To evaluate the clinical utility of superb microvascular imaging (SMI) for noninvasive diagnosis of renal interstitial fibrosis (IF) and compare its performance with conventional hemodynamic indicators, we prospectively enrolled 106 patients, categorized into minimal/mild IF (n = 71) and moderate/severe IF (n = 35). Collected measures included SMI-derived vascular density, capsule-to-terminal vessel distance (SMI distance), estimated glomerular filtration rate (eGFR), and conventional Doppler indices. Univariate and multivariable logistic regression identified independent predictors, which were integrated into a multivariable prediction model. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) assessed net benefit across clinical thresholds. A nomogram was constructed for individualized prediction. SMI vascular density (OR = 0.955; p = 0.013), SMI distance (OR = 3.161; p = 0.036), and eGFR (OR = 0.976; p = 0.040) were independent predictors. Their areas under the curve (AUCs) were 0.779, 0.742, and 0.733, with sensitivities of 82.9, 77.1, and 77.1% and specificities of 64.8, 67.6, and 57.7%, respectively. The multivariable prediction model achieved an AUC of 0.837, with 68.6% sensitivity, 85.9% specificity, and 80.2% accuracy. DCA confirmed superior net benefit of the multivariable prediction model across most thresholds, consistently outperforming single parameters. SMI provides high sensitivity for early IF detection and, combined with eGFR, enhances diagnostic accuracy. This multivariable prediction model demonstrates clinical utility and may serve as a practical noninvasive tool for fibrosis assessment in chronic kidney disease.
Ma et al. (Wed,) studied this question.
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