Analysis of risk factors correlated with angiographic vasospasm in patients with aneurysmal subarachnoid hemorrhage using explainable predictive modeling |
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| Affiliation: | 1. Department of Orthopedic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan;2. Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central 6, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan;3. Department of Emergency and Critical Care Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan;4. Department of Orthopedic Surgery, Mito Kyodo General Hospital, University of Tsukuba, 3-2-7, Miya-machi, Mito, Ibaraki 310-0015, Japan;5. Department of Orthopedic Surgery, Ibaraki Prefectural University of Health Sciences, 4669-2 Ami, Ami-machi, Inashiki-gun, Ibaraki 300-0394, Japan;6. Department of Biostatistics, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan;1. University of Texas Health at San Antonio, San Antonio, Texas, USA;2. Neurocritical Care Unit, Sanatorio Pasteur, Intensive Care Unit, Hospital San Juan Bautista, Catamarca, Argentina;3. Department of Epidemiology and Biostatistics, School of Medicine, University of Texas Health at San Antonio, Texas, USA;4. Department of Neurology, School of Medicine, University of Texas Health at San Antonio, Texas, USA;5. Department of Neurosurgery, School of Medicine, University of Texas Health at San Antonio, Texas, USA;1. Department of Neurosurgery, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China;2. Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, China;1. College of Physicians and Surgeons, Columbia University, New York, USA;2. Department of Neurosurgery, Columbia University, College of Physicians and Surgeons, New York, USA;3. Division of Neurocritical Care NYU Langone Medical Center, Departments of Neurology and Neurosurgery, New York, USA;4. Division of Critical Care Neurology, Department of Neurology, Columbia University, College of Physicians and Surgeons, New York, USA |
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| 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. |
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| Keywords: | Aneurysm Subarachnoid hemorrhage Explainable artificial intelligence Feature analysis Cerebral arterial vasospasm |
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