Longitudinal Biomarker Trajectories for EVD-Associated Infection Prediction: A Trajectory-Based Machine Learning Framework With Biological Onset Characterization.

BACKGROUND

EVD-associated infection occurs in 1%-40% of patients with indwelling external ventricular drains. Assessing cerebrospinal fluid (CSF) parameters remains challenging, especially in hemorrhagic neurological disease. This study developed and validated a machine learning framework for real-time prediction of EVD-associated infection.

METHODS

A retrospective cohort of 367 neurocritical patients with EVDs across 8419 EVD patient-days (2020-2025) was analyzed. Seven serially monitored biomarkers-white blood cell count (WBC), C-reactive protein; procalcitonin, and CSF cell count, lactate, glucose, and protein-were used to derive temporal features. A biological onset sub-study applied a composite scoring algorithm to 52 infected patients to identify the earliest day of coordinated multi-marker biomarker deterioration preceding clinical diagnosis.

RESULTS

Among 367 patients, 88 (24%) developed infection at a median of 9 days after drain insertion, with a median EVD duration of 17 days (IQR: 12-24) for the full cohort. The ensemble achieved AUC-ROC of 0.833 at 24 hours and 0.822 at 48 hours, with area under the precision-recall curve of 0.469 and 0.478, respectively. Sensitivity was 69%, specificity 92%, and negative predictive value 100% at 24 hours. The most influential predictors included dynamic changes in WBC and CSF lactate dynamics. In the biological onset sub-analysis, coordinated biomarker deterioration preceded clinical diagnosis by a median of 2 days (IQR: 1-4).

CONCLUSIONS

Longitudinal biomarker trajectory monitoring, combined with machine learning, enables timely prediction of EVD-associated infection. Biomarker changes precede clinical recognition by a median of two days, supporting prospective evaluation of daily risk monitoring in neurocritical care.