Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 13, 2021
Date Accepted: Apr 17, 2021
Date Submitted to PubMed: May 16, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The method to detect the adverse drug events through the chronological relationship between the medication period and the presence of adverse reactions from electronic medical record systems.
ABSTRACT
Background:
Taking medicine may cause a variety of adverse reactions. An enormous amount of money and effort are spent investigating adverse drug events (ADEs) in clinical trials and post-marketing surveillance. Real world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients.
Objective:
In this study, we generated a database of the patients’ medication history from the records of physician orders of EMR, which allowed the period of medication to be clearly identified.
Methods:
We developed the method to detect the ADE based on the chronological relationship between the presence of the adverse event and the medication period. To verify our method, we detected the ADE with alanine aminotransferase (ALT) elevation for aspirin, clopidogrel and ticlopidine. The accuracy of detecting ADE were examined by chart review and by the comparison with Roussel Uclaf Causality Assessment Method (RUCAM) which was known as standard method for detecting drug induced liver injury.
Results:
The calculated rates of ADE with ALT elevation for aspirin, clopidogrel and ticlopidine were 3.33%, 3.70% and 5.69%, respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADE were detected. Our method accurately predicted ADE in 90%, 100% and 100%, of patients with ALT elevation from aspirin, clopidogrel, and ticlopidine, respectively. With the comparison of the RUCAM, only 3 patients were not detected as ADE by our method.
Conclusions:
These findings demonstrate that the present method is effective for detecting ADE from EMR data.
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