NCT05738083

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 5,000 (estimated)
• Sponsor: Second Affiliated Hospital of Nanchang University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

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

• Aneurysmal Subarachnoid Hemorrhage

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	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.

Timeline

• Start date: 2018-10-01
• Primary completion: 2028-12-30
• Completion: 2029-12-30
• First posted: 2023-02-21
• Last updated: 2024-08-22

Locations

1 site across 1 country: China