Medication Clusters at Hospital Discharge and Risk of Adverse Drug Events at 30-days Post-Discharge: A Population-based Cohort Study of Older Adults
Bibliographic record
Abstract
ABSTRACT 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. KEY POINTS 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. PLAIN LANGUAGE SUMMARY Certain combinations of medications prescribed to patients when they are being discharged from hospital can increase the risk of adverse events after hospital discharge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".