Utilizing population-based clinical and administrative data to explore the relevance of frailty to cholinesterase inhibitor use and discontinuation at nursing home transition.
Bibliographic record
Abstract
IntroductionCholinesterase inhibitors (ChEIs) are medications used to treat cognitive symptoms associated with Alzheimer’s disease. Previous studies examining the determinants of continued use or withdrawal of ChEIs during the transition into a nursing home have lacked detailed clinical information needed to understand the range of factors associated with pharmacotherapeutic decision-making. Objectives and ApproachPopulation-based clinical and administrative health databases were linked to examine patterns of ChEI use among 47,851 adults (aged 66+) with dementia newly admitted to nursing homes in Ontario between April 2011-March 2015. We examined whether resident frailty, among other factors, was associated with ChEI discontinuation in the following year. Frailty was calculated using a validated 72-item index derived from the Resident Assessment Instrument (RAI-MDS 2.0). Discontinuation was defined as a 30-day period when no dispensations occurred and no supply of ChEI was available. Subdistribution hazard models estimated the association between resident characteristics and discontinuation, accounting for competing risk of death. ResultsOver one-third (36.7%) of residents were receiving a ChEI at admission and this proportion was lower among those defined as frail (33.6%) vs. non-frail (40.7%) at admission. Among those on a ChEI at admission, 82.3% continued use and 17.7% discontinued during the following year. After accounting for resident characteristics, ChEI type and previous use, the incidence of discontinuation was 15% higher in frail residents vs. non-frail residents (hazard ratio (HR)= 1.15, 95\% confidence interval (CI) [1.01,1.30]). Residents with severe aggressive behaviours (HR=1.82, 95% CI [1.60, 2.07]), and higher levels of cognitive impairment (HR=1.29, 95% CI [1.10, 1.51]) were more likely to discontinue. Residents aged 85+ (HR=0.69, 95% CI [0.61, 0.77]) and those who were widowed (HR=0.84, 95% CI [0.77, 0.91]) were less likely to discontinue. Conclusion/ImplicationsMost residents who entered on a ChEI continued treatment during follow-up. The availability of linked clinical and administrative data allowed for a novel exploration of predictors of ChEI discontinuation. Frailty, severity of cognitive impairment and aggressive behaviours were associated with ChEI discontinuation; whereas selected sociodemographic factors predicted continued use.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".