Risk of death and hospital admission for major medical events after initiation of psychotropic medications in older adults admitted to nursing homes
Bibliographic record
Abstract
BACKGROUND: Despite safety-related concerns, psychotropic medications are frequently prescribed to manage behavioural symptoms in older adults, particularly those with dementia. We assessed the comparative safety of different classes of psychotropic medications used in nursing home residents. METHODS: We identified a cohort of patients who were aged 65 years or older and had initiated treatment with psychotropics after admission to a nursing home in British Columbia between 1996 and 2006. We used proportional hazards models to compare rates of death and rates of hospital admissions for medical events within 180 days after treatment initiation. We used propensity-score adjustments to control for confounders. RESULTS: Of 10,900 patients admitted to nursing homes, atypical antipsychotics were initiated by 1942, conventional antipsychotics by 1902, antidepressants by 2169 and benzodiazepines by 4887. Compared with users of atypical antipsychotics, users of conventional antipsychotics and antidepressants had an increased risk of death (rate ratio [RR] 1.47, 95% confidence interval [CI] 1.14-1.91 for conventional antipsychotics and RR 1.20, 95% CI 0.96-1.50 for antidepressants), and an increased risk of femur fracture (RR 1.61, 95% CI 1.03-2.51 for conventional antipsychotics and RR 1.29, 95% CI 0.86-1.94 for antidepressants). Users of benzodiazepines had a higher risk of death (RR 1.28, 95% CI 1.04-1.58) compared with users of atypical antipsychotics. The RR for heart failure was 1.54 (95% CI 0.89-2.67), and for pneumonia it was 0.85 (95% CI 0.56-1.31). INTERPRETATION: Among older patients admitted to nursing homes, the risks of death and femur fracture associated with conventional antipsychotics, antidepressants and benzodiazepines are comparable to or greater than the risks associated with atypical antipsychotics. Clinicians should weigh these risks against the potential benefits when making prescribing decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".