Insights from the AQUA Registry: a retrospective study of anticholinergic polypharmacy in the United States
Bibliographic record
Abstract
Background: Anticholinergic (ACH) burden is a risk factor for negative health outcomes among older adults. Several medications contribute to ACH burden, including antimuscarinics used to manage overactive bladder (OAB). Objectives: This study aimed to understand the extent of ACH burden in an OAB population in the United States. Design: Non-interventional retrospective analysis. Methods: Adults with OAB whose care providers participated in the American Urological Association Quality (AQUA) Registry between 2014 and 2020 were included in this study. An adapted version of the Pharmacy Quality Alliance (PQA) measure of anticholinergic polypharmacy (poly-ACH) was used to assess ACH burden. The primary outcome was the annual prevalence of poly-ACH, and a secondary outcome was the percentage of patients taking 0, 1, 2, 3, 4, or ⩾ 5 ACH medications by calendar year. Analyses were stratified by age category at diagnosis and sex. Results: The sample comprised 552,840 patients with OAB. The mean age at initial OAB diagnosis was 65.7 years (58.2% male; 57.4% white). Prevalence of poly-ACH was highest in 2015 (3.7%) and lowest in 2020 (1.9%). Patients prescribed no ACH medications made up the largest proportion of each cohort, while those prescribed five or more comprised the smallest. The trend of decreasing proportions of patients taking increasing numbers of ACH medications was consistent. The proportion of patients prescribed no ACH medications increased from 63.3% in 2014 to 74.6% in 2020. The percentage of those prescribed three or more ACHs remained largely unchanged. Poly-ACH was highest among younger individuals (< 65 years of age) and females; temporal trends were similar overall and within each age and sex stratum. Conclusion: In this study, poly-ACH in patients with OAB was relatively infrequent and decreased over the study period. Further evaluation of poly-ACH is needed to assess whether the study findings reflect increased awareness of the negative effects of poly-ACH.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".