Integrating clinical, laboratory and quantitative CT features for predicting split renal function in urinary tract obstruction.

BACKGROUND: Urinary tract obstruction (UTO) can lead to progressive renal impairment, making accurate evaluation of split renal function (SRF) essential for clinical decision-making. Although radionuclide renal dynamic scintigraphy remains the gold standard for SRF assessment, its clinical application is constrained by procedural complexity, limited availability, and sensitivity to anatomical variations. Thus, there is a clinical need for simpler, reliable, and noninvasive alternative approaches. This study aimed to develop and validate a predictive model for SRF grading by integrating clinical variables, laboratory parameters, and quantitative contrast-enhanced computed tomography (CECT) features in patients with UTO. METHODS: A retrospective cohort of 78 patients with UTO (150 kidneys) was analyzed. Based on split renal glomerular filtration rate (GFR) determined using the Gates method, kidneys were categorized into normal, mild-to-moderate impairment, and severe impairment groups. Clinical variables, laboratory parameters, and quantitative CECT features were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and construct SRF grading models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: In Task 1 (normal vs. abnormal) and Task 2 (normal vs. mild-to-moderate), age was the key univariate predictor, while hemoglobin (Hb) and renal cortical thickness (Rc) were identified as independent predictors in the laboratory-based and CECT-based multivariate models, respectively. For Task 1, the Combined model [Clinical predictor (Age) + Laboratory model (Hb-based) + CECT model (Rc-based)] achieved the highest diagnostic performance (AUC = 0.890); for Task 2, the optimal model was [Clinical predictor (Age) + CECT model (Rc-based)] (AUC = 0.810). For Task 3 (mild-to-moderate vs. severe), Hb was the strongest univariate predictor, and Rc was the sole independent predictor in the CECT-based multivariate model. The highest diagnostic accuracy for Task 3 was achieved by the combined Laboratory predictor (Hb) + CECT model (Rc-based), with an AUC of 0.970. CONCLUSION: The CECT model (Rc-based) serves as a crucial imaging biomarker for evaluating SRF impairment in patients with UTO. Task-specific models combining the CECT model (Rc-based) with a clinical predictor (Age) and laboratory information—either as a univariable predictor (Hb) or as a multivariable laboratory model (Hb-based)—showed superior predictive performance. This integrated, noninvasive strategy may serve as a useful adjunct to radionuclide imaging for individualized SRF assessment, pending prospective validation.