Abstract
Background: Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) have 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 aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operator 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% 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.
Methods: We reviewed a single surgeon’s case series of 226 patients suffering aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operator 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% 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.
| Original language | English |
|---|---|
| Journal | Operative Neurosurgery |
| Early online date | 31 Jul 2017 |
| DOIs | |
| Publication status | Published - 2017 |