Abstract Body: Introduction:
Heart failure with preserved ejection fraction (HFpEF) presents a unique challenge in cardiology due to its diverse etiology and clinical presentation. Our study introduces a cutting-edge integrated deep learning model that combines multi-dimensional datasets, encompassing genetic variants from GWAS, cardiac structural and functional assessments from MRI, and electrophysiological data from ECGs, along with comorbidity profiles including CKD and LFT.
Research Questions:
Can a deep learning model that integrates multi-dimensional datasets accurately predict HFpEF risk?
How does the inclusion of different types of data (genetic, MRI, comorbidity) affect the model's predictive accuracy?
Goals:
To develop a deep learning model that accurately predicts HFpEF risk
To assess the impact of different types of data on the model's predictive accuracy.
Methods:
The model employs a graph convolutional multi-model deep learning approach to dissect the intricate interplay between genetic variants from GWAS, cardiac structural and functional assessments from MRI, electrophysiological data from ECGs, and comorbidity profiles including CKD and LFT.
Results:
The baseline genetic model achieved a PRS of 2.04 and an outcome accuracy of 81%. The incorporation of MRI features improved the outcome accuracy to 85.91%. The inclusion of comorbidities such as CKD and T2D resulted in the highest outcome accuracy of 88.57%.
Discussion: The results suggest that the inclusion of MRI features and comorbidities improves the model's predictive accuracy. This model holds significant promise for clinical adoption and the advancement of personalized treatment strategies.