Risk factors predictive of adverse drug events and drug-related falls in aged care residents: secondary analysis from the ReMInDAR trial
Bibliographic record
Abstract
BACKGROUND: Residents of aged-care facilities have high rates of adverse drug events. This study aimed to identify risk factors for adverse drug events in aged-care residents. METHOD: This was a secondary study using data from a multicentre randomised controlled trial. Data from 224 residents for whom there was 6 months of baseline information were analysed. We assessed the risk of adverse drug events and falls (post hoc) in the subsequent 6 months. Adverse events were identified via a key word search of the resident care record and adjudicated by a multidisciplinary panel using a modified version of the Naranjo criteria. Covariates identified through univariable logistic regression, including age, sex, medicines, physical activity, cognition (Montreal Cognitive Assessment), previous adverse events and health service use were included in multivariable models. RESULTS: Overall, 224 residents were included, with a mean age of 86 years; 70% were female. 107 (48%) residents had an adverse drug event during the 6-month follow-up. Falls and bleeding were experienced by 73 (33%) and 28 (13%) residents, respectively. Age (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.01-1.10), weight (OR 1.02, 95% CI 1.002-1.04), previous fall (OR 2.58, 95% CI 1.34-4.98) and sedative or hypnotic medicine use (OR 1.98, 95% CI 1.52-2.60) were associated with increased risk of adverse drug events. Increased cognition (OR 0.89, 95% CI 0.83-0.95) was protective. Risk factors for falls were previous fall (OR 3.27, 95% CI 1.68-6.35) and sedative or hypnotic medicines (OR 3.05, 95% CI 1.14-8.16). Increased cognition (OR 0.88, 95% CI 0.83-0.95) was protective. CONCLUSION: Our results suggest residents with a previous fall, reduced cognition, and prescription of sedative or hypnotic medicines were at higher risk of adverse drug events and should be considered for proactive prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".