputation quality score of the genotype data used in the study varied in the range of 0.898–1.000, with a mean value of 0.995 10 . We divided the patients into genetically normal and genetically sensitive warfarin responder groups based on their carrier status of the CYP2C9*2 , CYP2C9*3 and VKORC1 (g.3588G>A) alleles following the approach presented by Mega et al 12 Due to the small sample size, we combined the sensitive and highly sensitive group into a single group referred as sensitive warfarin responders hereafter 13 .…”

“…This was a retrospective study integrating real world data from Finnish national registers and biobanks. 10 Three biobanks (THL Biobank, Auria Biobank, Helsinki Biobank) were responsible for data collection linking their genotype data with healthcare encounter data of the Finnish Institute of Health and Welfare (THL), drug dispensation data from the Social Insurance Institution of Finland (Kela) and laboratory data from Finnish hospital districts and municipalities. Each participating organisation was responsible for the quality and integrity of their data sets.…”

Development of medical applications based on AI models and register data – regulatory considerations

“…In most cases the data need to be acquired from one or more health data registers, such as electronic health record systems (EHRs). Real-world data (RWD) accumulated in EHRs can be made available by the respective data controller for so called secondary use referring to the usage of data for another purpose than the purpose for which the data was originally collected [9]. Secondary use of data may take place without the consent of the data subject based on public interest in the area of public health or scientific research as defined by the General Data Protection Regulation (GDPR), article 9(2) i, j.…”

Biobanking potential for biomedical research in endocrinology

“…Существует тенденция к созданию вместо отдельных хранилищ сетей биобанков и интеграции биобанков с национальными регистрами [40,41]. Такой подход позволяет расширить возможности для создания крупных БРК и получить структурированные данные высокой степени аннотированности [41].…”

“…Существует тенденция к созданию вместо отдельных хранилищ сетей биобанков и интеграции биобанков с национальными регистрами [40,41]. Такой подход позволяет расширить возможности для создания крупных БРК и получить структурированные данные высокой степени аннотированности [41]. Примерами коллабораций биобанков являются Canadian Tissue Repository Network (CTRNet), EuroBioBank, Danish National Biobank [19,40,42].…”