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INTRODUCTION: Messenger ribonucleic acid (mRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines have been associated with myocarditis/pericarditis, especially in young males. We evaluated the risk of myocarditis/pericarditis following mRNA vaccines by brand, age, sex and dose number in Singapore. METHODS: Adverse event reports of myocarditis/pericarditis following mRNA vaccines received by the Health Sciences Authority from 30 December 2020 to 25 July 2022 were included, with a data lock on 30 September 2022. Case adjudication was done by an independent panel of cardiologists using the US Centers for Disease Control and Prevention case definition. Reporting rates were compared with expected rates using historical data from 2018 to 2020. RESULTS: Of the 152 adjudicated cases, males comprised 75.0%. The median age was 30 years. Most cases occurred after Dose 2 (49.3%). The median time to onset was 2 days. Reporting rates were highest in males aged 12-17 years for both primary series (11.5 [95% confidence interval [CI] 6.7-18.4] per 100,000 doses, post-Dose 2) and following booster doses (7.1 [95% CI 3.0-13.9] per 100,000 doses). In children aged 5-11 years, myocarditis remained very rare (0.2 per 100,000 doses). The reporting rates for Booster 1 were generally similar or lower than those for Dose 2. CONCLUSIONS: The risk of myocarditis/pericarditis with mRNA vaccines was highest in adolescent males following Dose 2, and this was higher than historically observed background rates. Most cases were clinically mild. The risk of myocarditis should be weighed against the benefits of receiving an mRNA vaccine, keeping in mind that SARS-CoV-2 infections carry substantial risks of myocarditis/pericarditis, as well as the evolving landscape of the disease.
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BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. PURPOSE: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-AE relations from unstructured hospital discharge summaries. BASIC PROCEDURES: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. MAIN FINDINGS: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. PRINCIPAL CONCLUSIONS: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program.