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A nomogram clinical prediction model for predicting urinary infection stones: development and validation in a retrospective study

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World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to develop a nomogram prediction model to predict the exact probability of urinary infection stones before surgery in order to better deal with the clinical problems caused by infection stones and take effective treatment measures.

Methods

We retrospectively collected the clinical data of 390 patients who were diagnosed with urinary calculi by imaging examination and underwent postoperative stone analysis between August 2018 and August 2023. The patients were randomly divided into training group (n = 312) and validation group (n = 78) using the "caret" R package. The clinical data of the patients were evaluated. Univariate and multivariate logistic regression analysis were used to screen out the independent influencing factors and construct a nomogram prediction model. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) and clinical impact curves were used to evaluate the discrimination, accuracy, and clinical application efficacy of the prediction model.

Results

Gender, recurrence stones, blood uric acid value, urine pH, and urine bacterial culture (P < 0.05) were independent predictors of infection stones, and a nomogram prediction model (https://zhaoyshenjh.shinyapps.io/DynNomInfectionStone/) was constructed using these five parameters. The area under the ROC curve of the training group was 0.901, 95% confidence interval (CI) (0.865–0.936), and the area under the ROC curve of the validation group was 0.960, 95% CI (0.921–0.998). The results of the calibration curve for the training group showed a mean absolute error of 0.015 and the Hosmer–Lemeshow test P > 0.05. DCA and clinical impact curves showed that when the threshold probability value of the model was between 0.01 and 0.85, it had the maximum net clinical benefit.

Conclusions

The nomogram developed in this study has good clinical predictive value and clinical application efficiency can help with risk assessment and decision-making for infection stones in diagnosing and treating urolithiasis.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ROC:

Receiver operating characteristic

AUC:

Area under curve

CI:

Confidence interval

DCA:

Decision curve analysis

CT:

Computed tomography

PCNL:

Percutaneous nephrolithotomy

LPNL:

Laparoscopic nephrolithotomy

URSL:

Ureteroscopic lithotripsy

LUL:

Laparoscopic ureterolithotomy

ESWL:

Extracorporeal shockwave lithotripsy

OR:

Odds ratio

BMI:

Body mass index

D-J:

Double J

WBC:

White blood cell

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Acknowledgements

This study was supported by the Xuzhou Key Research and Development Project (No. KC22152).

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Authors and Affiliations

  1. Department of Urology, Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009, Jiangsu, China

    Jinhong Shen & Yan Zhao

  2. Department of Urology, Xuzhou Central Hospital, Xuzhou, 221009, Jiangsu, China

    Jinhong Shen, Zhiliang Xiao, Xitao Wang & Yan Zhao

Authors
  1. Jinhong Shen
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  2. Zhiliang Xiao
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  3. Xitao Wang
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  4. Yan Zhao
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Contributions

SJH contributed to paper design, data analysis, and draft of manuscript. XZL was involved in data collection and management. WXT collected data. ZY performed revision of manuscript.

Corresponding author

Correspondence to Yan Zhao.

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Conflict of interest

The authors have declared that no competing interests exist.

Ethics approval

All data analysis was carried out in accordance with applicable laws and regulations described in the Declaration of Helsinki and approved by Medical Ethics Committee of Xuzhou Central Hospital approval, reference number: XZXY-LK-20230801-0129. This study is a retrospective study, all data are anonymous, and formal consent is not required with the consent of the Medical Ethics Committee of Xuzhou Central Hospital.

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Shen, J., Xiao, Z., Wang, X. et al. A nomogram clinical prediction model for predicting urinary infection stones: development and validation in a retrospective study. World J Urol 42, 211 (2024). https://doi.org/10.1007/s00345-024-04904-7

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