Polypharmacy and risk of mortality among patients with heart failure following hospitalization: a nested case–control study
Bibliographic record
Abstract
Heart failure (HF) is associated with morbidity, rehospitalization and polypharmacy. The incidence rate of mortality in HF patients with polypharmacy is poorly studied. We examine the association of polypharmacy with mortality risk in incident hospitalized HF patients with a primary diagnosis after discharge from the hospital using Quebec administrative databases, Canada from 1999 to 2015. Polypharmacy, cardiovascular (CV) polypharmacy and non-CV polypharmacy were respectively defined as exposure to ≥ 10 drugs, ≥ 5 CV drugs and ≥ 5 non-CV drugs within three months prior to the case or the control selection date. We conducted a nested case-control study to estimate rate ratios (RR) of all-cause mortality using a multivariate conditional logistic regression during one-year of follow-up. We identified 12,242 HF patients with a mean age of 81.6 years. Neither CV polypharmacy (RR 0.97, 95%CI 0.82-1.15) nor non-CV polypharmacy (RR 0.93, 95%CI 0.77-1.12) were associated with lower mortality risk. However, all polypharmacy (RR 1.31, 95%CI 1.07-1.61) showed an association with mortality risk. Myocardial infarction, valvular disease, peripheral artery disease, diabetes, major bleeding, chronic kidney disease, high comorbidity score, high Frailty score, hydralazine and spironolactone users were associated with increasing mortality risk, ranging from 15 to 61%, while use of angiotensin II inhibitors, beta-blockers, statins, anticoagulant, and antiplatelets were associated with lower risk, ranging from 23 to 32%.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".