Assessment of Renal Fibrosis in Patients With Chronic Kidney Disease Using Shear Wave Elastography and Clinical Features: A Random Forest Approach

Abstract

Objective: Renal fibrosis is the common pathological hallmark of chronic kidney disease (CKD) progression. In this study, a random forest (RF) classifier based on 2-D shear wave elastography (SWE) and clinical features for the differential severity of renal fibrosis in patients with CKD is proposed.

Methods: A total of 162 patients diagnosed with CKD who underwent 2-D SWE and renal biopsy were prospectively enrolled from April 2019 to December 2021 and then randomized into training (n = 114) and validation (n = 48) cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machines (SVM-RFE) algorithm were employed to select renal fibrosis-related features from clinical information and elastosonographic findings. An RF model was subsequently constructed using the aforementioned informative parameters in the training cohort and evaluated in terms of discrimination, calibration and clinical utility in both cohorts.

Results: The LASSO and SVM-RFE analyses revealed that age, sex, blood urea nitrogen, renal resistive index, hypertension and the 2D-SWE value were independent risk variables associated with renal fibrosis severity. The established RF model incorporating these six variables exhibited fine discrimination in both the derivation (area under the curve [AUC]: 0.84, 95% confidence interval [CI]: 0.76-0.91) and validation (AUC: 0.88, 95% CI: 0.77-0.98) cohorts. Moreover, the calibration curve revealed satisfactory predictive accuracy, and the decision curve analysis revealed a significant clinical net benefit.

Conclusion: The developed RF model, via a combination of the 2-D SWE value and clinical information, indicated satisfactory diagnostic performance and clinical practicality toward differentiating moderate-severe from mild renal fibrosis, which may provide critical insight into risk stratification for patients with CKD.

Keywords: Chronic kidney disease; Machine learning; Renal fibrosis; Shear wave elastography.