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Medication Clusters at Hospital Discharge and Risk of Adverse Drug Events at 30-days Post-Discharge: A Population-based Cohort Study of Older Adults

Background: Certain combinations of medications can be harmful and may lead to serious drug-drug interactions. Identifying potentially problematic medication clusters could help guide prescribing decisions in hospital. Objectives: To characterize medication prescribing patterns at hospital discharge and determine which medication clusters are associated with an increased risk of adverse drug events (ADEs) in the 30-days post hospital discharge. Methods: All residents of the province of Ontario in Canada aged 66 years or older admitted to hospital between March 2016-February 2017 were included. Identification of medication prescribing clusters at hospital discharge was conducted using latent class analysis. Cluster identification was based on medications dispensed 30-days post-hospitalization. Multivariable logistic regression was used to assess the potential association between membership to a particular medication cluster and ADEs post-discharge, while also evaluating other patient characteristics. Results: 188,354 patients were included in the study cohort. Median age (IQR) was 77 (71-84) and patients had a median (IQR) of 9 (6-13) medications dispensed in the year prior to admission. The study population consisted of 6 separate clusters of dispensing patterns post discharge: 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 in the respiratory cluster had the highest risk of ADEs (aOR: 1.12, 95% CI: 1.08-1.17) compared to all the other clusters, while those in the neurocognitive & complex care needs cluster had the lowest risk (aOR:0.82, 95% CI: 0.77-0.87). Conclusion: This study suggests that ADEs post hospital discharge are linked to identifiable clusters of medications, in addition to non-modifiable patient characteristics, such as age and certain comorbidities. This information may help clinicians and researchers better understand what patient populations and which types of interventions may benefit patients, to reduce their risk of experiencing an ADE.