Antihypertensive deprescribing in frail long-term care residents (OptimizeBP): protocol for a prospective, randomised, open-label pragmatic trial
Bibliographic record
Abstract
Introduction Although antihypertensive medication use is common among frail older adults, observational studies in this population suggest blood pressure (BP) lowering may convey limited benefit and perhaps even harm. This protocol describes an antihypertensive deprescribing trial in frail older adults powered for mortality and morbidity outcomes. Methods and analysis Design: Prospective, parallel, randomised, open-label pragmatic trial. Participants: Long-term care (LTC) residents ≥70 years of age, diagnosed with hypertension, with mean systolic BP <135 mm Hg, ≥1 daily antihypertensive medication and no history of congestive heart failure. Setting: 18 LTC facilities in Alberta, Canada, with eligible residents identified using electronic health services data. Intervention: All non-opted-out eligible residents are randomised centrally by a provincial health data steward to either usual care, or continually reducing antihypertensives provided an upper systolic threshold of 145 mm Hg is not exceeded. Deprescribing is carried out by pharmacists/nurse practitioners, using an investigator-developed algorithm. Follow-up: Provincial healthcare databases tracking hospital, continuing care and community medical services. Primary outcome: All-cause mortality. Secondary outcome: Composite of all-cause mortality or all-cause unplanned hospitalisation/emergency department visit. Tertiary outcomes: All-cause unplanned hospitalisation/emergency department visit, non-vertebral fracture, renal insufficiency and cost of care. Also, as assessed roughly 135-days postrandomisation, fall in the last 30 days, worsening cognition, worsening activities of daily living and skin ulceration. Process outcomes: Number of daily antihypertensive medications (broken down by antihypertensive class) and average systolic and diastolic BP over study duration. Primary outcome analysis: Cox proportional hazards survival analysis. Sample size: The trial will continue until observation of 247 primary outcome events has occurred. Current status: Enrolment is ongoing with ~400 randomisations to date (70% female, mean age 86 years). Ethics and dissemination Ethics approval was obtained from the University of Alberta Health Ethics Review Board (Pro00097312) and results will be published in a peer-reviewed journal. Trial registration number NCT05047731 .
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.085 | 0.017 |
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".