Metabolic Syndrome Predicts Uric Acid Stones in the Upper Urinary Tract: Development and Validation of a Nomogram Model

Xinyu Shen; Qianqing Pan; Yuhua Huang; Jianan You; Yunyi Chen; Xiang Ding

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Metabolic Syndrome Predicts Uric Acid Stones in the Upper Urinary Tract: Development and Validation of a Nomogram Model

Xinyu Shen ^[1] ; Qianqing Pan ^[1] ; Yuhua Huang ^[1] ; Jianan You ^[1] ; Yunyi Chen ^[1] ; Xiang Ding ^[1]
1. [1] First Affiliated Hospital of Soochow University
  
  First Affiliated Hospital of Soochow University
  
  China
Localización: Archivos españoles de urología, ISSN 0004-0614, Tomo 76, Nº. 4, 2023, págs. 255-263
Idioma: inglés
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Resumen
- Background: Accurately identifying uric acid stones is pivotal in determining the appropriate treatment strategy for patients. This study aimed to design an innovative nomogram to predict the occurrence of uric acid stones in the upper urinary tract.
  
  Methods: This retrospective study examined 680 patients with urinary stones from October 2019 to September 2022. Risk factors were identified through univariate and multivariate logistic regression, leading to the development of a nomogram. This model’s validity was then assessed internally using receiver operating characteristic (ROC) curves, the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
  
  Results: Our findings revealed that metabolic syndrome (odds ratio (OR) = 4.347, 95% confidence interval (CI) 1.306–14.466, p = 0.017), serum urea levels (OR = 1.004, 95% CI 1.143–2.002, p = 0.004), urinary pH (OR = 0.185, 95% CI 0.059–0.583, p = 0.004), urinary potassium (OR = 0.926, 95% CI 0.875–0.981, p = 0.009), and urinary calcium (OR = 0.693, 95% CI 0.492–0.977, p = 0.037) are independent factors for upper urinary tract uric acid stones. Utilizing the five variables, we developed a predictive nomogram. The AUC of the training cohort and the validation cohort were 0.917 (95% CI 0.871–0.963) and 0.914 (95% CI 0.850–0.978), respectively. Calibration curves indicated strong consistency in both cohorts, and the DCA revealed the model’s clinical utility.
  
  Conclusions: We devised a reliable and user-friendly nomogram to predict uric acid stones in the upper urinary tract. It is based on metabolic syndrome, serum biochemical markers, and 24-hour urinary parameters. Key determinants include metabolic syndrome, serum urea, urinary pH, urinary potassium and urinary calcium.