Association of Polypharmacy and Potentially Inappropriate Medications With Outcomes in Older Adults With DLBCL: A Population Study
Bibliographic record
Abstract
BACKGROUND: Polypharmacy and potentially inappropriate medications (PIMs) may be associated with survival and health care utilization in older patients undergoing treatment for diffuse large B-cell lymphoma (DLBCL). METHODS: This population-based study examined patients with DLBCL aged ≥66 years receiving rituximab-based therapy from January 1, 2006, to December 31, 2017, and followed them until March 31, 2019. Polypharmacy was defined as taking ≥5 or ≥8 concurrent medications within 90 days of treatment initiation. PIMs were assessed using the Anticholinergic Risk Scale (ARS) and the Geriatric Oncology Potentially Inappropriate Medications (GO-PIM) scale. Cox regression and negative binomial models were conducted, adjusting for age, sex, frailty, and comorbidity burden (Aggregated Diagnosis Groups [ADGs]). The primary outcome was all-cause mortality, and the secondary outcome was health care utilization, measured by unplanned emergency department visits and hospitalizations. RESULTS: A total of 5,527 patients were included (median age, 75 years; 48% female), of whom 69% and 40% had polypharmacy defined as ≥5 and ≥8 medications, respectively. In terms of PIMs, 27% of patients had at least one PIM based on ARS, whereas 70% had a high-risk medication based on the GO-PIM scale. Polypharmacy was associated with increased risk of all-cause mortality in the adjusted analysis, with an adjusted hazard ratio (aHR) of 1.14 (95% CI, 1.05-1.23; P=.0021) for patients taking ≥5 medications, and 1.18 (95% CI, 1.09-1.27; P<.0001) for those taking ≥8 medications. Increasing number of PIMs was associated with increased mortality risk. Polypharmacy was associated with an increased relative risk of health care utilization, with an adjusted rate ratio (aRR) of 1.14 (95% CI, 1.06-1.22; P=.0004) for patients taking ≥5 medications, and 1.16 (95% CI, 1.08-1.24; P<.0001) for those taking ≥8 medications. For PIMs, a higher score on the GO-PIM scale was associated with greater risk of health care utilization (aRR for ≥3 medications, 1.20; 95% CI, 1.09-1.32; P=.0003), whereas ARS was not. CONCLUSIONS: Polypharmacy and PIMs are associated with an increased relative risk of mortality and health care utilization among older adults with DLBCL undergoing treatment, independent of frailty and comorbidity.
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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 | 0.002 |
| 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 | 0.001 | 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".