The application of shear wave elastography in monitoring renal allografts

Background: Follow-up for renal transplant recipients depends on laboratory tests for the monitoring of renal function. Ultrasound shear wave elastography (SWE), as a noninvasive technique, can measure renal allograft stiffness, offering a potential direct indicator of the allograft’s functional status. This study aimed to establish a model for monitoring and assessing renal allograft function by combining SWE imaging of renal allografts with laboratory indicators to assist in monitoring renal function.

Methods: Ultrasound SWE was performed in renal transplant recipients who met the inclusion and exclusion criteria during routine follow-up ultrasound examinations at West China Hospital of Sichuan University between December 2021 and August 2022. The stiffness values were recorded as shear wave velocity (SWV). Stiffness was measured three times in each patient, and the median of the three measurements was used for subsequent analysis. The data collected comprised relevant laboratory test indicators—including serum creatinine (Scr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), serum uric acid (UA), serum cystatin C (Cys C), cholesterol, and albumin—and Doppler parameters—including the systolic peak velocity, end-diastolic flow velocity, and resistance index from the main, segmental, interlobar, and arcuate arteries of the renal allograft. Correlation analysis was conducted between SWV values and the collected parameters. Univariate and multivariate analyses were performed to identify factors associated with impaired allograft function, defined as an eGFR <60 mL/min/1.73 m². A binary logistic regression model was subsequently constructed. Based on the model, nomograms, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis curves were generated. All statistical analyses were performed with SPSS 25.0 software and the rms package in R, with a two-sided P value <0.05 considered statistically significant.

Results: A total of 462 renal transplant recipients were included (256 males and 206 females), with a median age of 34 years (interquartile range, 29–42 years). Spearman rank correlation analysis revealed that SWV significantly correlated with several parameters: renal transplantation time (ρ=–0.128; P=0.007), Scr (ρ=0.209; P=0.000), eGFR (ρ=–0.234; P=0.000), BUN (ρ=0.128; P=0.006), UA (ρ=0.146; P=0.002), and Cys C (ρ=0.213; P=0.000). Univariate analysis revealed that SWV, age, renal transplantation time, BUN, Scr, Cys C, UA, serum albumin, and the resistive indices of the segmental, interlobar, and arcuate arteries differed significantly (P<0.05). The binary logistic regression model, which incorporated the factors of SWV, age, Scr, and Cys C identified in the multivariate analyses, demonstrated excellent discriminative performance, with an area under the ROC curve of 0.962 in the training set and 0.929 in the validation set.

Conclusions: Ultrasound SWE served as a reliable, noninvasive adjunct for assessing renal allograft function. Furthermore, the binary logistic regression model integrating SWV with clinical parameters (age, Scr, and Cys C) demonstrated high predictive accuracy, offering a promising composite tool for stratifying graft dysfunction risk.