A novel comprehensive predictive model for obstructive pyonephrosis patients with upper urinary tract stones

Area of Science:

• Urology
• Nephrology
• Medical Imaging

Background:

• Calculous pyonephrosis is often diagnosed late, typically during procedures like percutaneous nephrolithotripsy or nephrostomy.
• Accurate pre-operative diagnosis of obstructive pyonephrosis in patients with upper urinary tract stones remains a clinical challenge.

Purpose of the Study:

• To identify risk factors for predicting obstructive pyonephrosis in patients presenting with upper urinary tract stones.
• To develop and validate a predictive model for early diagnosis of pyonephrosis.

Main Methods:

• Retrospective analysis of clinical data from 322 patients with upper urinary tract stones and obstructive hydronephrosis.
• Patients were categorized into pyonephrosis and non-pyonephrosis groups for comparative analysis.
• Univariate, multivariate logistic regression, and ROC curve analyses were employed to identify independent risk factors and construct a predictive model.

Main Results:

• Key differentiating factors included sex, recent urinary tract infection (UTI), stone density, CT value of hydronephrosis, serum creatinine, and degree of hydronephrosis.
• Multivariate analysis identified hydronephrosis degree, CT value, urine leukocytes, urine culture, and blood neutrophils as independent predictors of pyonephrosis.
• The developed comprehensive model demonstrated high predictive accuracy (AUC, 0.970).

Conclusions:

• Hydronephrosis severity, its CT value, blood neutrophil count, urine leukocyte presence, and urine culture results are significant independent risk factors for pyonephrosis.
• The novel comprehensive model effectively predicts pyonephrosis and warrants further validation in prospective studies.