Journal of the American Medical Informatics Association · 2019 · 24 citations · 32 references
MedicinePediatricsPharmacologyElectronic Health RecordsPediatric EpidemiologyPharmacovigilanceAdverse Drug Event DetectionAdverse Drug Event ReportingAdverse Drug Reaction ReportingClinical Natural Language ProcessingHypertension In ChildrenPulmonary HypertensionAdministrative Claims DataAdministrative Claims Data AnalysisAdverse Drug Event Mention DetectionElectronic Health Record-based PharmacovigilanceElectronic Health Records DatabasesPediatric Adverse Drug Event ReportingPediatric Pulmonary HypertensionSource Data Comparison
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.
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Guergana Savova, James Masanz, Philip V. Ogren et al. · Journal of the American Medical Informatics Association · 2010 · 2K citations · Full text
Eberechukwu Onukwugha, Candice Yong, Arif Hussain et al. · BMC Medical Research Methodology · 2014 · 596 citations · Full text
Medicine, Biostatistics, Oncology +16