Effectiveness of external ventricular drainage versus lumbar drainage in aneurysmal subarachnoid hemorrhage: A...

There is no universally recognized optimal cerebrospinal fluid (CSF) drainage strategy. This study aimed to comprehensively compare the efficacy and safety of external ventricular drainage (EVD) and lumbar drainage (LD) in the management of aneurysmal subarachnoid hemorrhage (aSAH).

We consecutively included aSAH patients who underwent surgical treatment with CSF drainage between January 2017 and December 2020 in the Chinese Multicenter Cerebral Aneurysm Database (CMAD). After 1:1 propensity score matching (PSM), intergroup outcomes and in-hospital complications were compared between the EVD and LD groups. The Generalized Estimating Equation (GEE) model was used to assess the relationship between the drainage method and intergroup outcomes. Kaplan-Meier curves were used to analyze survival, and Cox proportional hazard modeling was performed to identify risk factors for mortality.

952 aSAH patients were initially included. After PSM, 167 patients receiving EVD were matched with 167 patients receiving LD, resulting in a total of 334 patients for the matched analysis. Patients receiving EVD had higher 2-year mortality (27.1% vs 15.1%, p = 0.011) and worse functional outcomes at discharge (45.5% vs 34.0%, adjusted OR: 0.567 95% CI: 0.324-0.991, p = 0.046). However, functional outcomes at 2 years did not show significant differences (23.4% vs 22.0%, adjusted OR: 0.811 95% CI: 0.375-1.754, p = 0.594). No differences were observed in in-hospital complication rates between the two groups. Multivariable Cox proportional hazard modeling identified WFNS Grade IV-V as a risk factor for mortality in the EVD group. In the LD group, mortality risk factors included age ⩾ 65 years, and diabetes.

LD demonstrated significant advantages in short-term functional outcomes, and long-term survival outcomes, but did not demonstrate significant differences in long-term functional outcomes and in-hospital complications. Risk factors identified in the prognostic analysis may inform clinical decision-making.