Machine Learning to Predict Postoperative Pneumonia in Brain Tumor Patients

Rationale Postoperative pneumonia (POP) remains a frequent and clinically important complication after elective craniotomy for brain tumor resection, contributing to prolonged hospitalization, increased cost, and worse clinical outcomes. Conventional POP risk assessment is often experience-based or relies on simplified scoring approaches, which may not adequately capture nonlinear interactions among perioperative factors. This study implements an explainable machine-learning (ML) prediction model within routine perioperative workflows and evaluates whether model-assisted care can improve POP prevention and related resource utilization compared with usual care.

Decision support system

An explainable gradient boosting machine (GBM) model is used to estimate individual POP risk from routinely available perioperative variables. Interpretability is provided using SHAP-based explanations at two complementary levels:

Population level: summarizes the most influential predictors and selected interaction patterns to support clinical understanding and model governance.

Patient level: generates an individualized contribution visualization (e.g., waterfall-style), highlighting the main drivers of a specific patient's risk estimate.

The system automatically assigns a risk tier (low, moderate, high, or very high) and links each tier to standardized prevention pathway templates (e.g., airway management optimization, antimicrobial stewardship-consistent strategies per local policy, and nutritional support). The tool does not mandate treatment; clinicians may accept, modify, or override any suggestion.

Evaluation framework The overall project includes retrospective model development/optimization, prospective external validation/calibration, and a pragmatic implementation evaluation. The registered interventional evaluation uses a multicenter, cluster randomized crossover design with monthly alternating periods of model-assisted care versus usual care. Allocation procedures, eligibility criteria, planned enrollment, and endpoint definitions/time windows are specified in the corresponding record modules (Study Design, Arms/Interventions, Outcome Measures, and Eligibility) to avoid duplication in this section.

Implementation and integration The model is deployed as a lightweight web service with unified APIs and data-exchange formats to enable non-disruptive integration with hospital information systems (HIS) and electronic medical records (EMR). A web-based front end and a Python-based back end support RESTful calls and are designed for low-latency inference (target single-prediction latency <200 ms), suitable for perioperative and inpatient workflows.

Data governance and model updating To support long-term generalizability across hospitals and mitigate dataset shift, the project establishes a closed-loop maintenance process ("local de-identification → cloud retraining → model version management → edge deployment"). Model updates are version-controlled and deployed under governance procedures consistent with local regulations, institutional policies, and applicable ethics approvals.

Inclusion criteria

• Age ≥ 18 years.
• Undergoing elective craniotomy for intracranial brain tumor resection.
• Perioperative clinical data available in the electronic medical record to derive required predictors.
• Expected postoperative survival ≥ 7 days.

Exclusion criteria

• Evidence of active infection (including pneumonia) prior to surgery.
• Thoracic surgery or severe chest trauma within 30 days prior to craniotomy.
• Spinal tumors or extracranial peripheral nerve tumors.
• Pregnancy or lactation.
• Hospice care, expected survival < 7 days, or insufficient data completeness for model calculation.