Division of Pharmacoepidemiology and Clinical Pharmacology, Department of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Br J Clin Pharmacol. 2023 Dec;89(12):3715-3752. doi: 10.1111/bcp.15872. Epub 2023 Sep 7.
Certain combinations of medications can be harmful and may lead to serious adverse drug events (ADEs). Identifying potentially problematic medication clusters could help guide prescribing and/or deprescribing decisions in hospital. The aim of this study is to characterize medication prescribing patterns at hospital discharge and determine which medication clusters were associated with an increased risk of ADEs in the 30-day posthospital discharge.
All residents of the province of Ontario in Canada aged 66 years or older admitted to hospital between March 2016 and February 2017 were included. Identification of medication clusters prescribed at hospital discharge was conducted using latent class analysis. Cluster identification and categorization were based on medications dispensed up to 30-day posthospitalization. Multivariable logistic regression was used to assess the potential association between membership to a particular medication cluster and ADEs postdischarge, while also evaluating other patient characteristics.
In total, 188 354 patients were included in the study cohort. Median age (interquartile range) was 77 (71-84) years, and patients had a median (IQR) (interquartile range [IQR]) of 9 (6-13) medications dispensed prior to admission. Within the study population, 6 separate clusters of dispensing patterns were identified: cardiovascular (14%), respiratory (26%), complex care needs (12%), cardiovascular and metabolic (15%), infection (10%), and surgical (24%). Overall, 12 680 (7%) patients had an ADE in the 30 days following discharge. After considering other patient characteristics, those belonging to the respiratory cluster had the highest risk of ADEs (adjusted odds ratio: 1.12, 95% confidence interval: 1.08-1.17) compared with all the other clusters, while those in the complex care needs cluster had the lowest risk (adjusted odds ratio: 0.82, 95% confidence interval: 0.77-0.87).
This study suggests that ADEs post hospital discharge can be linked with identifiable medication clusters. This information may help clinicians and researchers better understand patient populations that are more or less likely to benefit from peri-hospital discharge interventions aimed at reducing ADEs.
某些药物组合可能有害，并可能导致严重的药物不良事件（ADE）。识别潜在的问题药物组合可以帮助指导医院的处方决策和/或停药决策。本研究的目的是描述出院时的药物处方模式，并确定哪些药物组合与出院后 30 天内发生 ADE 的风险增加相关。
本研究纳入了 2016 年 3 月至 2017 年 2 月期间在加拿大安大略省住院的所有 66 岁或以上的居民。使用潜在类别分析识别出院时开出处方的药物组合。根据出院后 30 天内配药情况对药物组合进行识别和分类。多变量逻辑回归用于评估特定药物组合与出院后 ADE 之间的潜在关联，同时评估其他患者特征。
共有 188354 名患者纳入研究队列。中位年龄（四分位间距）为 77（71-84）岁，患者入院前平均（IQR）（四分位间距）有 9（6-13）种药物。在研究人群中，共确定了 6 种不同的配药模式：心血管（14%）、呼吸系统（26%）、复杂护理需求（12%）、心血管代谢（15%）、感染（10%）和手术（24%）。总体而言，出院后 30 天内有 12680 名（7%）患者发生 ADE。在考虑其他患者特征后，与其他所有药物组合相比，属于呼吸系统药物组合的患者发生 ADE 的风险最高（调整后的比值比：1.12，95%置信区间：1.08-1.17），而属于复杂护理需求药物组合的患者发生 ADE 的风险最低（调整后的比值比：0.82，95%置信区间：0.77-0.87）。
本研究表明，出院后发生的 ADE 可能与可识别的药物组合相关。这些信息可能有助于临床医生和研究人员更好地了解更有可能或不太可能受益于减少 ADE 的围出院干预的患者群体。
