Efficacy and safety of ureteroscopy in children with lower pole renal stones : a machine learning predictive model from the EAU section of endourology
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Summary
This summary is machine-generated.Machine learning accurately predicts outcomes for pediatric kidney stone treatment using flexible ureteroscopy. Key factors like stone number and burden help stratify risk for incomplete clearance.
Area Of Science
- Pediatric Urology
- Minimally Invasive Surgery
- Machine Learning in Medicine
Background
- Pediatric kidney stone disease, especially lower pole (LP) calculi, poses treatment challenges.
- Flexible ureteroscopy with laser lithotripsy (fURSL) is a common treatment, but outcomes vary.
- Predicting surgical success for pediatric LP stones requires further investigation.
Purpose Of The Study
- To apply machine learning (ML) models for predicting surgical outcomes in pediatric patients undergoing fURSL.
- To identify key preoperative predictors of incomplete stone clearance in lower pole calculi.
Main Methods
- Retrospective analysis of 91 pediatric patients (≤16 years) with isolated LP stones undergoing fURSL.
- Development and evaluation of 15 ML models, including Random Forest and Gradient Boosting.
- Utilized SHAP values for interpretability to identify significant predictors.
Main Results
- Random Forest model achieved 80.95% validation accuracy and 76.67% F1-score.
- Stone number, total stone burden, age, and operative time were identified as top predictors of outcome.
- fURSL demonstrated safety and efficacy, though incomplete stone clearance was noted.
Conclusions
- ML models show strong predictive performance for fURSL outcomes in pediatric kidney stone patients.
- ML can aid in preoperative risk stratification for lower pole calculi.
- Further validation and prospective studies are needed to refine ML tools for clinical application.
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