数据表1：整合超声与临床风险因素预测痛风患者颈动脉斑块易损性——机器学习方法

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach.docx
• DOI：10.3389/fmed.2025.1556387.s006
• 发布日期：2025-06-19
• 文件数：1 个
• 文件总体积：2.26 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Integrating_ultrasound_and_clinical_risk_factors_to_predict_carotid_plaque_vulnerability_in_gout_patients_a_machine_learning_approach_docx/29366285

命令行下载

curl -L -o "Data Sheet 1_Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach.docx" "https://ndownloader.figshare.com/files/55504271"

文件清单

• Data Sheet 1_Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach.docx（2.26 MB）

文件总体积 2.26 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

ObjectivesThis study aimed to identify independent risk factors for carotid plaque (CP) vulnerability in patients with gout and to develop a predictive model incorporating both gout-specific and cardiovascular factors.
MethodThis study was designed as a retrospective cohort analysis that enrolled patients with newly diagnosed gout. These patients were retrospectively followed for a period of 1 to 2 years to evaluate the incidence of CP vulnerability. CP vulnerability was assessed using standardized ultrasound examinations and graded according to the Plaque Reporting and Data System (Plaque-RADS). Multivariate ordinal logistic regression analysis was employed to identify independent risk factors associated with CP vulnerability, with a particular focus on the impact of gout-related variables. Based on these results, a random forest prediction model was developed by integrating ultrasound imaging features and clinical variables to predict CP vulnerability.
ResultsTophi (OR = 1.760, p = 0.009), power Doppler (PD) signal grades (Grade 2: OR = 1.540, p = 0.002; Grade 3: OR = 1.890, p = 0.001), and the number of gout flares in the last year (OR = 1.524, p = 0.001) were identified as independent risk factors for CP vulnerability. The random forest model showed excellent predictive performance (C-index = 0.997) and highlighted tophi, PD signal grades, and gout flare frequency as key gout-specific contributors to CP risk.
ConclusionThe presence of tophi, positive PD signals, and increased number of gout flares are significantly associated with CP vulnerability in patients with gout. The proposed machine learning model, integrating gout-specific and cardiovascular factors, provides a novel and effective approach for personalized risk stratification and management in gout patients, bridging the gap between rheumatic inflammation and cardiovascular risk assessment.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1556387.s006（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Integrating_ultrasound_and_clinical_risk_factors_to_predict_carotid_plaque_vulnerability_in_gout_patients_a_machine_learning_approach_docx/29366285）。