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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine
Rong Xu1, Jia-Jia Wang2, Wei-Hong Zhao1
1Department of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China.
Purpose:
Postoperative fever is a common complication following percutaneous nephrolithotomy (PCNL) that occurs even in patients with sterile urine cultures. Traditional risk-assessment tools are insufficient in this subset of patients. This study aims to develop a risk prediction model for detecting postoperative fever in patients with sterile preoperative urine cultures by integrating the Mayo Adhesive Probability (MAP) score with machine learning (ML) techniques.
Methods:
This retrospective cohort study included 730 patients with sterile urine cultures who underwent mini-percutaneous nephrolithotomy (mini-PCNL) at Taizhou Hospital from March 2022 to March 2025. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify key variables. Ten ML models were built, and Shapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability, and the optimal model was selected. An online tool was deployed for clinical use.
Results:
Postoperative fever occurred in 17.4% of the patients. Logistic regression (LR) demonstrated the best predictive performance (AUC = 0.914, accuracy = 92.1%, specificity = 97.3%). Major predictive factors for fever risk included MAP score ≥ 3, diabetes mellitus, female sex, positive urine leukocytes, and low lymphocyte-monocyte ratio (LMR). SHAP analysis confirmed MAP score and urine leukocytes as the most influential variables. Limitations of the study include its single-centre design.
Conclusions:
The proposed tool, combining MAP scores with ML, significantly enhances the accuracy of predicting the risk of postoperative fever following mini-PCNL in patients with sterile urine cultures. The LR model demonstrates strong utility and interpretability, offers personalised risk assessments preoperatively and constitutes a practical and accessible online tool for individualised clinical decision-making.
Insights
A new tool combining Mayo Adhesive Probability (MAP) scores with machine learning (ML) accurately predicts postoperative fever after mini-percutaneous nephrolithotomy (mini-PCNL) in patients with sterile urine. This aids in personalized preoperative risk assessment and clinical decision-making.
Area of Science:
- Urology
- Nephrology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Postoperative fever is a common complication after percutaneous nephrolithotomy (PCNL), even with sterile urine cultures.
- Existing risk assessment tools are inadequate for predicting fever in this specific patient group.
- Accurate prediction of postoperative fever is crucial for patient management and resource allocation.
Purpose of the Study:
- To develop a predictive model for postoperative fever in patients undergoing mini-PCNL with sterile preoperative urine cultures.
- To integrate the Mayo Adhesive Probability (MAP) score with machine learning (ML) techniques for enhanced risk prediction.
- To create an interpretable and clinically applicable tool for preoperative risk assessment.
Main Methods:
- Retrospective cohort study of 730 patients undergoing mini-PCNL with sterile urine cultures.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify key predictive variables.
- Development and comparison of ten ML models, with Shapley Additive exPlanations (SHAP) for interpretability; Logistic Regression (LR) selected as optimal.
- Deployment of an online tool for clinical use.
Main Results:
- Postoperative fever occurred in 17.4% of patients.
- The LR model achieved high predictive performance (AUC=0.914, accuracy=92.1%, specificity=97.3%).
- Key predictors included MAP score ≥3, diabetes mellitus, female sex, positive urine leukocytes, and low lymphocyte-monocyte ratio (LMR).
- SHAP analysis highlighted MAP score and urine leukocytes as most influential factors.
Conclusions:
- The integrated MAP score and ML model significantly improves the prediction of postoperative fever following mini-PCNL in patients with sterile urine.
- The LR model offers strong predictive utility, interpretability, and enables personalized preoperative risk assessment.
- The developed online tool provides a practical and accessible resource for individualized clinical decision-making.
