Non-invasive risk stratification of immunoglobulin A nephropathy and crescent formation using contrast-enhanced ultrasound combined with serological markers.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) has heterogeneous clinical and pathological manifestations. Renal biopsy is the invasive diagnostic standard, and crescents mark active glomerular injury. Quantitative contrast-enhanced ultrasound (CEUS) can characterize renal microvascular perfusion. We aimed to develop a non-invasive nomogram integrating quantitative CEUS parameters and routine serological biomarkers to diagnose IgAN and predict crescent formation. METHODS: In this retrospective single-center study, 184 patients with chronic kidney disease (CKD) underwent CEUS within 7 days before renal biopsy. Patients were classified into IgAN (n=94) and non-IgAN (n=90) groups, and the IgAN cohort was further stratified by crescent status (n=47 in each subgroup). Quantitative time-intensity curve (TIC) parameters were extracted from regions of interest (ROIs) using VueBox 7.0 software. Elliptical ROIs of approximately 4 mm × 3 mm were placed in the mid renal cortex and medulla closest to the probe to generate cortical and medullary TICs. Cortex-to-medulla normalization was applied. The analyzed parameters included peak enhancement (PE), wash-in rate (WiR), and wash-in perfusion index (WiP). Logistic regression models were constructed for IgAN diagnosis, crescent prediction in IgAN, and crescent prediction in the entire cohort. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate discrimination, calibration, and clinical net benefit. DeLong tests compared the combined model (CEUS parameters plus serological indicators), clinical-only model, and CEUS-only model to assess the incremental diagnostic value of CEUS. Subgroup analysis based on estimated glomerular filtration rate (eGFR) was performed for crescent prediction in IgAN to examine the influence of renal function on model performance. RESULTS: IgAN patients showed significantly altered renal perfusion characteristics compared with non-IgAN patients. The combined model incorporating age, eGFR, urine albumin-to-creatinine ratio (UACR), and WiR achieved moderate discrimination for IgAN diagnosis, with an area under the curve (AUC) of 0.78 [95% confidence interval (CI): 0.72-0.85]. For crescent prediction in IgAN, the combined model based on age and WiP achieved an AUC of 0.74 (95% CI: 0.64-0.85). For crescent prediction in the entire cohort, the combined model based on age and rise time (RT) showed an AUC of 0.72 (95% CI: 0.64-0.81). DeLong tests showed that the combined models outperformed the corresponding clinical-only and CEUS-only models (all P<0.05), supporting the incremental but moderate diagnostic value of CEUS. Subgroup analysis showed stronger predictive performance in patients with preserved renal function. CONCLUSIONS: CEUS-derived microvascular information, when combined with clinical biomarkers, provides incremental diagnostic information and may assist pre-biopsy risk stratification and crescent formation prediction in CKD. These findings should be interpreted as exploratory and require external validation before routine clinical implementation.