Development and validation of an interpretable machine learning model for predicting systemic inflammatory response
Leibo Wang1, Wei He2, Tao Qiu1
1Department of Urology, Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China.
Background:
Systemic inflammatory response syndrome (SIRS) is a common and potentially reversible early infectious complication after percutaneous nephrolithotomy (PCNL). Accurate perioperative risk stratification may enable timely intervention; however, existing prediction models show limited generalisability and poor clinical interpretability.
Methods:
We conducted a multicenter retrospective cohort study including patients who underwent PCNL at three hospitals in China between Jan 1, 2015, and Dec 30, 2024. Patients from one center were randomly divided into training and internal validation cohorts, while two independent cohorts served for external validation. Perioperative demographic, laboratory, imaging, and surgical variables were collected. Feature selection was performed using least absolute shrinkage and selection operator regression and the Boruta algorithm. Seven machine learning models were developed and compared. Given outcome imbalance, the area under the precision-recall curve (AUPRC) was prespecified as the primary performance metric. Model calibration, decision curve analysis, and SHapley Additive exPlanations (SHAP) were used to assess reliability, clinical utility, and interpretability.
Results:
A total of 2,684 patients were included, with postoperative SIRS occurring in 9.8%-12.7% across cohorts. Six predictors were consistently identified: stone size, urine nitrite, urine culture results, operative time, residual stone status, and the neutrophil-to-albumin ratio. The random forest model showed the most balanced performance, with AUPRC values ranging from 0.581 to 0.641 and AUROC values from 0.873 to 0.920 across validation cohorts. Calibration was satisfactory, and decision curve analysis demonstrated a higher net clinical benefit than alternative models. SHAP analysis revealed clinically coherent, non-linear associations between key predictors and SIRS risk. An online prediction tool was developed to support individualized risk estimation.
Conclusion:
An interpretable machine learning model based on routinely available perioperative variables can reliably predict SIRS after PCNL across multiple centers. This approach may facilitate early postoperative risk stratification and support timely clinical decision-making to mitigate infectious complications. Prospective and multi-regional validation is warranted.
