Electronic health records (EHRs) are expected to be a good source of data for pharmacovigilance. However, current quantitative methods are not applicable to EHR data. We propose a novel quantitative postmarketing surveillance algorithm, the Comparison of Laboratory Extreme Abnormality Ratio (CLEAR), for detecting adverse drug reaction (ADR) signals from EHR data. The methodology involves calculating the odds ratio of laboratory abnormalities between a specific drug-exposed group and a matched unexposed group. Using a 10-year EHR data set, we applied the algorithm to test 470 randomly selected drug-event pairs. It was found possible to analyze a single drug-event pair in just 109 ± 159 seconds. In total, 120 of the 150 detected signals corresponded with previously reported ADRs (positive predictive value (PPV) = 0.837 ± 0.113, negative predictive value (NPV) = 0.659 ± 0.180). By quickly and efficiently identifying ADR signals from EHR data, the CLEAR algorithm can significantly contribute to the utilization of EHR data for pharmacovigilance.

Information about the QT interval from surface electrocardiograms (ECGs) is essential for surveillance of the proarrhythmia potential of marketed drugs. However, ECG records obtained in daily practice cannot be easily used for this purpose without labor-intensive manual effort. This study was aimed at constructing an open-access QT database, the Electrocardiogram Vigilance with Electronic Data Warehouse (ECG-ViEW). This longitudinal observational database contains 710,369 measurements of QT and associated clinical data from 371,401 patients. The de-identified database is freely available at http://www.ecgview.org.