Abstract 4147920: Optimal Approach to Generate Patient-Level Decision from Study-Level Predictions Using a Multimodal Deep Learning Model for Detecting Transthyretin Cardiac Amyloidosis

Background: Transthyretin cardiac amyloidosis (ATTR-CA) is an underdiagnosed cause of heart failure and early disease detection is essential for improving outcomes. We previously developed a multimodal deep learning model, named ATTRact-Net, leveraging electrocardiogram (ECG) and echocardiogram data that can identify patients with ATTR-CA. Patients at risk for ATTR-CA often have multiple ECGs and echos available for analysis, leading to many different risk predictions by AI models. There is little evidence on how to go from study-level prediction to patient-level decision. We studied varying methods to integrate multiple ECG/echo pair predictions to maximize model performance in the detection of ATTR-CA. We hypothesized that retaining the patient-level mean risk across the prior 2 years would improve model performance compared to using maximum predicted risk. Methods and Results: ATTRact-Net was originally trained on 799 patients with 22,344 ECG/echo pairs completed within 2 years of PYP scan. Previously published performance of the model showed an AUROC of 0.83 in internal testing. For this study, a new test set composed of an outside hospital not used for training, had 422 patients with 12,387 pairs and ATTR-CA prevalence of 23%. In this new hospital’s population, we tested model performance using four patient-level aggregation methods (mean, median, max, and no aggregation) for integrating predictions across multiple ECG/echo pairs. Performance was compared based on the area under the receiver operating characteristic curve (AUROC), Diagnostic Odds Ratio (DOR), and F1 score using the Youden index as the optimal threshold. In the external validation dataset, the model achieved AUROCs of 0.78 (max), 0.82 (mean), 0.82 (median) and 0.82 (none) shown in Figure 1A. Mean and median aggregation outperformed both max and no aggregation by F1-score and DOR (Figure 1B). Conclusion: For a multimodal AI model detecting ATTR-CA, aggregating ECG/echo risk predictions using mean or median from the prior 2 years considerably improved diagnostic yield compared to using patient’s maximum score or no aggregation. A prospective clinical trial is underway using this strategy for early diagnosis of ATTR-CA across our healthcare system.