Detection of adverse drug events in e-prescribing and administrative health data: a validation study

Bettina Habib,Robyn Tamblyn,Nadyne Girard,Tewodros Eguale,Allen Huang

doi:10.1186/s12913-021-06346-y

Abstract

BackgroundAdministrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection.MethodsWe conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons.ResultsOf 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician.ConclusionCombining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

Highlights

Administrative health data are increasingly used to detect adverse drug events (ADEs)
Research design We conducted a prospective cohort study to assess the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of using treatment change orders in e-prescribing data and diagnostic codes in administrative health data to detect adverse events attributable to medications prescribed in a primary care setting
We found that the sensitivity of our combined approach was improved almost two-fold when the analysis was restricted to Discussion In this validation study, we found that treatment change orders in e-prescribing data and diagnostic codes in administrative health data, individually and combined, enhanced the identification of ADEs caused by antidepressant and antihypertensive medications prescribed in a primary care setting compared to the standard code set

Summary

Introduction

Administrative health data are increasingly used to detect adverse drug events (ADEs). While modern drug therapy plays an important role in managing health problems, adverse drug events (ADEs) are frequent and costly, with up to 16% of emergency department (ED) visits and 7% of hospital admissions being medicationrelated [3,4,5,6]. The frequency of these events can be explained in part by established processes for drug approval, which test drugs in tightly controlled settings and in a limited number of patients who infrequently represent those typically prescribed the drug after approval. Robust methods of post-marketing surveillance have emerged as a requirement to monitor the safety and effectiveness of medications after they have been approved for sale [12,13,14]

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