Polypharmacy in older adults after transcatheter or surgical aortic valve replacement
Bibliographic record
Abstract
Abstract Background Polypharmacy is associated with higher rates of adverse drug events and unplanned hospital visits in medical patients. Little is known about polypharmacy in frail older adults undergoing transcatheter (TAVR) or surgical (SAVR) aortic valve replacement. Purpose To determine the prevalence and prognostic implications of polypharmacy and potentially inappropriate medications (PIM) following TAVR or SAVR. Methods A post hoc analysis of the McGill Frailty Registry was conducted. Patients 70 years of age or older who were discharged alive after TAVR or SAVR at two university hospitals were included. Discharge prescriptions were codified and analyzed using the MedSafer electronic tool that has been validated to flag drug interactions and PIMs considering patient-specific comorbidities. Associations with the primary outcome of 30-day all-cause readmission were examined by logistic regression after adjusting for age, sex, Charlson Comorbidity Index, and procedure type. Results The cohort consisted of 495 patients (52% TAVR, 21% isolated SAVR, 27% combined SAVR). The mean age was 80.1±5.5 years with 52% females. The mean number of medications was 10.2±3.7 with 90% having 5 or more medications. A total of 55 patients were readmitted within 30 days. While the total number of medications was not predictive, three specific PIMs were found to be harmful and one PIM was found to be protective for readmission: clopidogrel with warfarin or heparin (OR 3.99; 95% CI 1.47, 10.82), diltiazem with heart failure (OR 3.16; 95% CI 1.04, 36.41), doxazosin or terazosin with hypertension (OR 6.21; 95% CI 0.99, 38.88), and any proton pump inhibitor (OR 0.47; 95% CI 0.26, 0.86). Of note, the combination of clopidogrel with direct oral anticoagulants was not found to be harmful for readmission. Conclusion The prevalence of polypharmacy is elevated in older patients undergoing TAVR or SAVR. Specific PIMs, but not total number of medications prescribed, were found to be associated with potentially preventable readmissions. Funding Acknowledgement Type of funding sources: None.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".