Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources

Author:

Geva Alon123,Abman Steven H45,Manzi Shannon F167,Ivy Dunbar D58,Mullen Mary P79,Griffin John2,Lin Chen1,Savova Guergana K17,Mandl Kenneth D1710

Affiliation:

1. Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA

2. Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts, USA

3. Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA

4. Division of Pediatric Pulmonary Medicine, Children’s Hospital Colorado, Aurora, Colorado, USA

5. Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA

6. Division of Genetics & Genomics, Clinical Pharmacogenomics Service, Department of Pharmacy, Boston Children’s Hospital, Boston, Massachusetts, USA

7. Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA

8. Division of Cardiology, Heart Institute, Children’s Hospital Colorado, Aurora, Colorado, USA

9. Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA

10. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA

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.

Funder

National Institutes of Health grant numbers

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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