Association between cancer, CHA2DS2VASc risk, and in-hospital ischaemic stroke in patients hospitalized for atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is commonly encountered in cancer patients. We investigated the CHA2DS2VASc score, and its association with in-hospital ischaemic stroke in patients with cancer who were hospitalized for AF. METHODS AND RESULTS: Using the United States National Inpatient Sample, all hospitalizations with principal diagnosis of AF between October 2015 and December 2018 were stratified by cancer diagnosis, type, and CHA2DS2VASc risk categories (low risk, low-moderate risk, moderate-high risk). In-hospital ischaemic stroke and its association with the CHA2DS2VASc risk score was assessed across the groups using hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI). Discrimination of CHA2DS2VASc score for in-hospital ischaemic stroke was evaluated with Receiver Operating Characteristic and Area Under the Curve (AUC). Among 1 341 870 included hospitalizations, 71 965 (5.4%) had comorbid cancer. Cancer patients had a higher proportion of moderate-high CHA2DS2VASc risk compared with their non-cancer counterparts (86.5% vs. 82.3%, P < 0.001). Compared with their low CHA2DS2VASc risk counterparts, cancer patients in low-moderate and moderate-high risk scores had similar odds of developing stroke (aOR 1.28 95% CI 0.22-7.63 and aOR 1.78 95% CI 0.41-7.66, respectively). The CHA2DS2VASc risk score had poor discrimination for ischaemic stroke in the cancer group (AUC 0.538 95% CI 0.477-0.598). CONCLUSION: Cancer patients with AF have high CHA2DS2VASc risk. Discrimination of CHA2DS2VASc for ischaemic stroke is lower in cancer than non-cancer patients, and CHA2DS2VASc may not be adequate in determining ischaemic risk in cancer population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".