Incidence and pattern of anticoagulation in patients with atrial fibrillation and atrial flutter at tertiary hospital in Oman
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) and atrial flutter (AFL) are the two most common cardiac arrhythmias encountered in clinical practice. Anticoagulants are the cornerstone in cerebrovascular accident prevention in these patients. Despite their role in reducing stroke risk, emergency room visits, and overall treatment costs, studies have shown that oral anticoagulants (OAC) are underutilized, even in high-risk patients. Objective: The aim of this study is to explore the appropriateness and pattern of anticoagulant prescriptions in patients with AF and/or AFL in a tertiary hospital as recommended by the international guidelines. Method: This retrospective observational study was conducted by reviewing the electronic medical records of 389 adult patients with either AF or AFL in Sultan Qaboos University Hospital (SQUH) between July 2018 and July 2020. The appropriateness of oral anticoagulation was assessed according to international guidelines, namely, 2020 Canadian Cardiology Society (CCS), 2020 European Society of Cardiology (ESC), and 2019 American Heart Association/American College of Cardiology/Heart Rhythm Society (AHA/ACC/HRS). Analysis was performed using univariate statistics (Chi-square and percentages) to report categorical data. Results: Oral anticoagulants were prescribed appropriately in 91% of the patients according to CCS guidelines, and 92% and 96% according to ESC and (AHA/ACC/HRS) class Ia and IIa recommendations, respectively. Among non-anticoagulated patients (28 patients, 9%) had proper justification. On the other hand, 38 patients (9.8%) out of the whole cohort (389) had a CHADS-65 score of zero but received OAC mostly due to cardioversion, rheumatic heart disease, or mechanical heart valve prosthesis. Novel oral anticoagulants (NOACs) were the most commonly used OAC 251/336 (75%), with rivaroxaban being the main drug used 234/250 (92%) of all NOAC prescriptions. 75% of patients with eGFR<50 ml/min received the proper dose of rivaroxaban. Bleeding was the most frequently reported side effect of OAC in 52 (13.4%) of the entire cohort. Conclusion: Nearly 91% of patients with AF/AFL followed at SQUH were prescribed an appropriate anticoagulant as per international guidelines. The decisions to anticoagulate a patient with AF/AFL and the type of anticoagulant used were appropriate
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".