Prevalence of medication overload among older people with HIV: a MedSafer study
Bibliographic record
Abstract
BACKGROUND: Older people with HIV (PWH) are at risk of polypharmacy (taking multiple medications). Most medications may be necessary and indicated to manage HIV (e.g., antiretroviral therapy [ART]) and HIV-associated comorbidities. However, some are potentially inappropriate medications (PIMs), defined as causing greater harm than benefit, which leads to medication overload. The objective of this study was to characterize polypharmacy (taking multiple medications) and medication overload (prescription of ≥ 1 PIMs) among older PWH. METHODS: This retrospective study included older PWH (aged ≥ 50 years old) attending the tertiary care HIV clinic at the McGill University Health Centre (Montreal, Canada), from June 2022-June 2023. Patient characteristics, medications, and select laboratory values (e.g., CD4 count, hemoglobin A1C) were entered into the MedSafer software identifying PIMs and classifying them according to risk of adverse drug event. We measured the prevalence of polypharmacy (≥ 5 medications prescribed, both including and excluding ART) and medication overload (≥ 1 PIMs). Multivariable logistic regression identified factors associated with medication overload. RESULTS: The study included 100 patients, with a median age of 59 years (IQR = 54-63; range 50-82); 42% female. Polypharmacy affected 89% of patients when including antiretroviral therapy (ART) and 60% when excluding ART. Medication overload was present in 58% of patients, and 37.4% of identified PIMs were classified as high-risk. Polypharmacy was the sole predictor of medication overload. CONCLUSION: Older PWH are at significant risk of medication overload and receiving higher risk PIMs. Deprescribing PIMs in this population could improve medication appropriateness while reducing the risk of ADEs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".