Abstract
Chronic Kidney Disease (CKD) patients are at higher risk of Major Adverse Cardiovascular Events (MACE). Echocardiography evaluates left ventricle (LV) function and heart abnormalities. LV Wall (LVW) pathophysiology and systolic/diastolic dysfunction are linked to MACE outcomes (\(O^-\) and \(O^+\)) in CKD patients. However, traditional LV volume-based measurements like ejection-fraction offer limited predictive value as they rely only on end-phase frames. We hypothesize that analyzing LVW morphology over time, through spatiotemporal analysis, can predict MACE risk in CKD patients. However, accurately delineating and analyzing LVW at every frame is challenging due to noise, poor resolution, and the need for manual intervention. Our contribution includes (a) developing an automated pipeline for identifying and standardizing heart-beat cycles and segmenting the LVW, (b) introducing a novel computational biomarker—STAR-Echo—which combines spatiotemporal risk from radiomic (\(M_R\)) and deep learning (\(M_T\)) models to predict MACE prognosis in CKD patients, and (c) demonstrating the superior prognostic performance of STAR-Echo compared to \(M_R\), \(M_T\), as well as clinical-biomarkers (EF, BNP, and NT-proBNP) for characterizing cardiac dysfunction. STAR-Echo captured the gray level intensity distribution, perimeter and sphericity of the LVW that changes differently over time in individuals who encounter MACE outcomes. STAR-Echo achieved an AUC of \(0.71 [0.53-0.89]\) for MACE outcome classification and also demonstrated prognostic ability in Kaplan-Meier survival analysis on a holdout cohort (\(S_v=44\)) of CKD patients (\(N=150\)). It achieved superior MACE prognostication (p-value = 0.037 (log-rank test)), compared to \(M_R\) (p-value = 0.042), \(M_T\) (p-value = 0.069), clinical biomarkers—EF, BNP, and NT-proBNP (p-value >0.05).
R. Dhamdhere and G. Modanwal—These authors contributed equally to this work.
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Acknowledgement
Research reported in this publication was supported by the National Cancer Institute under award numbers R01CA268287A1, U01 CA269181, R01CA26820701A1, R01CA249992-01A1, R0CA202752-01A1, R01 CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astra-zeneca. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government.
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Dhamdhere, R. et al. (2023). STAR-Echo: A Novel Biomarker for Prognosis of MACE in Chronic Kidney Disease Patients Using Spatiotemporal Analysis and Transformer-Based Radiomics Models. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_28
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