德国主动脉瓣评分在巴西中心外科主动脉瓣置换风险评估中的应用

数据集获取（Figshare）

• 源站标题：German Aortic Valve Score in Risk Assessment for Surgical Aortic Valve Replacement in a Brazilian Center
• DOI：10.6084/m9.figshare.14281613.v1
• 发布日期：2021-03-24
• 文件数：5 个
• 文件总体积：694.41 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/German_Aortic_Valve_Score_in_Risk_Assessment_for_Surgical_Aortic_Valve_Replacement_in_a_Brazilian_Center/14281613

命令行下载

curl -L -o "0102-7638-rbccv-35-02-0141-gf01.jpg" "https://ndownloader.figshare.com/files/27116423"
curl -L -o "0102-7638-rbccv-35-02-0141-gf02.jpg" "https://ndownloader.figshare.com/files/27116432"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/27116438"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/27116441"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/27116447"

文件清单

• 0102-7638-rbccv-35-02-0141-gf01.jpg（290.22 KB）
• 0102-7638-rbccv-35-02-0141-gf02.jpg（387.30 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）

文件总体积 694.41 KB（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

Abstract Objective: To test the German Aortic Valve (GAV) score at our university hospital in patients undergoing isolated aortic valve replacement (AVR). Methods: A total of 224 patients who underwent isolated conventional AVR between January 2015 and December 2018 were included. Patients with concomitant procedures and transcatheter aortic valve implantation were excluded. Patients’ data were collected and analyzed retrospectively. Patients’ risk scores were calculated according to criteria described by GAV score. Sensitivity, specificity, and accuracy (area under the ROC curve [AUC]) were also calculated. The calibration of the model was tested by the Hosmer-Lemeshow method. Results: The mortality rate was 8.04% (18 patients). The patients’ mean age was 58.2±19.3 years and 25% of them were female (56 patients). Mean GAV score was 1.73±5.86 (min: 0.0; max: 3.53). The GAV score showed excellent discriminative capacity (AUC 0.925, 95% confidence interval 0.882-0.956; P<0.001). The cutoff “1.8” turned out to be the best discriminatory point with the best combination of sensitivity (88.9%) and specificity (75.7%) to predict operative death. Hosmer-Lemeshow method revealed a P-value of 0.687, confirming a good calibration of the model. Conclusion: The GAV score applies to our population with high predictive accuracy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.14281613.v1（source: https://scielo.figshare.com/articles/dataset/German_Aortic_Valve_Score_in_Risk_Assessment_for_Surgical_Aortic_Valve_Replacement_in_a_Brazilian_Center/14281613）。