Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm

NCT06796283 | Completed

• 1 other identifier: 2021efyB03
• Study Type: observational
• Target: 2,628
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this observational study is to construct a recurrence risk prediction model for ischemic stroke within 1, 3, 6, and 12 months using XGBoost combined with Convolutional Neural Network (CNN) algorithm. Method: Follow up was conducted on the study subjects at 1, 3, 6, and 12 months after discharge. Follow up primary outcome: Whether the study subjects experienced recurrent stroke events. Secondary outcome: Improved Rinkin score. Collect information on research subjects: It includes demographic data, physical examination, medical history, imaging images, medication use, scale scores, CYP2C19 genotype test results, laboratory tests, and other complex multidimensional data.

Conditions

Ischemic Stroke

Outcome Measures

Primary Outcomes (1)

• Recurrent ischemic stroke,

  Follow up was conducted on the study subjects at 1, 3, 6, and 12 months after discharge, with the main outcome being whether the study subjects experienced stroke recurrence events

  April 6th, 2021- March 12th, 2024

Secondary Outcomes (1)

• modified Rankin score

  April 6th, 2021- March 12th, 2024

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Ischemic stroke patients treated in hospitals who meet the inclusion and exclusion criteria for the project

You may qualify if:

• Aged between 18-85 years
• Diagnosed with ischemic stroke or transient ischemic attack (diagnosis in accordance with the standards set by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association in 2014)
• Within the acute phase of the illness (within 2 weeks)
• Voluntarily participates and signs an informed consent form

You may not qualify if:

• Patients with cancer
• Patients with cardiogenic infarction, other causes of cerebral infarction, or cryptogenic cerebral infarction
• Patients with hemorrhagic stroke, mixed stroke, and tumor stroke
• Patients with severe heart, lung, or liver system diseases
• Patients judged by the researcher to have poor compliance and unable to complete long-term follow-up
• Patients currently participating in any clinical trials related to investigational drugs or medical devices

Sponsors & Collaborators

• Second Affiliated Hospital of Nanchang University: lead

Study Sites (1)

The Second Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, 330008, China

Location

Related Publications (1)

• Liu J, Li J, Wu Y, Luo H, Yu P, Cheng R, Wang X, Xian H, Wu B, Chen Y, Ke J, Yi Y. Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study. Neuroscience. 2025 Jan 26;565:222-231. doi: 10.1016/j.neuroscience.2024.12.002. Epub 2024 Dec 2.

  PMID: 39631660 RESULT

Related Links

• Explore deep - learning - based infarct lesion segmentation in AIS patients' brain MRI, radiomics - based 1 - year recurrence prediction, and develop a combined model for accurate AIS recurrence prediction

MeSH Terms

Conditions

Ischemic Stroke

Condition Hierarchy (Ancestors)

Stroke; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• yingping Y Yi

  Second Affiliated Hospital of Nanchang University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 13, 2025
• First Posted: January 28, 2025
• Study Start: April 26, 2021
• Primary Completion: March 4, 2023
• Study Completion: December 31, 2024
• Last Updated: February 21, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)