Urology · 2025
Quantitative Voiding Cystourethrogram Features Predict Detecting Photopenic Renal Parenchyma Defects on Radionuclide Imaging in Patients With Vesicoureteral Reflux.
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Ahmad Ihtisham, Khondker Adree, Palchik Shelly, Kwong Jethro C C, Yadav Priyank, Dos Santos Joana, Rickard Mandy, Lorenzo Armando J
Аннотация
OBJECTIVE: To evaluate the utility of quantitative features on voiding cystourethrogram (VCUG) to predict the presence of photopenic renal defects (PRDs) consistent with scarring. PRDs remain an indicator of kidney damage in patients with vesicoureteral reflux (VUR), and their presence influences management and long-term outcomes.
METHODS: We identified children with primary VUR who underwent a VCUG and a nuclear renal scan within 6 months at our institution between 2016 and 2021. Quantitative VUR features (qVUR) were extracted, including ureteral width (proximal, distal, and maximum) and ureteral tortuosity. Logistic regression models were developed to predict PRDs employing clinical features, VCUG indication, and VUR laterality (baseline model); the addition of VUR grade (grade model); the addition of qVUR (qVUR model); and the addition of both grade and qVUR (combined model).
RESULTS: In total, 130 children (163 renal units) were included, with median age at VCUG of 20 months. PRD+ and PRD- groups differed significantly in age (47 vs 7 months, P <.001), high-grade reflux (54% vs 29%, P = .001), and recurrent urinary tract infection history (57% vs 26%, P <.001). Model performance was highest for the combined model (AUROC 0.84, AUPRC 0.87), followed by qVUR (AUROC 0.80, AUPRC 0.85) and grade (AUROC 0.81, AUPRC 0.82). This analysis was limited by the absence of clinical endpoints incorporated into model development, including the development of chronic kidney disease and hypertension.
CONCLUSION: Quantitative VCUG features show promise in refining predictive models to identify children with VUR who are at risk for PRDs suggestive of renal scarring, outperforming traditional metrics.
METHODS: We identified children with primary VUR who underwent a VCUG and a nuclear renal scan within 6 months at our institution between 2016 and 2021. Quantitative VUR features (qVUR) were extracted, including ureteral width (proximal, distal, and maximum) and ureteral tortuosity. Logistic regression models were developed to predict PRDs employing clinical features, VCUG indication, and VUR laterality (baseline model); the addition of VUR grade (grade model); the addition of qVUR (qVUR model); and the addition of both grade and qVUR (combined model).
RESULTS: In total, 130 children (163 renal units) were included, with median age at VCUG of 20 months. PRD+ and PRD- groups differed significantly in age (47 vs 7 months, P <.001), high-grade reflux (54% vs 29%, P = .001), and recurrent urinary tract infection history (57% vs 26%, P <.001). Model performance was highest for the combined model (AUROC 0.84, AUPRC 0.87), followed by qVUR (AUROC 0.80, AUPRC 0.85) and grade (AUROC 0.81, AUPRC 0.82). This analysis was limited by the absence of clinical endpoints incorporated into model development, including the development of chronic kidney disease and hypertension.
CONCLUSION: Quantitative VCUG features show promise in refining predictive models to identify children with VUR who are at risk for PRDs suggestive of renal scarring, outperforming traditional metrics.