Abstract 15425: Is Accurate Stroke Risk ssessment with CHADS <sub>2</sub> Necessary in Managing Patients with Atrial Fibrillation? Insights from the Stroke Prevention and Rhythm Interventions in Atrial Fibrillation (SPRINT-AF) Registry
Bibliographic record
Abstract
Introduction: Guidelines have advocated use of scoring schemes such as CHADS 2 to assess stroke risk for patients with atrial fibrillation (AF). However, recent studies had demonstrated poor agreement between physician-reported and scoring-derived stroke risk. From a national registry, we assessed if rates of oral anticoagulant (OAC) use differ between patients whose stroke risks are concordantly or discordantly categorized. Methods: From December 2012 to July 2013, a cross-sectional analysis of 936 consecutive AF patients was performed, enrolled from 109 primary care and specialty practices in 10 Canadian provinces. Based on clinical judgment, physicians categorized each patient as low, moderate, or high risk for stroke. We categorized patients’ stroke risk based on their CHADS 2 score (low: 0; moderate: 1, high: ≥2). Agreement between physician-reported and CHADS 2 risk was reported by the weighted kappa. We compared rates of OAC use between patients whose stroke risk was concordantly or discordantly categorized by clinicians, relative to those derived from CHADS 2 . Results: Complete data were available in 929 (98.8%) patients for analysis. The weighted kappa between physician-reported and CHADS 2 -derived stroke risk was 0.41 (95% CI: 0.34 to 0.48). Physician-determined stroke risk was concordantly categorized to CHADS 2 scores in 544 (58.6%) patients. Among patients with CHADS 2 ≥2, rates of OAC use were similar between the concordant and discordant groups (91.7% vs. 90.4%, p=0.66). For patients with CHADS 2 =1, rates of OAC use were higher in the concordant group (84.2% vs. 66.4%, p<0.01). For patients with CHADS 2 =0, rates of OAC use were lower in the concordant group (43.5% vs. 80.0%, p<0.01). Conclusions: In this contemporary AF registry, the agreement between physician-reported and CHADS 2 -derived stroke risk was only modest. Despite this, the rate of OAC use in patients at high risk (CHADS 2 ≥2) was similarly high between the concordant and discordant groups. However, rates of OAC use between the 2 groups differed among patients at lower stroke risk. Our results suggest that discrepancy in stroke risk categorization is associated with guideline-discordant OAC use, particularly for patients with lower CHADS 2 scores.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".