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

Abstract

Abstract Objectives To establish a predictive model for sepsis after percutaneous nephrolithotomy (PCNL) using machine learning to identify high-risk patients and enable early diagnosis and intervention by urologists. Methods A retrospective study including 694 patients who underwent PCNL was performed. A predictive model for sepsis using machine learning was constructed based on 22 preoperative and intraoperative parameters. Results Sepsis occurred in 45 of 694 patients, including 16 males (35.6%) and 29 females (64.4%). Data were randomly segregated into an 80% training set and a 20% validation set via 100-fold Monte Carlo cross-validation. The variables included in this study were highly independent. The model achieved good predictive power for postoperative sepsis (AUC = 0.89, 87.8% sensitivity, 86.9% specificity, and 87.4% accuracy). The top 10 variables that contributed to the model prediction were preoperative midstream urine bacterial culture, sex, days of preoperative antibiotic use, urinary nitrite, preoperative blood white blood cell (WBC), renal pyogenesis, staghorn stones, history of ipsilateral urologic surgery, cumulative stone diameters, and renal anatomic malformation. Conclusion Our predictive model is suitable for sepsis estimation after PCNL and could effectively reduce the incidence of sepsis through early intervention.

Keywords

• Urinary calculi
• Percutaneous nephrolithotomy
• Sepsis
• Machine learning
• Early intervention