Intracranial Pressure during External Ventricular Drainage Weaning Is an Outcome Predictor of Traumatic Brain Injury

Rapid Versus Gradual Weaning of External Ventricular Drain: A Systematic Literature Review and Meta-analysis

The insertion of an external ventricular drain (EVD) is one of the most common neurosurgical procedures. Whether the weaning method (gradual or rapid) influences the ventriculoperitoneal shunt (VPS) insertion rate has not been conclusively established. The aim of this study is to provide a systematic literature review and conduct a meta-analysis of studies comparing gradual with rapid EVD weaning regarding VPS insertion rate. Articles were identified by searching the Pubmed/Medline, Embase, and Web of Science databases throughout October 2022. Two independent researchers assessed the studies for inclusion and quality. We included randomized trials, prospective cohort studies, and retrospective cohort studies, which compared gradual and rapid EVD weaning. The primary outcome was VPS insertion rate, whereas secondary outcomes were EVD-associated infection (EVDAI) rate and length of stay in the hospital and intensive care unit (ICU). Four studies directly comparing rapid versus gradual EVD weaning, with 1337 patients suffering from subarachnoid hemorrhage, were identified and included in the meta-analysis. VPS insertion rate was 28.1% and 32.1% in patients with gradual and rapid EVD weaning, respectively (relative risk 0.85, 95% confidence interval 0.49-1.46, p = 0.56). Further, the EVDAI rate was comparable between the groups (gradual group 11.2%, rapid group 11.5%, relative risk 0.67, 95% confidence interval 0.24-1.89, p = 0.45), whereas length of stay in the ICU and hospital were significantly shorter in the rapid weaning group (2.7 and 3.6 days, respectively; p < 0.01). Rapid EVD weaning seems comparable to gradual EVD weaning concerning VPS insertion rates and EVDAI, whereas hospital and ICU length of stay is significantly reduced.

Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study

Objective: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. Methods: A retrospective cohort study was conducted among patients admitted into neurointensive care units between 1 January 2015 and 31 December 2020 who underwent external ventricular drainage due to traumatic brain injury, hydrocephalus, and nonaneurysmal spontaneous intracranial hemorrhage. Multivariate logistic regression in combination with the least absolute shrinkage and selection operator regression was applied to derive prediction models and optimize variable selections. Other machine-learning algorithms, including the support vector machine and K-nearest neighbor, were also applied to derive alternative prediction models. Five-fold cross-validation was used to train and validate each model. Model performance was assessed by calibration plots, receiver operating characteristic curves, and decision curves. A nomogram analysis was developed to explicate the weights of selected features for the optimal model. Results: Multivariate logistic regression showed the best performance among the three tested models with an area under curve of 0.846 ± 0.006. Six variables, including hemoglobin, albumin, length of operation time, American Society of Anesthesiologists grades, presence of traumatic subarachnoid hemorrhage, and a history of diabetes, were selected from 37 variable candidates as the top-weighted prediction features. The decision curve analysis showed that the nomogram could be applied clinically when the risk threshold is between 20% and 100%. Conclusions: The occurrence of external ventricular-drainage-associated intracranial infections could be predicted using optimal models and feature-selection approaches, which would be helpful for the prevention and treatment of this complication in neurointensive care units.