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Machine Learning-based Prediction of Complications, Disability, and Death in Patients With aSAH

Aneurysmal Subarachnoid Hemorrhage Clinical Trial

Official title:
Machine Learning-based Prediction of Complications, Disability, and Death in Patients With Aneurysmal Subarachnoid Hemorrhage

Clinical Trial Summary

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).

Clinical Trial Description

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, we 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, we will train models based on these potential contributing factors to predi radiomics models to highly predict these complications, disabilty, and death. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05738083
Study type Observational [Patient Registry]
Source Second Affiliated Hospital of Nanchang University
Contact Xingen Zhu, Prof
Phone 13803546020
Email zxg2008vip@163.com
Status Recruiting
Phase
Start date October 1, 2018
Completion date December 30, 2025
View Details
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