表1_初诊特发性扩张型心肌病左心室逆重构的早期预测：线性模型、随机森林与极端梯度提升比较

数据集获取（Figshare）

• 源站标题：Table_1_Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting.DOCX
• DOI：10.3389/fcvm.2021.684004.s001
• 发布日期：2021-08-04
• 文件数：1 个
• 文件总体积：2.58 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Early_Prediction_of_Left_Ventricular_Reverse_Remodeling_in_First-Diagnosed_Idiopathic_Dilated_Cardiomyopathy_A_Comparison_of_Linear_Model_Random_Forest_and_Extreme_Gradient_Boosting_DOCX/15103857

命令行下载

curl -L -o "Table_1_Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Ra.DOCX" "https://ndownloader.figshare.com/files/29037456"

文件清单

• Table_1_Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Ra.DOCX（2.58 MB）

文件总体积 2.58 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

Introduction: Left ventricular reverse remodeling (LVRR) is associated with decreased cardiovascular mortality and improved cardiac survival and also crucial for therapeutic options. However, there is a lack of an early prediction model of LVRR in first-diagnosed dilated cardiomyopathy.
Methods: This single-center study included 104 patients with idiopathic DCM. We defined LVRR as an absolute increase in left ventricular ejection fraction (LVEF) from >10% to a final value >35% and a decrease in left ventricular end-diastolic diameter (LVDd) >10%. Analysis features included demographic characteristics, comorbidities, physical sign, biochemistry data, echocardiography, electrocardiogram, Holter monitoring, and medication. Logistic regression, random forests, and extreme gradient boosting (XGBoost) were, respectively, implemented in a 10-fold cross-validated model to discriminate LVRR and non-LVRR, with receiver operating characteristic (ROC) curves and calibration plot for performance evaluation.
Results: LVRR occurred in 47 (45.2%) patients after optimal medical treatment. Cystatin C, right ventricular end-diastolic dimension, high-density lipoprotein cholesterol (HDL-C), left atrial dimension, left ventricular posterior wall dimension, systolic blood pressure, severe mitral regurgitation, eGFR, and NYHA classification were included in XGBoost, which reached higher AU-ROC compared with logistic regression (AU-ROC, 0.8205 vs. 0.5909, p = 0.0119). Ablation analysis revealed that cystatin C, right ventricular end-diastolic dimension, and HDL-C made the largest contributions to the model.
Conclusion: Tree-based models like XGBoost were able to early differentiate LVRR and non-LVRR in patients with first-diagnosed DCM before drug therapy, facilitating disease management and invasive therapy selection. A multicenter prospective study is necessary for further validation.
Clinical Trial Registration:http://www.chictr.org.cn/usercenter.aspx (ChiCTR2000034128).

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2021.684004.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Early_Prediction_of_Left_Ventricular_Reverse_Remodeling_in_First-Diagnosed_Idiopathic_Dilated_Cardiomyopathy_A_Comparison_of_Linear_Model_Random_Forest_and_Extreme_Gradient_Boosting_DOCX/15103857）。