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Background <p>Double J (DJ) stent encrustation is a common postoperative complication that can lead to severe infection, obstruction, or stent retention. Existing predictive models primarily focus on indwelling time and urinary pH but have largely excluded patients with chronic kidney disease (CKD), limiting their applicability. This multicenter study aimed to develop and externally validate a nomogram for individualized prediction of DJ stent encrustation, incorporating renal function status for the first time.</p> Methods <p>A total of 760 patients who underwent upper urinary tract stone surgery with postoperative DJ stent placement were retrospectively analyzed. Clinical, biochemical, and behavioral variables were evaluated. Multivariate logistic regression identified independent predictors, which were used to construct a predictive nomogram. External validation was performed using an independent cohort of 337 patients from another tertiary hospital. Model discrimination, calibration, and clinical benefit were assessed by receiver operating characteristic (ROC) curve analysis, bootstrap calibration, and decision curve analysis (DCA).</p> Results <p>Stent encrustation occurred in 121 patients (15.9%). Four variables—stent indwelling time, urine pH, daily water intake, and renal function stage—were independently associated with encrustation (<i>p</i> &lt; 0.05 for all). The nomogram achieved excellent discrimination (AUC = 0.877) and maintained strong external performance (AUC = 0.884). CKD significantly increased risk in a dose-dependent manner, and interaction analysis revealed a synergistic effect between CKD and urine pH (<i>p</i> = 0.002), explaining the lack of independent significance of pH in CKD subgroups.</p> Conclusion <p>This study established and externally validated the first nomogram for predicting DJ stent encrustation that includes CKD as a systemic variable. The model demonstrates high accuracy and generalizability, offering a practical tool for early identification of high-risk patients, particularly those with renal impairment, to guide individualized stent management and prevent irreversible renal damage.</p>

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Risk analysis and nomogram-based prediction for Double-J stent encrustation: accounting for chronic kidney disease

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Mingbin Xu,
  • Daoyuan Li,
  • Shuming He,
  • Chengyang Li

摘要

Background

Double J (DJ) stent encrustation is a common postoperative complication that can lead to severe infection, obstruction, or stent retention. Existing predictive models primarily focus on indwelling time and urinary pH but have largely excluded patients with chronic kidney disease (CKD), limiting their applicability. This multicenter study aimed to develop and externally validate a nomogram for individualized prediction of DJ stent encrustation, incorporating renal function status for the first time.

Methods

A total of 760 patients who underwent upper urinary tract stone surgery with postoperative DJ stent placement were retrospectively analyzed. Clinical, biochemical, and behavioral variables were evaluated. Multivariate logistic regression identified independent predictors, which were used to construct a predictive nomogram. External validation was performed using an independent cohort of 337 patients from another tertiary hospital. Model discrimination, calibration, and clinical benefit were assessed by receiver operating characteristic (ROC) curve analysis, bootstrap calibration, and decision curve analysis (DCA).

Results

Stent encrustation occurred in 121 patients (15.9%). Four variables—stent indwelling time, urine pH, daily water intake, and renal function stage—were independently associated with encrustation (p < 0.05 for all). The nomogram achieved excellent discrimination (AUC = 0.877) and maintained strong external performance (AUC = 0.884). CKD significantly increased risk in a dose-dependent manner, and interaction analysis revealed a synergistic effect between CKD and urine pH (p = 0.002), explaining the lack of independent significance of pH in CKD subgroups.

Conclusion

This study established and externally validated the first nomogram for predicting DJ stent encrustation that includes CKD as a systemic variable. The model demonstrates high accuracy and generalizability, offering a practical tool for early identification of high-risk patients, particularly those with renal impairment, to guide individualized stent management and prevent irreversible renal damage.