Abstract 4341860: Machine Learning Model Outperforms Conventional Score to Distinguish Transthyretin Amyloid Cardiomyopathy from Other Cardiac Conditions that Cause Left Ventricular Hypertrophy

Abstract

Introduction: Early and accurate diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) is known to improve prognosis. However, it is sometimes challenging to distinguish ATTR-CM from other cardiac conditions that cause left ventricular hypertrophy (LVH), such as hypertrophic cardiomyopathy (HCM), hypertensive LVH (h-LVH), and aortic stenosis (AS). The Mayo ATTR-CM score is commonly used to distinguish ATTR-CM from these mimickers. Herein, we constructed a machine learning (ML) model and compared its performance to the Mayo score. Hypothesis: A ML model based on readily available clinical variables will outperform the Mayo score in distinguishing ATTR-CM from other cardiac conditions that cause LVH. Methods: We performed a multicenter case-control study of 1,605 patients, including 303 ATTR-CM cases and 1,302 controls – i.e., 935 with HCM, 331 with h-LVH, and 36 with AS. We allocated the former two-thirds of each disease group to the training set and the latter one-third to the prospective test set for validation. We developed a main ML model in the training set using XGBoost with 18 clinical variables including demographics, comorbidities, laboratory values, and echocardiographic data. We examined its performance in the test set using the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). In addition, we developed a simplified ML model using only the top 5 variables identified by feature importance and tested its performance. Results: The top 5 most important variables were age, LV diameter in systole, LV posterior wall thickness in diastole, LV ejection fraction, and left atrial diameter ( Figure 1 ). The main ML model (AUC: 0.96, 95% CI: 0.93–0.98) outperformed the Mayo score (AUC: 0.84, 95% CI: 0.79–0.89, DeLong’s P <0.0001; Figure 2 ) in the test set. DCA demonstrated that the ML model provided greater net benefit across all threshold probabilities ( Figure 3 ). The simplified 5-variable model also outperformed the Mayo score (AUC: 0.94, 95% CI: 0.91–0.97, DeLong’s P <0.0001). Conclusions: In this multicenter study, we developed ML models that effectively distinguish ATTR-CM from its mimickers that cause LVH using readily available clinical variables. Given the simplicity and accuracy of our models, the present study serves as an important initial step toward the development of a robust, data-driven tool that could be readily automated and integrated into electronic medical record system to screen for ATTR-CM.