Discriminative Accuracy of CHA₂DS₂VASc Score, and Development of Predictive Accuracy Model Using Machine Learning for Ischemic Stroke in Cardiac Amyloidosis

ABSTRACT Background Cardiac amyloidosis (CA) in conjunction with atrial fibrillation (AF) presents unique management challenges. CHA2DS2VASc score in these patients is believed to underestimate the risk of ischemic stroke, necessitating a better predictive model in these patients. Methods Data was 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 (aOR). AutoScore; an interpretable machine learning framework, was used to develop a stroke risk prediction model, the predictive accuracy of which 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 2015-2019. 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 aOR 1.85), and intracranial hemorrhage (aOR 14.4 and aOR 4.26) were significantly higher in CA-AF compared with non-CA-AF at both index admission and 30-days, respectively. The CHA 2 DS 2 VASc score had a poor discriminative accuracy for stroke at 30-days in CA-AF (AUC 49%, 95%CI 47%-51%, p=0.54). The machine learning autoscore integrative model revealed that the predictive ability of our newly proposed E-CHADS score (end-stage renal disease (ESRD), congestive heart failure, hypertension, active cancer, dementia, and diabetes mellitus) for 30-day risk of ischemic stroke in CA-AF was excellent (for a cutoff of 52 points random forest score) with an AUC of 80% (95%CI 74%-86%) Conclusion Cardiac amyloidosis carries a high risk of ischemic stroke that is not accurately predicted by the CHA 2 DS 2 VASc score. Our proposed model (E-CHADS) identifies 3 new variables (ESRD, dementia, and cancer) that have higher discriminative accuracy for ischemic stroke in these patients.