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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

Jiaxin Li¹, Yao Du², Gaoming Huang¹

¹Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.

Scientific Reports

|February 5, 2025

Summary

A new machine learning model uses Computed Tomography (CT) values to predict Systemic Inflammatory Response Syndrome (SIRS) after kidney stone surgery. This tool helps identify high-risk patients for early intervention against post-operative urosepsis.

Keywords:

CT values Machine learning Renal endoscopic lithotripsy Shapley Additive exPlanations (SHAP)Urosepsis

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Area of Science:

Urology
Medical Imaging
Artificial Intelligence

Background:

Post-operative Systemic Inflammatory Response Syndrome (SIRS) is a significant concern following endoscopic kidney stone surgery.
Early identification of patients at risk for SIRS and subsequent urosepsis is crucial for timely intervention and improved outcomes.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting SIRS after endoscopic kidney stone surgery.
To utilize Computed Tomography (CT) values and other clinical variables for risk stratification.
To provide urologists with a tool for early diagnosis and management of post-operative complications.

Main Methods:

A retrospective study included 833 patients undergoing retrograde intrarenal surgery (RIRS) or percutaneous nephrolithotomy (PCNL).
Five ML algorithms were trained using ten preoperative/intraoperative variables to predict SIRS.
The SHapley Additive exPlanations (SHAP) method was employed to interpret feature importance.

Main Results:

15.1% of patients (126/833) developed postoperative SIRS.
All five ML models showed strong predictive performance (AUC range: 0.690-0.858).
The eXtreme Gradient Boosting (XGBoost) model achieved the highest AUC (0.858), with high sensitivity (0.877) and specificity (0.981).
Key predictors identified by the ML model and SHAP analysis included Hounsfield Unit (HU), urinary protein, stone burden, and serum uric acid.

Conclusions:

A novel ML model effectively predicts SIRS after endoscopic kidney stone surgery using CT values and clinical data.
The developed model demonstrates high accuracy and can aid in assessing urosepsis risk in postoperative patients.
This tool supports urologists in early risk identification and management strategies for post-surgical complications.