Abstract 4364857: Retrospective Analysis of the Accuracy and Clinical Utility of Predictive Artificial Intelligence in Cardiovascular Event Risk Assessment : PACE Study

Introduction: Predictive analytics powered by artificial intelligence (AI) and machine learning (ML) are revolutionizing cardiovascular risk assessment. Accurate prediction of low-density lipoprotein cholesterol (LDL-C) is critical for evaluating cardiovascular disease (CVD) risk and guiding therapeutic decisions. This study evaluates deep learning (DL) models for LDL-C prediction in patients with prior cardiovascular events, comparing their performance against traditional ML methods and established LDL-C estimation formulas. Methods: We retrospectively analyzed data from 8,315 patients with documented cardiovascular events from Rhythm Heart and Critical Care. Key lipid parameters included LDL-C, triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C). Patient CVD history was blinded during model training to ensure unbiased prediction. DL models tested included Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and a Transformer-based architecture. These were benchmarked against Back Propagation Neural Network (BPNN) models and LDL-C formulas by Sampson and Martin. Model performance was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results: The models generated LDL-C predictions for 5,132 patients (61% of the cohort). The Transformer-based model achieved the highest accuracy with an RMSE of 10.58 mg/dL and MAPE of 7.35%, significantly outperforming BPNN (RMSE 17.16 mg/dL; MAPE 11.01%), RNN (RMSE 32.47 mg/dL), and LSTM (RMSE 32.51 mg/dL). Deep learning models also surpassed traditional LDL-C formulas in accuracy. Partial Dependence Plots (PDP) of the Transformer model revealed clinically meaningful relationships between LDL-C and predictors such as HDL-C, BMI, and thyroid hormones, supporting physiological validity and interpretability. Conclusion: This study demonstrates that DL models, particularly the Transformer-based approach, significantly outperform conventional methods in predicting LDL-C levels among patients with cardiovascular events. The model’s superior accuracy and interpretability offer a promising clinical tool for personalized risk assessment, early detection, and optimized management of CVD. Incorporation of such AI-driven models into clinical workflows could improve patient outcomes and resource allocation in cardiovascular care.