Multi-Center Registry Cohort Study on Prognostic Factors and Prediction Model Construction in Aneurysmal SAH
Multi-Center Registry Cohort Study on Prognostic Factors and Prediction Model Construction in Aneurysmal Subarachnoid Hemorrhage
Second Affiliated Hospital of Nanchang University
5,000 participants
Oct 1, 2018
OBSERVATIONAL
Conditions
Summary
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.
Eligibility
Inclusion Criteria4
- 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 Criteria6
- 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.
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Interventions
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.
Locations(1)
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NCT05738083