Predictive Model for Post-Transplant Renal Fibrosis Using Ultrasound Shear Wave Elastography.

Abstract

BACKGROUND The aim of this study was to investigate the clinical utility of ultrasound shear wave elastography (SWE) for assessment of renal fibrosis in post-renal transplant patients. MATERIAL AND METHODS We selected 183 patients who underwent renal transplantation. The complete dataset was randomly partitioned into a training cohort (128 cases) and a validation cohort (55 cases). All patients were subjected to SWE and renal allograft biopsy. The baseline data was compared using t-test, Z-test, or chi-square test. Through univariate and multivariate analyses, we identified independent risk factors influencing renal fibrosis after transplantation, a predictive model for post-transplant renal fibrosis was developed, and calibration curves, decision curve analyses, and ROC curves were generated. RESULTS Age, TST, Scr, GFR, and Emean showed significant differences (P<0.05). The C-index of the nomogram was 0.85, and the calibration curve and Hosmer-Lemeshow test demonstrated accurate diagnosis of fibrosis in both the training and validation sets (P>0.05). DCA showed that the prediction model effectively improved the diagnostic accuracy of fibrosis. The highest AUC of the nomogram for combined prediction of renal fibrosis in transplant patients was 0.902 in the training group and 0.871 in the validation group. These values were significantly higher compared to the AUCs of individual predictors (P<0.05). CONCLUSIONS Ultrasound SWE allows for early evaluation of renal fibrosis following transplantation. The prediction model, constructed by amalgamating other indicators, augments the accuracy and reliability of the prediction, providing more precise and accurate diagnostic and therapeutic recommendations for clinical practitioners.