Prognostic Prediction Model of Patients With AcUte Stroke undeRgoing EndOvascular TheRApy (AURORA)

Recruiting now

Conditions studied: Acute Ischemic Stroke

In brief

Stroke is the leading cause of disability-adjusted life years (DALYs) in China, imposing a heavy burden on society and families. Endovascular therapy (EVT) has opened the 2.0 era of acute ischemic stroke (AIS) treatment, but still up to 1/3 of patients have poor neurological prognosis. The results of several studies at home and abroad and by our team indicate that anesthesia method and perioperative management are one of the key factors affecting the neurological prognosis of EVT treatment in AIS patients. Based on machine learning big data analysis methods, a prognostic model for EVT treatment of AIS patients can be established to guide individualized treatment decisions. Current prediction models only include patients' baseline variables, and lack the inclusion of intraoperative (anesthesia management and interventional process) and postoperative (intensive monitoring treatment) variables, which limits the clinical application of prediction tools. We will establish a large prospective cohort database including preoperative, intraoperative, and postoperative variables, integrate heterogeneous information from multiple sources based on artificial intelligence machine learning algorithms, and build prognostic prediction models with better clinical applicability and calibration, with the aim of optimizing perioperative management of endovascular therapy, guiding individualized clinical decision-making, and improving patients' clinical prognosis.

Key facts

Study ID
NCT06009315
Run by
Beijing Tiantan Hospital
People needed
949
Starts
2024-02-23
Expected to finish
2026-12-31
Last updated by the study team
2024-08-01

Who can join

Age: 18 and older, up to 100. Sex: any. Healthy volunteers: not accepted.

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Where it is running

Full record on ClinicalTrials.gov

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