Clinical trial NCT06796283: Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
Official title: Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Ischemic Stroke
Lead sponsor: Second Affiliated Hospital of Nanchang University
Sponsor class: OTHER
First posted: 2025-01-28
Enrollment: 2628
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Ischemic stroke patients treated in hospitals who meet the inclusion and exclusion criteria for the project

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.

Detailed description: This project conducts a one-year follow-up on the enrolled subjects (patients with ischemic stroke) to observe the recurrence and modified Rankin scores after discharge. Combining complex multidimensional data such as demographic information, physical examination, medical history, imaging images, medication status, NIHSS scale scores, Glasgow Coma Scale scores, CYP2C19 genotype test results, and laboratory examinations of the subjects; Convolutional Neural Networks (CNNs) are used to segment lesions and extract features from the subjects' imaging images; Cox regression models are employed to obtain factors influencing recurrence; a prediction model for the risk of recurrence within 1, 3, 6, and 12 months for ischemic stroke is constructed using the XGBoost algorithm combined with Convolutional Neural Networks. The exploration aims to provide new insights and methods for the prevention and control of major chronic diseases by evaluating the effectiveness of XGBoost in predicting the risk of ischemic stroke recurrence at different times.

1. Inclusion Criteria：

   * 18-85 years old;
   * Diagnosed with ischemic stroke or transient ischemic attack (diagnosis meets the criteria established by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association in 2014);
   * During the acute phase of onset (2 weeks);
   * Voluntarily participate and sign an informed consent form.
2. Exclusion criteria：

   * Cancer patients;
   * Cardiogenic infarction, cerebral infarction of other causes, and cerebral infarction of unknown causes;
   * Patients with hemorrhagic stroke, mixed stroke, and tumor stroke;
   * Merge with severe heart, lung, and liver system diseases;
   * Researchers identify patients with poor compliance and inability to complete long-term follow-up;
   * Patients currently participating in any clinical trials related to investigational drugs or medical devices.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Recurrent ischemic stroke,
timeFrame: April 6th, 2021- March 12th, 2024
Secondary outcomes:
measure: modified Rankin score
timeFrame: April 6th, 2021- March 12th, 2024
Other outcomes:
Locations:
city: Nanchang
country: China
facility: The Second Affiliated Hospital of Nanchang University
state: Jiangxi
Study officials:
affiliation: Second Affiliated Hospital of Nanchang University
name: yingping Y Yi
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.