Abstract
Purpose: Leukotriene receptor antagonists (LTRAs) are widely prescribed as controller medications for pediatric asthma. However, there have been increasing concerns about potential neuropsychiatric adverse reactions associated with LTRAs. Findings from observational studies have been inconsistent, and direct comparisons of the risk of neuropsychiatric events (NPEs) between LTRAs and inhaled corticosteroids (ICS) remain limited in the pediatric population. Methods: A retrospective cohort study was conducted utilizing a nationwide claims database (January 2018–April 2022) and a multicenter electronic health record (EHR) database (January 2006–March 2022) from South Korea. Patients aged 5–18 years diagnosed with asthma before initiating LTRA or ICS were included. The primary outcome was NPEs within 90 days of exposure, defined using two methods: diagnostic code-based analysis and natural language processing (NLP)-based analysis using clinical notes. After propensity score stratification, Cox proportional hazards models were used to estimate risks. Results: The diagnostic code-based analysis on the claims database included 169 636 LTRA users and 28 845 ICS users. There was no statistically significant difference in the risk of NPEs between LTRA and ICS (calibrated hazard ratios [HRs], 1.14 [95% CI, 0.92–1.42]). In the NLP-based analysis using EHR database, 1641 LTRA users and 1607 ICS users were included. The results were consistent with those of the diagnostic code-based analysis (calibrated HR, 1.33 [95% CI, 0.66–2.68]). Conclusions: LTRA use was not found to be associated with a significantly increased risk of NPEs in children with asthma. These findings offer valuable insights to support clinical decision-making in pediatric asthma treatment.
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Kim, S., Han, C. H., Chang, J., Cho, J., Jeong, K., Kim, H., … Kim, K. W. (2025). Comparative Risk for Neuropsychiatric Events in Leukotriene Receptor Antagonist vs. Inhaled Corticosteroid in Children With Asthma: A Nationwide Observational Study With a Complementary Analysis Using Natural Language Processing. Pharmacoepidemiology and Drug Safety, 34(11). https://doi.org/10.1002/pds.70254
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