Habib Bettina, Tamblyn Robyn, Girard Nadyne, Eguale Tewodros, Huang Allen
Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC, H3A 1A3, Canada.
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
BMC Health Serv Res. 2021 Apr 23;21(1):376. doi: 10.1186/s12913-021-06346-y.
Administrative 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.
We 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.
Of 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.
Combining 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.
行政健康数据越来越多地用于检测药物不良事件（ADEs）。然而，少数评估用于ADE检测的诊断代码的研究显示敏感性较低，这可能是由于代码集狭窄、医生对ADEs认识不足以及行政数据报告不充分所致。本研究的目的是确定将行政数据中扩展的国际疾病分类（ICD）代码集与电子处方数据相结合是否能提高ADE检测率。
我们对在初级保健机构新开具抗抑郁药或抗高血压药并随访2个月的患者进行了一项前瞻性队列研究。金标准ADEs定义为经临床专家判定与药物相关的患者报告症状。行政数据中的潜在ADEs定义为随访期间因研究药物已知不良反应而进行的医生诊疗、急诊就诊或住院治疗，通过ICD代码识别。电子处方数据中的潜在ADEs定义为随访期间因安全或有效性原因而进行的研究药物停药或剂量调整。
688名研究参与者中，445名（64.7%）为女性，平均年龄为64.2岁（标准差13.9）。386名（56.1%）患者的研究药物为抗高血压药，302名（43.9%）为抗抑郁药。采用金标准定义，114名（16.6%）患者发生了ADE，其中抗高血压药使用者中有40名（10.4%），抗抑郁药使用者中有74名（24.5%）。扩展的ICD代码集的敏感性为7.0%，电子处方数据的敏感性为9.7%，两者结合的敏感性为14.0%。特异性较高（86.0 - 95.0%）。当分析仅限于27%表示已向医生报告症状的患者时，联合方法的敏感性提高到25.8%。
将扩展的诊断代码集与电子处方数据相结合可提高ADE检测率。由于很少有患者向医生报告症状，通过患者门户和移动健康应用等新兴数字技术收集患者报告的结局，可能会实现更高的检测率。
