Clinical Features, Risk Factors, and Functional Outcomes of Varicella Zoster Virus Central Nervous System Infections: A BUHASDER Multicenter Retrospective Cohort Study

Varicella zoster virus (VZV) is a well-established cause of central nervous system infections, including meningitis and encephalitis, which may lead to significant morbidity and mortality. These infections can occur with or without the typical dermatomal rash and may present with a wide spectrum of neurological findings. With increasing life expectancy and the widespread use of immunosuppressive and immunomodulatory therapies, the incidence of VZV reactivation and related complications is expected to rise.

Despite advances in molecular diagnostic methods, which have improved the timely and accurate detection of VZV in cerebrospinal fluid, large-scale multicenter data on VZV-related central nervous system infections remain limited, particularly at the national level.

This multicenter retrospective cohort study aims to evaluate the clinical features, risk factors, and functional outcomes of adult patients diagnosed with VZV meningitis and encephalitis. Data will be collected from participating centers using a standardized dataset, including demographic characteristics, clinical presentation, comorbidities, laboratory findings, cerebrospinal fluid analysis, imaging results, treatment details, and follow-up outcomes.

The study will compare patients with and without complications to identify factors associated with adverse outcomes. Complications assessed during follow-up (minimum 1 month, up to 12 months) will include neurological sequelae such as epilepsy, cognitive impairment, cranial nerve deficits, and functional disability measured by the Modified Rankin Scale. Mortality and intensive care unit requirements will also be evaluated.

Statistical analyses will be performed to determine associations between clinical variables and outcomes. Categorical variables will be compared using chi-square or Fisher's exact tests, and continuous variables will be analyzed according to their distribution. Logistic regression models will be used to identify independent predictors of complications. A p-value of <0.05 will be considered statistically significant.