A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions

Abstract

Introduction: Observational healthcare data contain information useful for hastening detection of adverse drug reactions (ADRs) that may be missed by using data in spontaneous reporting systems (SRSs) alone. There are only several papers describing methods that integrate evidence from healthcare databases and SRSs. We propose a methodology that combines ADR signals from these two sources.

Objectives: The aim of this study was to investigate whether the proposed method would result in more accurate ADR detection than methods using SRSs or healthcare data alone.

Research design: We applied the method to four clinically serious ADRs, and evaluated it using three experiments that involve combining an SRS with a single facility small-scale electronic health record (EHR), a larger scale network-based EHR, and a much larger scale healthcare claims database. The evaluation used a reference standard comprising 165 positive and 234 negative drug-ADR pairs.

Measures: Area under the receiver operator characteristics curve (AUC) was computed to measure performance.

Results: There was no improvement in the AUC when the SRS and small-scale HER were combined. The AUC of the combined SRS and large-scale EHR was 0.82 whereas it was 0.76 for each of the individual systems. Similarly, the AUC of the combined SRS and claims system was 0.82 whereas it was 0.76 and 0.78, respectively, for the individual systems.

Conclusions: The proposed method resulted in a significant improvement in the accuracy of ADR detection when the resources used for combining had sufficient amounts of data, demonstrating that the method could integrate evidence from multiple sources and serve as a tool in actual pharmacovigilance practice.

Fig. 1

Electronic health record (EHR) cohort identification and candidate covariates selection. ADR adverse drug reaction, ICD-9 International Statistical Classification of Diseases, Version 9

Fig. 2

Methodological framework. ADR adverse drug reaction, CCAE MarketScan Commercial Claims and Encounters, EHR Electronic health record, FAERS FDA Adverse Event Reporting System, GE EHR GE Healthcare MQIC (Medical Quality Improvement Consortium) database, NYP/CUMC New York Presbyterian Hospital at Columbia University Medical Center, OMOP Observational Medical Outcomes Partnership

Fig. 3

Histograms of signal scores when combining FAERS with the three healthcare data sets. Signal scores for FAERS and the EHR are signified by log odds ratio, and signal scores for the GE EHR and the claims data are signified by log relative risks. EHR Electronic health record, FAERS FDA Adverse Event Reporting System, GE EHR GE Healthcare MQIC (Medical Quality Improvement Consortium) database, NYP/CUMC New York Presbyterian Hospital at Columbia University Medical Center