Association of Polypharmacy and Potentially Inappropriate Medications With Frailty Among Older Adults With Blood Cancers
Bibliographic record
Abstract
BACKGROUND: Polypharmacy and potentially inappropriate medications (PIMs) are common among older adults with blood cancers, but their association with frailty and how to manage them optimally remain unclear. PATIENTS AND METHODS: From 2015 to 2019, patients aged ≥75 years presenting for initial oncology consult underwent screening geriatric assessment. Patients were determined to be robust, prefrail, or frail via deficit accumulation and phenotypic approaches. We quantified each patient's total number of medications and PIMs using the Anticholinergic Risk Scale (ARS) and a scale we generated using the NCCN Medications of Concern called the Geriatric Oncology Potentially Inappropriate Medications (GO-PIM) scale. We assessed cross-sectional associations of PIMs with frailty in multivariable regression models adjusting for age, gender, and comorbidity. RESULTS: Of 785 patients assessed, 603 (77%) were taking ≥5 medications and 421 (54%) were taking ≥8 medications; 201 (25%) were taking at least 1 PIM based on the ARS and 343 (44%) at least 1 PIM based on the GO-PIM scale. Among the 468 (60%) patients on active cancer treatment, taking ≥8 medications was associated with frailty (adjusted odds ratio [aOR], 2.82; 95% CI, 1.92-4.17). With each additional medication, the odds of being prefrail or frail increased 8% (aOR, 1.08; 95% CI, 1.04-1.12). With each 1-point increase on the ARS, the odds of being prefrail or frail increased 19% (aOR, 1.19; 95% CI, 1.03-1.39); with each additional PIM based on the GO-PIM scale, the odds increased 65% (aOR, 1.65; 95% CI, 1.34-2.04). CONCLUSIONS: Polypharmacy and PIMs are prevalent among older patients with blood cancers; taking ≥8 medications is strongly associated with frailty. These data suggest careful medication reconciliation for this population may be helpful, and deprescribing when possible is high-yield, especially for PIMs on the GO-PIM scale.
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 | 0.006 |
| 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 | 0.001 |
| 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".