Multivariable and Bayesian network analysis of outcome predictors in acute aneurysmal subarachnoid hemorrhage: Review of a pure surgical series in the post-international subarachnoid aneurysm trial era

Document Type

Article

Abstract

Background: Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatmentmodalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. Objective: To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. Methods: We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Results: Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Conclusion: Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Publication Date

6-1-2018

Publication Title

Operative Neurosurgery

ISSN

23324252

E-ISSN

23324260

Volume

14

Issue

6

First Page

603

Last Page

610

PubMed ID

28973260

Digital Object Identifier (DOI)

10.1093/ons/opx163

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