Purpose. In 2019, the Innovative Medicines Initiative funded the ConcePTION project to enhance monitoring of medication safety in pregnancy and breastfeeding. This paper describes how the ConcePTION Pregnancy Algorithm (PA) identified pregnancies in 10 diverse European electronic healthcare data sources and estimated their duration. Methods. Data sources from six European countries were mapped to the ConcePTION Common Data Model. Any pregnancy-related record was retrieved from various available data banks, including birth register, primary care records, and hospital records, and reconciled into episodes of pregnancy (starting between 01/2015 and 12/2019), each with start date, end date, and type of end. A random forest model was used to estimate missing gestational ages for incomplete records. Parameters were tailored to data sources to address local variations in data availability, collection, and governance. Model performance was evaluated using cross-validated Root Mean Squared Error (RMSE). Results. The PA identified ~ 2.7 million pregnancies, in over 2.2 million individuals. Most ended in live births (50%-83%), 1%-15% in elective terminations, and 4%-10% in spontaneous abortions, depending on data sources. Pregnancies with unknown type of end were also retrieved (2%-34%). Gestational age was predicted for 6%-89% of records (RMSE: 17-50 days). The median gestational age at first identified pregnancy record ranged from 47 to 280 days. Conclusions. We developed an open-source algorithm to identify and date pregnancies, including early-stage pregnancies with unknown end and/or ongoing at the time of data extraction. This algorithm may facilitate multinational studies, improving generation of timely real-world evidence about use and safety of medicinal products in pregnancy.

A document by Nicolas Thurin. Click on the document to view its contents.

Thrombotic events are common during COVID-19 infection. Aspirin might be beneficial. Objective: Systematic review and meta-analysis of deaths in users and non-users of aspirin. Data sources: Pubmed Medline, Google scholar, Clinicaltrials.gov, Cochrane, to June 8, 2021, Study selection: Studies providing adjusted or matched evaluation of association of exposure to aspirin and death in COVID-19 patients were included. Data extraction and synthesis: Data were used as published, as Odds ratio, hazard ratio or relative risks and 95% CI from which log(OR) and SE were recalculated. These were entered in an inverse variance odds ratios random-effects model, using RevMan 5.4 (the Cochrane Collaboration). Main outcomes and measure: The prespecified outcome studied was death. Results: Nine studies (8 observational, one interventional) included 14989 patients exposed to aspirin and 15857 unexposed. Overall Odds Ratio of death in aspirin exposed patients in a random effects model was 0.63, 95% confidence interval [0.40-0.99], I2 94%. Using a fixed-effect model did not change much the result (0.76 [0.71-0.81], removing the Recovery trial (OR 0.43 [0.38-0.49], I271%, or the two largest studies (0.66 [0.47-0.93], I2 38%) reduced heterogeneity without materially altering the results. The funnel plot showed no evident publication bias Conclusion: this meta-analysis suggests that the use of aspirin may be associated with a lower risk of death in COVID-19. Considering the results of the Recovery Study, it would appear preferable to continue aspirin in patients who have a non-covid indication, but possibly useless to add it if they don’t.