Multi-Center Registry Cohort Study on Prognostic Factors and Prediction Model Construction in Aneurysmal SAH
NCT ID: NCT05738083
Last Updated: 2024-08-22
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
5000 participants
OBSERVATIONAL
2018-10-01
2029-12-30
Brief Summary
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Detailed Description
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The primary objective of PROSAH-MPC is to construct and validate robust prediction models that can accurately forecast the risks of complications, disability, and mortality in aSAH patients. By leveraging the strengths of a large, multi-center, prospective cohort design, the study aims to overcome the limitations of previous single-center, limited sample size, or retrospective studies, enabling a more holistic and generalizable understanding of the disease.
Central to the study is the collection of extensive clinical and radiological data from enrolled patients, including demographics, medical histories, treatment regimens, radiological features, and follow-up outcomes. Radiomic analysis of imaging data, such as CT and MRI scans, will be employed to extract subtle but crucial features that may predict patient outcomes by deep learning. This data-rich approach ensures that the prediction models are built on a solid foundation of evidence-based knowledge.
PROSAH-MPC's ultimate goal is to transform the way we approach aSAH management by providing clinicians with reliable tools to assess individual patient risks and tailor treatment plans accordingly. The validated prediction models have the potential to enhance early recognition of high-risk patients, facilitate timely interventions, and ultimately improve patient outcomes and quality of life.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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aneurysmal subarachnoid hemorrhage
primary subarachnoid hemorrhage caused by intracranial ruputured aneurysm
Machine Leaning Models
Area Under the Curve (ROC): Measures the overall performance of the model across all classification thresholds. A higher AUC value indicates better performance.
Accuracy: The proportion of correctly predicted outcomes (both positive and negative) out of all predictions made.
Precision (Positive Predictive Value, PPV): The proportion of correctly predicted positive outcomes out of all predicted positive outcomes.
Sensitivity (True Positive Rate, TPR): The proportion of actual positive outcomes that are correctly identified by the model.
Specificity (True Negative Rate, TNR): The proportion of actual negative outcomes that are correctly identified by the model.
Interventions
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Machine Leaning Models
Area Under the Curve (ROC): Measures the overall performance of the model across all classification thresholds. A higher AUC value indicates better performance.
Accuracy: The proportion of correctly predicted outcomes (both positive and negative) out of all predictions made.
Precision (Positive Predictive Value, PPV): The proportion of correctly predicted positive outcomes out of all predicted positive outcomes.
Sensitivity (True Positive Rate, TPR): The proportion of actual positive outcomes that are correctly identified by the model.
Specificity (True Negative Rate, TNR): The proportion of actual negative outcomes that are correctly identified by the model.
Eligibility Criteria
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Inclusion Criteria
* Cerebral angiography (CTA) and digital subtraction angiography (DSA) examination confirming intracranial aneurysm rupture as the cause of the subarachnoid hemorrhage;
* Blood routine, biochemical function, blood coagulation function, and craniocerebral CT performed within 24 hours of symptom onset;
* Underwent aneurysm clipping by surgery or endovascular embolization within 72 hours after-onset.
Exclusion Criteria
* Incomplete image data or blood test information;
* Long-term use of anticoagulant medications such as aspirin or warfarin;
* Admitted to hospital with active infectious diseases;
* long-term anticoagulant drugs such as aspirin, wave dimensions;
* Presence of other intracranial vascular malformations.
18 Years
ALL
No
Sponsors
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Panzhihua Central Hospital
OTHER
Zhujiang Hospital
OTHER
Southwest Hospital, China
OTHER
Renmin Hospital of Wuhan University
OTHER
Second Affiliated Hospital of Nanchang University
OTHER
Responsible Party
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Principal Investigators
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Xingen Zhu, Prof
Role: STUDY_CHAIR
Second Affiliated Hospital of Nanchang University
Qianxue Chen, Prof
Role: PRINCIPAL_INVESTIGATOR
Renmin Hospital of Wuhan University
Locations
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The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, China
Countries
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Central Contacts
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Facility Contacts
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References
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Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M. Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment. Ther Clin Risk Manag. 2025 Mar 7;21:293-307. doi: 10.2147/TCRM.S504745. eCollection 2025.
Shu L, Xiao B, Jiang Y, Tang S, Yan T, Wu Y, Wu M, Lv S, Lai X, Zhu X, Hu P, Ye M. Comparison of LVIS and Enterprise stent-assisted coiling embolization for ruptured intracranial aneurysms: a propensity score-matched cohort study. Neurosurg Rev. 2024 Sep 7;47(1):560. doi: 10.1007/s10143-024-02756-8.
Other Identifiers
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IIT-O-2023-011
Identifier Type: -
Identifier Source: org_study_id
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