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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.
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.
Inclusion Criteria:
Exclusion criteria:
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| modified Rankin score | Follow up was conducted on the study subjects at 1, 3, 6, and 12 months after discharge, with the secondary outcome being the modified Rinkin score.Improved Rinkin Scale: Completely asymptomatic (0 points). Despite symptoms, there is no obvious functional impairment and the ability to complete all daily work and life tasks (1 point). Mild disability, unable to complete all pre illness activities, but able to take care of daily affairs without assistance (2 points). Moderate disability, requiring partial assistance, but able to walk independently (3 points). Moderate to severe disability, unable to walk independently, requiring assistance from others in daily life (4 points). Severe disability, bedridden, urinary incontinence, and complete dependence on others in daily life (5 points). The higher the score, the worse the participant's neurological function recovery. |
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Inclusion Criteria:
Exclusion Criteria:
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Ischemic stroke patients treated in hospitals who meet the inclusion and exclusion criteria for the project
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| Name | Affiliation | Role |
|---|---|---|
| yingping Y Yi | Second Affiliated Hospital of Nanchang University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Second Affiliated Hospital of Nanchang University | Nanchang | Jiangxi | 330008 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39631660 | Result | 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. |
| Label | URL |
|---|---|
| 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 | View source |
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| Type | Date | Date Unknown |
|---|---|---|
| Release | Mar 19, 2025 | |
| Reset | Apr 8, 2025 | |
| Release | Apr 21, 2025 | |
| Reset | May 6, 2025 |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP_ICF | Yes | Yes | Yes | Study Protocol, Statistical Analysis Plan, and Informed Consent Form | Apr 29, 2021 | Jan 13, 2025 | Prot_SAP_ICF_000.pdf |
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| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Mar 19, 2025 | Apr 8, 2025 | |||
| Apr 21, 2025 |
| ID | Term |
|---|---|
| D000083242 | Ischemic Stroke |
| ID | Term |
|---|---|
| D020521 | Stroke |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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| April 6th, 2021- March 12th, 2024 |
| May 6, 2025 |
| D009422 |
| Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |