Adverse drug events related to polypharmacy in older adults: Defining outcomes and identifying opportunities for safer prescribing
Bibliographic record
Abstract
Background: Polypharmacy, or the concurrent use of multiple medications, is prevalent in adults aged 65 years and older.These patients are vulnerable to adverse drug events (ADEs), due to age-related physiologic changes, the co-prescription of multiple medications, and the use of potentially inappropriate medications (PIMs).Deprescribing is a proposed solution for managing polypharmacy and reducing ADEs.The primary outcome of interest is to reduce ADEs, without a concurrent increase in adverse drug withdrawal events (ADWE).Currently, there is no universally accepted gold standard adjudication method for use in clinical trials of deprescribing, to measure this outcome, nor a method specifically designed to capture ADWEs.Objectives: The primary goal of my thesis was to familiarize myself with polypharmacy and the challenges faced when deprescribing in a clinical setting.I sought to perform a thorough literature review to identify, compare, and contrast existing approaches for the adjudication of ADEs.Secondly, based on extensive research, I took the initial steps to craft a new method in order to allow researchers to easily and more accurately capture rates of ADEs (including ADWEs) in deprescribing interventions.A third objective of my research was to apply the principles of safer prescribing to special populations including; patients on risky medications, patients with chronic illnesses, and patients with COVID-19.Methods and Results: Objective #1-Methods to Adjudicate Adverse Drug Events, I performed a systematic review of the literature, compared and contrasted the identified adjudication methods.I identified 10 unique ADE adjudication methods.Objective #2-New Method of ADE Adjudication, I made recommendations for an updated methodology.This new method is easy to use, applicable in a variety of settings, has an ADWE component.Objective #3-COVID-SAFER, I theoretically exposed a cohort of patients 65 years and older enrolled in a deprescribing study to hydroxychloroquine.The cohort contained a total of 1,001 unique patients, of which, 590 (58.9%) had one or more home medications that could potentially interact with hydroxychloroquine, and of these 255 (43.2%) were flagged as potentially inappropriate by the MedSafer tool.Discussion: The primary aim of my thesis was to critically analyse the literature.I identified ADE adjudication methods, compared and contrasted their strengths and limitations, and Contribution of Authors
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".