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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Machine learning algorithm to predict postoperative bleeding complications after lateral decubitus percutaneous

Rui Meng¹, Weining Wang¹, Zhipeng Zhai¹

¹Department of Urology, YuQuan Hospital, Tsinghua University, Beijing, China.

|January 26, 2024

Summary

This summary is machine-generated.

Machine learning models can predict bleeding after percutaneous nephrolithotomy (PCNL). The Random Forest model showed the best performance, aiding urologists in treatment decisions for kidney and ureteral stones.

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Area of Science:

Urology
Medical Informatics
Nephrology

Background:

Postoperative bleeding is a significant risk following percutaneous nephrolithotomy (PCNL).
Accurate prediction of bleeding complications is crucial for patient management and surgical planning.

Purpose of the Study:

To develop and evaluate machine learning models for predicting postoperative bleeding after lateral decubitus PCNL.
To identify key factors influencing bleeding risk in patients undergoing PCNL for renal and upper ureteral stones.

Main Methods:

Retrospective collection of data from 356 patients undergoing lateral decubitus PCNL.
Development of predictive models using Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost).
Evaluation of model performance using accuracy, precision, F1-score, ROC curves, and AUC.

Main Results:

12.07% of patients experienced postoperative bleeding complications.
The Random Forest model achieved the highest accuracy (74.5%) and AUC (0.679).
Logistic Regression and Random Forest models demonstrated reasonable predictive capabilities.

Conclusions:

Machine learning models, particularly Random Forest, can effectively predict postoperative bleeding after lateral decubitus PCNL.
These predictive tools can assist urologists in optimizing treatment strategies and managing potential complications.
The models may also aid in predicting stone residue and recurrence post-PCNL.