Association of Preoperative Anticholinergic Medication Exposure With Postoperative Healthcare Resource Use and Outcomes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to measure the association of preoperative anticholinergic exposure with length of stay (LOS) and other outcomes in older people having elective noncardiac surgery. SUMMARY BACKGROUND DATA: Anticholinergic medications are associated with adverse events in nonsurgical populations; the association of anticholinergic medications with outcomes in elective surgery patients is poorly described. METHODS: We conducted a retrospective, population-based cohort study using linked administrative data in Ontario, Canada. We identified all people >65 years old, from 2003 to 2014, having major, elective noncardiac surgery. Anticholinergic medication exposure was quantified using the Anticholinergic Risk Scale (ARS). Multilevel, multivariable modeling measured the adjusted association of ARS with LOS (primary outcome), institutional discharge, readmissions, costs, and survival (secondary outcomes). RESULTS: Of 245,410 individuals, 71,569 had anticholinergic exposure (ARS 1-2, 15.6%; ARS ≥3, 13.6%). Median LOS was 5 days (interquartile range 3-7). Using proportional hazards analysis to model time to discharge, adjusting for in-hospital death as a competing risk, and surgical risk, demographic characteristics, and comorbidities, higher ARS scores were associated with longer LOS [smaller hazard ratios (HRs) mean longer LOS; ARS 1-2: adjusted HR 0.94, 95% confidence interval (CI), 0.93-0.95, P < 0.0001; ARS ≥3: adjusted HR 0.93, 95% CI, 0.91-0.95, P < 0.0001]. Similar associations were observed for all secondary outcomes. CONCLUSIONS: Increasing ARS scores were associated with increased LOS, decreased survival, higher rates of institutional discharge and readmission, and higher costs of care. Perioperative interventional research to reduce the anticholinergic exposure in older surgical patients is likely warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".