An Interpretable and Clinically Deployable Machine Learning Model for Predicting Early Postoperative Pneumonia of Brain Tumor: a Multicenter Diagnostic Study

Postoperative pneumonia (POP) is a common and serious complication after elective craniotomy for brain tumor resection. POP often develops within the first week after surgery and may lead to prolonged hospitalization, higher medical costs, and increased risk of severe illness. Because symptoms can be subtle in neurosurgical patients, POP may be detected late, limiting timely prevention and treatment.

This study will evaluate whether a machine-learning-based clinical decision support tool can help clinicians identify patients at high risk for POP early and improve perioperative preventive care. The tool uses routinely collected clinical information to estimate an individual patient's POP risk and provides an easy-to-understand explanation of key risk drivers. Based on the predicted risk level (low, moderate, high, or very high), the system suggests standardized preventive care pathways (e.g., perioperative airway management, targeted antibiotic strategies per local practice, and nutritional support), while allowing clinicians to override recommendations at any time.

Participants will be adults undergoing their first elective craniotomy for brain tumor resection at participating neurosurgical centers. The primary outcome is the occurrence of POP within 7 days after surgery, defined using CDC/NHSN criteria. Secondary outcomes include antibiotic use intensity, length of hospital stay, direct medical cost, and clinician decision confidence. Participants will be followed at postoperative days 1, 3, and 7 using electronic medical record review and phone confirmation when needed.

The goal of this study is to determine whether integrating an explainable AI risk prediction tool into routine care can reduce POP and improve the quality and efficiency of perioperative management after brain tumor surgery.