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Record W4366241946 · doi:10.1371/journal.pone.0284425

Quality of anticoagulant control and patient experience associated with long-term warfarin in Canadian patients with non-valvular atrial fibrillation: A multicentre, prospective study

2023· article· en· W4366241946 on OpenAlexaffabout
Rita Selby, Lisa Kaus, Faith Sealey, Marika Koo, Sameer Parpia, Brian Chan, Soo Jin Seung, Carole A. Bradley, Rachel Strauss, Nicole Mittmann

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsInstitute for Clinical Evaluative SciencesImpactHealth Sciences CentreBoehringer Ingelheim (Canada)McMaster UniversityUniversity Health NetworkUniversity of TorontoToronto Rehabilitation InstituteSunnybrook Health Science Centre
FundersBoehringer Ingelheim
KeywordsWarfarinMedicineAtrial fibrillationEmergency medicineAnticoagulantInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the fact that direct oral anticoagulants (DOACs) are favoured over warfarin for stroke prevention in patients with non-valvular atrial fibrillation (NVAF), physicians need to maintain competence in using and monitoring warfarin since many patients have contraindications or other barriers to using DOACs. Unlike DOACs, warfarin therapy requires regular blood testing to ensure that it is within a target range to ensure efficacy and safety. There is limited real-world data on the adequacy of warfarin control and the cost and burden of monitoring warfarin therapy in Canadian NVAF patients. OBJECTIVES: In a large cohort of Canadian patients with NVAF on warfarin we assessed time in therapeutic range (TTR), determinants of TTR, process of care, direct costs, health related quality of life and loss of work time and productivity related to warfarin therapy. METHODS: Five hundred and fifty one patients with NVAF, either newly initiated or stable on warfarin were prospectively enrolled across 9 Canadian provinces from primary care practices and anticoagulant clinics. Participating physicians provided baseline demographic and medical information. Patients completed diaries for 48 weeks, capturing information about International Normalized Ratio (INR) test results, test locations, process of INR monitoring, direct costs of travel, health-related quality of life and work productivity measures. TTR was estimated using linear interpolation of INR results and linear regression used to investigate associations between TTR and factors (defined a priori). RESULTS: Four hundred and eighty (87.1%) patients had complete follow-up with an overall TTR of 74.4% based on 7,175 physician-reported INR values from 501 patients. 88% of this cohort were monitored through routine medical care (RMC). The average number of INRs per patient during the 48-week period was 14.1 (standard deviation (SD) = 8.3) tests with a mean duration of 23.8 (SD = 11.1) days between tests. We did not find a relationship between TTR and age, sex, presence of major comorbidities, patient's province of residence or rural vs. urban residence. 12% of patients monitored through anticoagulant clinics had significantly better TTR than patients monitored through RMC (82% vs. 74%; 95% confidence interval: -13.8, -1.2; p = 0.02). Health related quality of life utility values were high and remained consistent throughout the study. The majority of patients reported no impact on either work productivity or impairment of regular activities due to being on long-term warfarin treatment. CONCLUSIONS: We showed excellent overall TTR in an observed Canadian cohort, with monitoring through a dedicated anticoagulant clinic being associated with a statistically and clinically significant improvement in TTR. The burden of warfarin therapy on patients' health related quality of life or daily work and activities was low.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.307
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes2
Has abstractyes

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