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

Results pending

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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Recruitment Status

RECRUITING

Total Enrollment

5000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-10-01

Study Completion Date

2029-12-30

Brief Summary

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PROSAH-MPC, a collaborative research project among neurosurgical centers in China, focuses on aneurysmal subarachnoid hemorrhage (aSAH). Its aim is to identify prognostic factors and develop robust prediction models for complications, disability, and mortality in aSAH patients. By leveraging a large, multi-center, prospective cohort design, PROSAH-MPC aims to overcome limitations of past studies and provide a more comprehensive understanding of the disease.

Detailed Description

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PROSAH-MPC (Prognostic Factors and Prediction Models in Aneurysmal Subarachnoid Hemorrhage Multi-Center Prospective Cohort) is an ambitious research endeavor that brings together a consortium of neurosurgical centers across various regions to comprehensively investigate the complexities of aneurysmal subarachnoid hemorrhage (aSAH). This multi-faceted study aims to unlock the prognostic factors that underpin the outcomes of patients afflicted with this severe and often life-threatening cerebrovascular disorder.

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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Aneurysmal Subarachnoid Hemorrhage

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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aneurysmal subarachnoid hemorrhage

primary subarachnoid hemorrhage caused by intracranial ruputured aneurysm

Machine Leaning Models

Intervention Type DIAGNOSTIC_TEST

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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Subarachnoid hemorrhage confirmed by computed tomography (CT);
* 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

* Aneurysm rupture bleeding time exceeding 24 hours before hospital admission;
* 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Panzhihua Central Hospital

OTHER

Sponsor Role collaborator

Zhujiang Hospital

OTHER

Sponsor Role collaborator

Southwest Hospital, China

OTHER

Sponsor Role collaborator

Renmin Hospital of Wuhan University

OTHER

Sponsor Role collaborator

Second Affiliated Hospital of Nanchang University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

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

Site Status RECRUITING

Countries

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China

Central Contacts

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Xingen Zhu, Prof

Role: CONTACT

13803546020

Ping Hu, MD

Role: CONTACT

13207109734

Facility Contacts

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Xingen Zhu, Prof

Role: primary

13803546020

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.

Reference Type DERIVED
PMID: 40071129 (View on PubMed)

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.

Reference Type DERIVED
PMID: 39242449 (View on PubMed)

Other Identifiers

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IIT-O-2023-011

Identifier Type: -

Identifier Source: org_study_id

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