(198) Extracting Pregnancies from Heterogeneous Data Sources in Europe: A Novel Algorithm in The Conception Project

Friday, August 26, 2022

8:00 AM – 6:00 PM CEST

Location: Hall C

Publication Number: 529

Presenting Author(s)

Giorgio Limoncella
– Agenzia Regionale di Sanit� della Toscana, Firenze, Firenze, Italy

Co-Author(s)

Claudia C. Bartolini
– ARS Toscana, Florence, Italy, Florence, Toscana, Italy
Carlos Duran
– Julius Center for Health Sciences and Primary Care, Department of Data Science & Biostatistics, University Medical Center Utrecht, the Netherlands, Netherlands
Anna Girardi
– ARS Toscana, Florence, Italy, Italy
Giulia Hyeraci
– Agenzia Regionale di Sanit� Toscana, Firenze, Firenze, Italy
Judit Riera Arnau, Dr
– University Medical Center Utrecht, Barcelona, Barcelona, Spain
Ana Llorente
– AEMPS, Spain
Lucía Cea Soriano
– Department of Public Health and Child Health. Faculty of Medicine. Universidad Complutense de Madrid, Spain
Mar Martin-Perez
– AEMPS, Spain
Patricia García-Poza
– AEMPS (Spanish Agency of Medicines and Medical Devices), Spain
Francisco Sanchez-Saez, PhD
– Foundation for the Promotion of Health and Biomedical Research of Valencia Region, Valencia, Valencia, Spain
Gabriel Sanfelix-Gimeno
– Foundation for the Promotion of Health and Biomedical Research of Valencia Region, Valencia, Valencia, Spain
Hedvig Nordeng, PhD, FISPE
– Professor, University of Oslo, Oslo, Oslo, Norway
Eimir Hurley, PhD
– Researcher, University of Oslo, Oslo, Oslo, Norway
Luigi A. Maglanoc
– University Center for Information Technology, University of Oslo, Oslo, Norway
Emily Holthuis
– PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands, Netherlands
Karin Swart
– PHARMO Institute, Utrecht, Utrecht, Netherlands
Valentina Ientile
– University of Messina, Italy, Italy
Matilde Tanaglia
– University of Verona, Italy
Jeremy Brown, MSc
– PhD student, London School of Hygiene and Tropical Medicine, London, United Kingdom
Kevin Wing
– Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom
Tania Schink
– Leibniz Institute for Prevention Research and Epidemiology - BIPS, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
Romin Pajouheshnia, PhD
– Assistant Professor (formerly), University of Utrecht, The Hague, Zuid-Holland, Netherlands
Marianne Cunnington, PhD
– Epidemiology, Value Evidence & Outcomes, R&D, GlaxoSmithKline, London, United Kingdom
Miriam C. Sturkenboom, PharmD, PhD, FISPE
– prof. dr., University Medical Center Utrecht, Utrecht, Utrecht, Netherlands
Rosa Gini, PhD
– Agenzia Reg Della Sanit, Firenze, Firenze, Italy

Background: The IMI-ConcePTION project aims to build an ecosystem to generate Real World Evidence to address the information gap of medication safety in pregnancy. Data sources do not come with an enumeration of the pregnancies experienced in their populations, and differ in terms of provenance. A novel algorithm to detect pregnancy episodes was designed and tested, also in data sources participating in two EMA-funded studies using the ConcePTION Common Data Model:CONSIGN and retinoids/valproate risk minimization measures study.

Objectives: To describe a novel algorithm extracting pregnancies from heterogeneous European data sources.

Methods: Six partners extracted instances from the following data sources: ARS, CASERTA (IT); BIFAP, VID (ES); PHARMO Database Network (NL) and UOSL (NO). Time periods varied from 2005 to 2021. Records were first retrieved from all available provenances, including: birth registries (BR), primary care medical records, hospital discharge records, procedures, and others. To retrieve coded diagnoses, codes were mapped from the Matcho algorithm and other sources to many vocabularies (ICD9CM, ICD10CM, ICPC) via the Codemapper tool. Records of the same person were listed longitudinally, and grouped in episodes of pregnancy. Using hierarchical rules we defined start date, end date (limited to pregnancies completed by the time of data extraction) and type of end, categorised as livebirth, still birth, termination, spontaneous abortion, ectopic, unknown (UKN), ongoing (ONG).

Results: Episodes of pregnancy were 566,050, 126,119, 1,954,078, 7,039 108,566, 885,415
in ARS, CASERTA, BIFAP, VID, PHARMO, UOSL, respectively. BR was the highest provenance in the hierarchy in 100% of cases in UOSL, and ranged from 95.1% in ARS to none in BIFAP. All data sources except UOSL could retrieve pregnancies whose end was UKN: these constituted 13.2%, 4.5%, 35.2%, 24.4%, and 1.0% of pregnancies identified in ARS, CASERTA, BIFAP, VID and PHARMO, respectively. ONG pregnancies could be retrieved from ARS, BIFAP, VID and PHARMO.

Conclusions: The algorithm leveraged heterogeneous provenances in Europe. Pregnancies could be retrieved that would have gone unnoticed if querying only information recorded at the end of pregnancy, including pregnancies that were ONG at the time of data extraction. Detecting ONG and UKN pregnancies can substantially increase cohorts, with potential to extensively assess impact of regulatory decisions, and to rapidly investigate emerging issues, such as drug utilization in pregnancies exposed to COVID, or exposure and safety of COVID vaccines during pregnancy.

Acknowledgement of funding: Innovative Medicines Initiative; European Medicines Agency