• Journal of neurosurgery · Jun 2019

    A sustained systemic inflammatory response syndrome is associated with shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.

    • Aaron P Wessell, Matthew J Kole, Gregory Cannarsa, Jeffrey Oliver, Gaurav Jindal, Timothy Miller, Dheeraj Gandhi, Gunjan Parikh, Neeraj Badjatia, E Francois Aldrich, and J Marc Simard.
    • Departments of1Neurosurgery.
    • J. Neurosurg. 2019 Jun 1; 130 (6): 198419911984-1991.

    ObjectiveThe authors sought to evaluate whether a sustained systemic inflammatory response was associated with shunt-dependent hydrocephalus following aneurysmal subarachnoid hemorrhage.MethodsA retrospective analysis of 193 consecutive patients with aneurysmal subarachnoid hemorrhage was performed. Management of hydrocephalus followed a stepwise algorithm to determine the need for external CSF drainage and subsequent shunt placement. Systemic inflammatory response syndrome (SIRS) data were collected for all patients during the first 7 days of hospitalization. Patients who met the SIRS criteria every day for the first 7 days of hospitalization were considered as having a sustained SIRS. Univariate and multivariate regression analyses were used to determine predictors of shunt dependence.ResultsSixteen percent of patients required shunt placement. Sustained SIRS was observed in 35% of shunt-dependent patients compared to 14% in non-shunt-dependent patients (p = 0.004). On multivariate logistic regression, female sex (OR 0.35, 95% CI 0.142-0.885), moderate to severe vasospasm (OR 3.78, 95% CI 1.333-10.745), acute hydrocephalus (OR 21.39, 95% CI 2.260-202.417), and sustained SIRS (OR 2.94, 95% CI 1.125-7.689) were significantly associated with shunt dependence after aneurysmal subarachnoid hemorrhage. Receiver operating characteristic analysis revealed an area under the curve of 0.83 for the final regression model.ConclusionsSustained SIRS was a predictor of shunt-dependent hydrocephalus following aneurysmal subarachnoid hemorrhage even after adjustment for potential confounding variables in a multivariate logistic regression model.

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