Current and recommended practices for evaluating adverse drug events using electronic health records: A systematic review. Issue 11 (5th October 2021)

• Record Type: Journal Article
• Authors: Ng, Ding Quan; Dang, Emily; Chen, Lijie; Nguyen, Mary Thuy; Nguyen, Michael Ky Nguyen; Samman, Sarah; Nguyen, Tiffany Mai Thy; Cadiz, Christine Luu; Nguyen, Lee; Chan, Alexandre
• Is Part Of: Journal of the American College of Clinical Pharmacy. Volume 4:Issue 11(2021)
• Page Start: 1457
• Page End: 1468
• Publication Date: 2021-10-05
• Subjects: adverse reactions -- electronic health records -- electronic medical records -- medication safety -- methods -- pharmacoepidemiology; Pharmacy -- Periodicals; Pharmacy Service, Hospital; Periodicals; Electronic journals; Periodical; 615.105
• DOI: 10.1002/jac5.1524
• Languages: English
• ISSNs: 2574-9870

Abstract

Abstract: Electronic health records (EHR) are widely used sources of real‐world data in pharmacoepidemiologic research. As there is no end‐to‐end guidance for generating medication safety evidence with EHR, this study conducted a systematic review to determine the current and recommended practices in the literature. PubMed, Scopus, and CINAHL were searched for English articles published between 1 January 2010 and 11 June 2020. Selected articles were published in peer‐reviewed journals, conducted in the United States, analyzed structured EHR data, and defined drug exposure and adverse drug events (ADEs). The study evaluated methodological quality with a modified Newcastle‐Ottawa Scale (NOS) score ranging from 0 to 9 points. Data synthesis was performed with thematic analysis. Twenty‐six from 3885 articles were selected. The majority were cohort studies (85%). The studies were well designed, with a median NOS score of 9. Drug exposure was defined with dispensing (58%) and prescribing (31%) records. ADEs were defined across five categories: diagnosis codes (77%), validated outcome algorithms (35%), objective measures (35%), treatment procedures (19%), and antidotes (2%). Common covariates were age (89%), gender (85%), comorbidities (81%), and medication‐co‐medication use (73%). Four studies (15%) empirically defined covariates in a data‐driven manner. Twenty‐two (85%) analyzed covariates as confounders or effect modifiers in their analyses. Results were analyzed with either intention‐to‐treat (73%) or as‐treated (39%) approaches. Key recommendations include selecting dispensing rather than prescribing records, considering a proxy date of dispensation where applicable, selecting new instead of prevalent drug users, improving adoption of validated outcome algorithms, and not utilizing objective measures as the primary indicator of ADEs.