Management of Ruptured Intracranial Arachnoid Cysts with Hemorrhage: A Bayesian Network Analysis of Factors Affecting...

Arachnoid cysts are extra-axial cerebrospinal fluid collections within the arachnoid membrane. Ruptured or hemorrhagic arachnoid cysts, although rare, present significant controversies in management. The present study is an attempt to analyze the factors contributing to management decision of ruptured/hemorrhagic arachnoid cysts using patient-level data from the literature.

A literature search was conducted on PubMed and EMBASE to identify case reports and series of ruptured arachnoid cysts. Tree-augmented naïve Bayes (TAN) classifiers were implemented to analyze factors influencing surgical decision. The dataset was split into training and testing sets (0.75:0.25) and augmented using data augmentation techniques to address class imbalance. TAN classifiers were evaluated for accuracy and area under the curve, and a web application was developed to explore the networks.

The dataset included 254 unique cases after exclusion of missing data. Middle cranial fossa cysts accounted for 95% of cases, with a male predominance (M:F ratio 4.29:1). Management was predominantly surgical (89.8%), with craniotomy being the most common procedure. TAN classifiers for surgery and type of surgery were validated internally with accuracies of 90.48 and 75%, respectively. Cyst location, presence and type of hemorrhage, patient age group, Galassi classification were key influencing variables. The choice of surgical modality was influenced by additional variables like head injury, seizure, and macrocrania.

TAN models highlighted the interrelated factors influencing management decision but do not propose definitive strategies. The generalizability of the findings are limited by heterogenous data, imbalance of various management strategies, particularly conservative management, and evolution of surgical techniques over time. The complexity of decision-making underscores the need for multicenter registries to improve data quality and to formulate optimal management strategy.