Aneurysmal Subarachnoid Hemorrhage Clinical Trial
Official title:
Machine Learning-based Model for Prediction of Complications, Disability, and Death in Patients With Aneurysmal Subarachnoid Hemorrhage
This study aims to prospectively collect the clinical and radiogical features to investigate the associations between the potential risk factors and secondary complications, adverse long-term functional outcomes, and death in patients with aneurysmal subarachnoid haemorrhage (aSAH).
Aneurysmal subarachnoid haemorrhage (aSAH) is an acute cerebrovascular disease with high mortality and morbidity. The early and delayed stages of brain injury after subarachnoid hemorrhage is usually accompanied by complications such as vasospams, delayed cerebral ischemia, rebleeding, hydrocephalus, and other organ damage, which determines a poor functional outcome. It is significantly important to investigate the association between the potential clinical and radiogical features with these complications and functional outcome after aSAH, including a well-known factor as total bleeding volume of subarachnoid hemorrhage. In this study, the investigators prospectively collected a huge dataset of aSAH to fully investigate these correlation, including the association between radiomics features with these complications to prove the role of early radiogical factors. Then, the investigators will train models based on these potential contributing factors to highly predict these complications, disabilty, and death. ;
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