Highlights
- •This study evaluated the contribution of risk factors correlated with angiographic vasospasm using XAI.
- •Results showed an association between aneurysm size and age as well as angiographic vasospasm.
- •This study provided evidence about risk factors of cerebral vasospasm and the efficacy of machine learning models.
Abstract
Cerebral vasospasm (CAV) is a major complication of aneurysmal subarachnoid hemorrhage
(aSAH) in patients with ruptured intracranial aneurysm. Explainable artificial intelligence
(XAI) was used to analyze the contribution of risk factors on the development of CAV.
We obtained data about patients (n = 343) treated for aSAH in our hospital. Predictive
factors including age, aneurysm size, Hunt and Hess grade, and modified Fisher grade
were used as input to analyze the contribution and correlation of factors correlated
with CAV using a random forest regressor. An analysis conducted using an XAI model
showed that aneurysm size (27.6%) was most significantly associated with the development
of CAV, followed by age (20.7%) and Glasgow coma scale score (7.1%). In some patients
with an estimated artificial intelligence-selected CAV value of 51%, the important
risk factors were aneurysm size (9.1 mm) and location, and hypertension is also considered
a major influencing factor. We could predict that Fisher grade 3 contributed to 20.3%,
and the group using Antiplatelet contributed to 12.2% which is expected to lower cerebral
CAV compared to the Control group (16.9%). The accuracy rate of the XAI system was
85.5% (area under the curve = 0.88). Using the modeling, aneurysm size and age were
quantitatively analyzed and were found to be significantly associated with CAV in
patients with aSAH. Hence, XAI modeling techniques can be used to analyze factors
correlated with CAV by schematizing prediction results in some patients. Moreover,
poor Fisher grade and use of postoperative antiplatelet agent are important factors
for prediction of CAV.
Graphical abstract
Graphical Abstract
Keywords
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References
- Hyperacute vasospasm after aneurysmal subarachnoid hemorrhage.Neurocrit Care. 2016; 24: 180-188
- Risk factor for poor outcome in elderly patients with aneurysmal subarachnoid hemorrhage based on post hoc analysis of the modified WFNS scale study.World Neurosurg. 2020; 141: e466-e473
- Multivariate analysis of predictors of cerebral vasospasm occurrence after aneurysmal subarachnoid hemorrhage.Stroke. 1999; 30: 1402-1408
Haley EC, Jr., Kassell NF, Torner JC, Truskowski LL, Germanson TP. A randomized trial of two doses of nicardipine in aneurysmal subarachnoid hemorrhage. A report of the Cooperative Aneurysm Study. J Neurosurg. 1994;80:788-96.
- Risk factors for cerebral vasospasm following aneurysmal subarachnoid hemorrhage: a review of the literature.World Neurosurg. 2016; 85: 56-76
- Predicting long-term outcomes after poor-grade aneurysmal subarachnoid hemorrhage using decision tree modeling.Neurosurgery. 2020; 87: 523-529
- Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage.J Neurointerv Surg. 2019; 11: 497-502
- Artificial intelligence in the management of intracranial aneurysms: current status and future perspectives.AJNR Am J Neuroradiol. 2020; 41: 373-379
- Random forests.Machine Learn. 2001; 45: 5-32
- Random forest: a classification and regression tool for compound classification and QSAR modeling.J Chem Inf Comput Sci. 2003; 43: 1947-1958
- From local explanations to global understanding with explainable AI for trees.Nature Machine Intel. 2020; 2: 56-67
- A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare.BMC Med Inf Decis Making. 2020; 20: 1-16
Cohen SB, Ruppin E, Dror G. Feature selection based on the Shapley value. IJCAI2005. p. 665-70.
Bowen D, Ungar L. Generalized SHAP: Generating multiple types of explanations in machine learning. arXiv preprint arXiv:200607155. 2020.
Bouneder L, Léo Y, Lachapelle A. X-SHAP: towards multiplicative explainability of Machine Learning. arXiv preprint arXiv:200604574. 2020.
- Receiver operating characteristic (ROC) curve for medical researchers.Indian Pediatr. 2011; 48: 277-287
- Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.Ann Clin Transl Neurol. 2020; 7: 2178-2185
- Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.Acta Neurochir (Wien). 2020; 162: 3093-3105
- Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques.IEEE Trans Inf Technol Biomed. 2009; 13: 794-801
- Thick and diffuse cisternal clot independently predicts vasospasm-related morbidity and poor outcome after aneurysmal subarachnoid hemorrhage.J Neurosurg. 2020; 1–9
- Risk factors associated with cerebral vasospasm following aneurysmal subarachnoid hemorrhage.Neurol Med Chir (Tokyo). 2014; 54: 465-473
Lee E, Braines D, Stiffler M, Hudler A, Harborne D. Developing the sensitivity of LIME for better machine learning explanation. Artificial intelligence and machine learning for multi-domain operations applications: International Society for Optics and Photonics; 2019. p. 1100610.
- Fooling lime and shap: adversarial attacks on post hoc explanation methods.in: Proceedings of the AAAI/ACM conference on AI, ethics, and society. 2020: 180-186
- XAI-Explainable artificial intelligence.Sci Robot. 2019; 4
- Factors associated with the development of vasospasm after planned surgical treatment of aneurysmal subarachnoid hemorrhage.J Neurosurg. 2003; 99: 644-652
- Risk factors and outcome in 100 patients with aneurysmal subarachnoid hemorrhage.Arq Neuropsiquiatr. 2001; 59: 676-680
- Age-associated vasospasm in aneurysmal subarachnoid hemorrhage.J Stroke Cerebrovascular Dis. 2013; 22: 22-27
Article info
Publication history
Accepted:
July 18,
2021
Received:
March 17,
2021
Identification
Copyright
© 2021 Published by Elsevier Ltd.
