Potentially Inappropriate Medication Use in Older Adults in the Preoperative Period: A Retrospective Study of a Noncardiac Surgery Cohort
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the prevalence of potentially inappropriate medications (PIMs) and its association with postoperative outcomes in a geriatric population in the preoperative setting. OBJECTIVES: The purpose of this study was to evaluate the prevalence of PIMs in an older elective surgery population and to explore associations between PIMs and postoperative length of stay (LOS) and emergency department (ED) visits in the 90 days post hospital discharge, depending on frailty status. METHODOLOGY: We performed a retrospective cohort study of older adults awaiting major elective noncardiac surgery and undergoing an evaluation in the preoperative clinic at a tertiary academic center between 2017 and 2018. We identified PIMs using MedSafer, a software tool built to improve the safety of prescribing. Frailty status was assessed using the 7-point Clinical Frailty Scale. We estimated the association between PIMs and postoperative LOS and ED visits in the 90 days post hospital discharge. RESULTS: The MedSafer software generated 394 recommendations on PIMs in 1619 medications for 252 patients. In total, 197 (78%) patients had at least one PIM. The cohort included 138 (51%) robust, 87 (32.2%) vulnerable and 45 (16.7%) frail patients. The association between PIMs and LOS was not significant for the robust and frail subgroups. For the vulnerable patients, every additional PIM increased LOS by 20% (incidence rate ratio 1.20; 95% confidence interval 0.90-1.44; p = 0.089) without reaching statistical significance. No association was found between PIMs and ED visits. CONCLUSION: PIMs identified by the MedSafer software were prevalent. Preoperative evaluation represents an opportunity to plan deprescribing of PIMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".