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Related Experiment Video

Updated: Jul 4, 2025

Standardized Colon Ascendens Stent Peritonitis in Rats - a Simple, Feasible Animal Model to Induce Septic Acute Kidney Injury

Standardized Colon Ascendens Stent Peritonitis in Rats - a Simple, Feasible Animal Model to Induce Septic Acute Kidney Injury

Published on: February 15, 2022

A novel post-percutaneous nephrolithotomy sepsis prediction model using machine learning.

Rong Shen¹, Shaoxiong Ming¹, Wei Qian²

¹Department of Urology, Shanghai Changhai Hospital, No.168 Changhai Rd, Shanghai, 200433, China.

|February 2, 2024

Summary

This summary is machine-generated.

This study developed a machine learning model to predict sepsis after percutaneous nephrolithotomy (PCNL). The model identifies high-risk patients, enabling early intervention to reduce sepsis incidence.

Keywords:

Early intervention Machine learning Percutaneous nephrolithotomy Sepsis Urinary calculi

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Published on: December 9, 2022

Area of Science:

Urology
Nephrology
Medical Informatics

Background:

Percutaneous nephrolithotomy (PCNL) is a common procedure for kidney stones.
Sepsis is a serious complication following PCNL, necessitating proactive risk identification.
Machine learning offers potential for developing predictive models in surgical outcomes.

Purpose of the Study:

To create a machine learning-based predictive model for sepsis post-PCNL.
To identify patients at high risk of developing sepsis after PCNL.
To facilitate early diagnosis and intervention for urologists.

Main Methods:

Retrospective analysis of 694 patients undergoing PCNL.
Development of a machine learning model using 22 preoperative and intraoperative parameters.
100-fold Monte Carlo cross-validation with an 80% training and 20% validation set.

Main Results:

Sepsis occurred in 45 of 694 patients.
The predictive model demonstrated strong performance: AUC=0.89, 87.8% sensitivity, 86.9% specificity, 87.4% accuracy.
Key predictors included preoperative urine culture, sex, antibiotic use, WBC count, and stone characteristics.

Conclusions:

The developed predictive model is effective for estimating sepsis risk after PCNL.
Early intervention guided by this model can potentially reduce sepsis incidence.
This tool supports urologists in managing patients at risk for postoperative sepsis.