Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources. (26th November 2019)

• Record Type: Journal Article
• Authors: Geva, Alon; Abman, Steven H; Manzi, Shannon F; Ivy, Dunbar D; Mullen, Mary P; Griffin, John; Lin, Chen; Savova, Guergana K; Mandl, Kenneth D
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 2(2020)
• Page Start: 294
• Page End: 300
• Publication Date: 2019-11-26
• Subjects: adverse drug event -- administrative claims -- healthcare -- natural language processing -- hypertension -- pulmonary; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz194
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claims data, among children treated with drugs for pulmonary hypertension. Materials and Methods: Textual mentions of medications and signs/symptoms that may represent ADEs were identified in clinical notes using natural language processing. Diagnostic codes for the same signs/symptoms were identified in our electronic data warehouse for the patients with textual evidence of taking pulmonary hypertension-targeted drugs. We compared rates of ADEs identified in clinical notes to those identified from diagnostic code data. In addition, we compared putative ADE rates from clinical notes to those from a healthcare claims dataset from a large, national insurer. Results: Analysis of clinical notes identified up to 7-fold higher ADE rates than those ascertained from diagnostic codes. However, certain ADEs (eg, hearing loss) were more often identified in diagnostic code data. Similar results were found when ADE rates ascertained from clinical notes and national claims data were compared. Discussion: While administrative claims and clinical notes are both increasingly used for RWD-based pharmacovigilance, ADE rates substantially differ depending on data source. Conclusion: Pharmacovigilance based on RWD may lead to discrepant results depending on the data source analyzed. Further work is needed to confirm the validity of identified ADEs, to distinguish them from disease effects, and to understand tradeoffs in sensitivity and specificity between data sources.