Characterizing PRN Use of Psychotropic Medications for Acute Agitation in Canadian LTC Residents With Dementia Before and During COVID‐19
Bibliographic record
Abstract
Abstract Background Agitation is a prevalent and disabling neuropsychiatric symptom of dementia that comprises a constellation of behaviours involving excessive motor activity, physical aggression, and verbal aggression. Pro re nata (PRN) injections of antipsychotics and benzodiazepines can be administered for severe acute agitation, but best practices surrounding their use remain poorly characterized. The aim of this study was to compare the frequency of use of injectable PRN haloperidol, olanzapine, and lorazepam for acute agitation in Canadian long‐term care (LTC) residents with dementia before and during the COVID‐19 pandemic. Methods A retrospective chart review was conducted for patients from two Canadian LTC facilities who had orders for PRN haloperidol, olanzapine, or lorazepam between January 1, 2018–May 1, 2019 (i.e. pre‐COVID‐19) and January 1, 2020–May 1, 2021 (i.e. COVID‐19). We reviewed electronic medical records to document PRN administrations of these psychotropic medications and collect data on age, sex, dementia diagnosis, days of observation, care type, medical history, and concomitant medications. Descriptive statistics and multivariate logistic and Poisson regression models were used to analyze the data. Results Of the 250 residents with dementia included, 45 of 103 (44%) people in the pre‐COVID‐19 period and 85 of 147 (58%) people in the COVID‐19 period with standing orders for injectable PRN haloperidol, olanzapine, or lorazepam received ≥1 injections of their prescribed medication. Haloperidol was the most frequently used agent (74% pre‐COVID‐19 and 81% during COVID‐19) and excessive motor activity (85% and 80%) was the most common indication for use in both time periods. Residents in the COVID‐19 period were two times more likely to receive PRN injections compared with those in the pre‐COVID‐19 period (OR = 1.96; 95% CI = 1.15‐3.34; p = 0.01). Count of injections per patient was not significantly different between the two time periods (IRR = 1.35; 95% CI = 0.78‐2.33; p = 0.3). Conclusion Our results contribute to the mounting evidence that agitation worsened in LTC residents with dementia during the pandemic and add to the limited existing literature on actual use of PRN psychotropic medications for acute agitation. The increased need for PRN rescue medications may be an indirect consequence of COVID‐19 restrictions, particularly the restrictions on socialization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".