STAR-Echo: A Novel Biomarker for Prognosis of MACE in Chronic Kidney Disease Patients Using Spatiotemporal Analysis and Transformer-Based Radiomics Models.

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).