Outcomes and Discriminatory Accuracy of the CHA2DS2VASc Score in Atrial Fibrillation and Cancer
Bibliographic record
Abstract
Atrial fibrillation (AF) is highly prevalent among cancer patients. The role of traditional risk stratification scores in the context of different cancer types in these patients remains unknown. The purpose of this study was to determine the discriminative accuracy of the CHA2DS2VASc score for ischemic stroke using receiver operating characteristic and area under the curve. The National Readmission Database (2015-2019) was used to identify all AF patients stratified by the cancer diagnosis, type, and CHA2DS2VASc category (low; moderate; high risk). Outcomes at 30-day readmission were compared between cancer and noncancer groups using hierarchical multivariable logistic regression to calculate adjusted odds ratios (aORs) and 95% CIs. A total of 6,996,088 AF patients were identified at index admission. Of these, 4,242,630 (642,237 cancer, 3,600,393 noncancer) were readmitted at 30 days. Cancer patients (92.1%) had a higher proportion of high CHA2DS2VASc scores compared with their noncancer counterparts (89.8%, P < 0.001). The 30-day readmission rate and incidence of major bleeding in cancer patients were significantly higher compared with their corresponding noncancer group across all CHA2DS2VASc categories. Among the different cancer types, hematological and lung cancer had a high propensity for major bleeding. The odds of ischemic stroke were lower in the cancer group across high (1.9% vs 2.4%; aOR: 0.78; 95% CI: 0.76-0.79; P < 0.0001), moderate (0.8% vs 1.3%; aOR: 0.57; 95% CI: 0.50-0.64; P < 0.0001), and low (0.4% vs 0.9%; aOR: 0.46; 95% CI: 0.34-0.62; P < 0.0001) risk category relative to the noncancer group irrespective of type of cancer. CHA2DS2VASc category had a statistically significant discriminatory accuracy for ischemic stroke in both cancer and noncancer patients. Cancer patients with AF are at a higher risk of readmission and major bleeding. The risk of ischemic stroke during readmission appears to be lower than noncancer patients. These findings may have implications for anticoagulant therapy in cancer patients.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".