Association of Polypharmacy with Survival, Complications, and Healthcare Resource Use after Elective Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Polypharmacy is increasingly prevalent in older patients and is associated with adverse events among medical patients. The impact of polypharmacy on outcomes after elective surgery is poorly described. The authors' objective was to measure the association of polypharmacy with survival, complications, and resource use among older patients undergoing elective surgery. METHODS: After registration (NCT03133182), the authors identified all individuals older than 65 yr old having their first elective noncardiac surgery in Ontario, Canada, between 2002 and 2014. Using linked administrative data, the authors identified all prescriptions dispensed in the 90 days before surgery and classified people receiving five or more unique medications with polypharmacy. The associations of polypharmacy with 90-day survival (primary outcome), complications, length of stay, costs, discharge location, and readmissions were estimated after multilevel, multivariable adjustment for demographics, comorbidities, previous healthcare use, and surgical factors. Prespecified and post hoc sensitivity analyses were also performed. RESULTS: Of 266,499 patients identified, 146,026 (54.8%) had polypharmacy. Death within 90 days occurred in 4,356 (3.0%) patients with polypharmacy and 1,919 (1.6%) without (adjusted hazard ratio = 1.21; 95% CI, 1.14 to 1.27). Sensitivity analyses demonstrated no increase in effect when only high-risk medications were considered and attenuation of the effect when only prescriptions filled in the 30 preoperative days were considered (hazard ratio = 1.07). Associations were attenuated or not significant in patients with frailty and higher comorbidity scores. CONCLUSIONS: Older patients with polypharmacy represent a high-risk stratum of the perioperative population. However, the authors' findings call into question the causality and generalizability of the polypharmacy-adverse outcome association that is well documented in nonsurgical patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".