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Stroke Risk Reduction in Atrial Fibrillation Through Pharmacist Prescribing

2024· article· en· W4400949639 on OpenAlexafffundabout
Roopinder K. Sandhu, Miriam Fradette, Meng Lin, Erik Youngson, Darren Lau, Tammy J. Bungard, Ross T. Tsuyuki, Lisa Dolovich, Jeff S. Healey, Finlay A. McAlister

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of TorontoMcMaster UniversityAlberta Health ServicesLibin Cardiovascular Institute of AlbertaPopulation Health Research InstituteUniversity of AlbertaCanadian VIGOUR CentreUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsAtrial fibrillationStroke (engine)PharmacistMedicineStroke riskCardiologyInternal medicinePharmacyIschemic strokeFamily medicine

Abstract

fetched live from OpenAlex

Importance: Major gaps in the delivery of appropriate oral anticoagulation therapy (OAC) exist, leaving a large proportion of persons with atrial fibrillation (AF) unnecessarily at risk for stroke and its sequalae. Objective: To investigate whether pharmacist-led OAC prescription can increase the delivery of stroke risk reduction therapy in individuals with AF. Design, Setting, and Participants: This prospective, open-label, patient-level randomized clinical trial of early vs delayed pharmacist intervention from January 1, 2019, to December 31, 2022, was performed in 27 community pharmacies in Alberta, Canada. Pharmacists identified patients 65 years or older with 1 additional stroke risk factor and known, untreated AF (OAC nonprescription or OAC suboptimal dosing) or performed screening using a 30-second single-lead electrocardiogram to detect previously unrecognized AF. Patients with undertreated or newly diagnosed AF eligible for OAC therapy were considered to have actionable AF. Data were analyzed from April 3 to November 30, 2023. Interventions: In the early intervention group, pharmacists prescribed OAC using guideline-based algorithms with follow-up visits at 1 and 3 months. In the delayed intervention group, which served as the usual care control, the primary care physician (PCP) was sent a notification of actionable AF along with a medication list (both enhancement over usual care). After 3 months, patients without OAC optimization in the control group underwent delayed pharmacist intervention. Main Outcomes and Measures: The primary outcome was the difference in the rate of guideline-concordant OAC use in the 2 groups at 3-month follow-up ascertained by a research pharmacist blinded to treatment allocation. Results: Eighty patients were enrolled with actionable AF (9 [11.3%] newly diagnosed in 235 individuals screened). The mean (SD) age was 79.7 (7.4) years, and 45 patients (56.3%) were female. The median CHADS2 (congestive heart failure, hypertension, age, diabetes, and stroke or transient ischemic attack) score was 2 (IQR, 2-3). Seventy patients completed follow-up. Guideline-concordant OAC use at 3 months occurred in 36 of 39 patients (92.3%) in the early intervention group vs 23 of 41 (56.1%) in the control group (P < .001), with an absolute increase of 34% and number needed to treat of 3. Of the 23 patients who received appropriate OAC prescription in the control group, the PCP called the pharmacist for prescribing advice in 6 patients. Conclusions and Relevance: This randomized clinical trial found that pharmacist OAC prescription is a potentially high-yield opportunity to effectively close gaps in the delivery of stroke risk reduction therapy for AF. Scalability and sustainability of pharmacist OAC prescription will require larger trials demonstrating effectiveness and safety. Trial Registration: ClinicalTrials.gov Identifier: NCT03126214.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.386
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2024
Admission routes3
Has abstractyes

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