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Objective To quantify the comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with use of adenovirus based covid-19 vaccines versus mRNA based covid-19 vaccines. Design International network cohort study. Setting Routinely collected health data from contributing datasets in France, Germany, the Netherlands, Spain, the UK, and the US. Participants Adults (age ≥18 years) registered at any contributing database and who received at least one dose of a covid-19 vaccine (ChAdOx1-S (Oxford-AstraZeneca), BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), or Ad26.COV2.S (Janssen/Johnson & Johnson)), from December 2020 to mid-2021. Main outcome measures Thrombosis with thrombocytopenia syndrome or venous or arterial thromboembolic events within the 28 days after covid-19 vaccination. Incidence rate ratios were estimated after propensity scores matching and were calibrated using negative control outcomes. Estimates specific to the database were pooled by use of random effects meta-analyses. Results Overall, 1 332 719 of 3 829 822 first dose ChAdOx1-S recipients were matched to 2 124 339 of 2 149 679 BNT162b2 recipients from Germany and the UK. Additionally, 762 517 of 772 678 people receiving Ad26.COV2.S were matched to 2 851 976 of 7 606 693 receiving BNT162b2 in Germany, Spain, and the US. All 628 164 Ad26.COV2.S recipients from the US were matched to 2 230 157 of 3 923 371 mRNA-1273 recipients. A total of 862 thrombocytopenia events were observed in the matched first dose ChAdOx1-S recipients from Germany and the UK, and 520 events after a first dose of BNT162b2. Comparing ChAdOx1-S with a first dose of BNT162b2 revealed an increased risk of thrombocytopenia (pooled calibrated incidence rate ratio 1.33 (95% confidence interval 1.18 to 1.50) and calibrated incidence rate difference of 1.18 (0.57 to 1.8) per 1000 person years). Additionally, a pooled calibrated incidence rate ratio of 2.26 (0.93 to 5.52) for venous thrombosis with thrombocytopenia syndrome was seen with Ad26.COV2.S compared with BNT162b2. Conclusions In this multinational study, a pooled 30% increased risk of thrombocytopenia after a first dose of the ChAdOx1-S vaccine was observed, as was a trend towards an increased risk of venous thrombosis with thrombocytopenia syndrome after Ad26.COV2.S compared with BNT162b2. Although rare, the observed risks after adenovirus based vaccines should be considered when planning further immunisation campaigns and future vaccine development.

Objective This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance. Materials and Methods We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models. Results We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited. Conclusion The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

Introduction Concerns of the persistence and severity of the adverse effects of fluoroquinolones, mainly involving the nervous system, muscles and joints, resulted in the 2018 referral procedure led by the European Medicines Agency (EMA). They advised to stop prescribing fluoroquinolones for infections of mild severity or of a presumed self-limiting course and for prevention of infections, plus to restrict prescriptions in cases of milder infections where other treatment options are available, and restrict in at-risk populations. We aimed to examine whether the impact of EMA regulatory interventions implemented throughout 2018–2019 had an impact on fluoroquinolone prescribing rates. Methods A retrospective population-based cohort study was conducted using electronic health care records from six European countries between 2016 and 2021. We analysed monthly incident fluoroquinolone use rates overall and for each fluoroquinolone active substance through flexible modelling via segmented regression to detect time points of trend changes, in monthly percentage change (MPC). Results The incidence of fluoroquinolone use ranged from 0.7 to 8.0/1000 persons per month over all calendar years. While changes in fluoroquinolone prescriptions were observed over time across countries, these were inconsistent and did not seem to be temporally related to EMA interventions (e.g., Belgium: February/May 2018, MPC − 33.3%, 95% confidence interval [CI] − 35.9 to − 30.7; Germany: February/May 2019, MPC − 12.6%, 95% CI − 13.7 to − 11.6]; UK: January/April 2016, MPC − 4.9%, 95% CI − 6.2 to − 3.6). Conclusion The regulatory action associated with the 2018 referral did not seem to have relevant effects on fluoroquinolone prescribing in primary care. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-023-01286-4.

Objectives This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators. Materials and Methods We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009–2019. Results We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009–2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented. Discussion Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented. Conclusion Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.