RecruitingNCT05738083

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

Sponsor

Second Affiliated Hospital of Nanchang University

Enrollment

5,000 participants

Start Date

Oct 1, 2018

Study Type

OBSERVATIONAL

Conditions

Aneurysmal Subarachnoid Hemorrhage

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

Min Age: 18 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying Machine Leaning Models for people with aneurysmal subarachnoid hemorrhage. The study is currently recruiting participants at 1 location. People eligible for this study include aged 18 Years and older.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTMachine 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.