Discriminative Accuracy of CHA2DS2-VASc Score, and Development of Predictive Accuracy Model Using Machine Learning for Ischemic Stroke Risk in Cardiac Amyloidosis and Atrial Fibrillation
Bibliographic record
Abstract
Background: CHA2DS2-VASc score in cardiac amyloidosis (CA) with atrial fibrillation (AF) is believed to underestimate ischemic stroke risk, necessitating a better predictive model. Methods: Data were obtained from the National Readmission Database (NRD). Outcomes between CA-AF and no-CA-AF were compared using multivariate regression analysis to calculate adjusted odds ratios (aORs). AutoScore, an interpretable machine learning framework, was used to develop a stroke risk prediction model, and its predictive accuracy was evaluated with an area under the curve (AUC) using the receiver operating characteristic analysis. Results: A total of 11,860,804 (CA-AF 22,687 (0.19%) and no-CA-AF 11,838,117) patients were identified from 2015 to 2019. The adjusted odds of mortality (aOR: 1.41 and 1.29), stroke (aOR: 1.78 and 1.74), non-intracranial hemorrhage (aOR: 2.10 and 1.85), and intracranial hemorrhage (aOR: 14.4 and 4.26) were significantly higher in CA-AF compared with non-CA-AF at both index admission and 30 days, respectively. The CHA2DS2-VASc score had a poor discriminative accuracy for stroke at 30 days in CA-AF (AUC 49%, 95% confidence interval (CI): 47 - 51, P = 0.54). The machine learning autoscore integrative model revealed excellent predictive ability of our newly proposed E-CHADS score (end-stage renal disease (ESRD), congestive heart failure (CHF), hypertension (HTN), cancer, dementia, and diabetes mellitus (DM)) for 30-day risk of ischemic stroke in CA-AF (cutoff of 52 points random forest score) with an AUC of 80% (95% CI: 74 - 86). Conclusions: CA with AF carries a high risk of ischemic stroke that is not accurately predicted by the CHA2DS2-VASc score. Our proposed model (E-CHADS) identifies three new variables (ESRD, dementia, and cancer) that have higher discriminative accuracy for ischemic stroke in these patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".