冠状动脉搭桥术后ICU住院时间预测因素：风险评分系统的开发

数据集获取（Figshare）

• 源站标题：Predictors of Length of Stay in Intensive Care Unit After Coronary Artery Bypass Grafting: Development a Risk Scoring System
• DOI：10.6084/m9.figshare.14281922.v1
• 发布日期：2021-03-24
• 文件数：6 个
• 文件总体积：285.23 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Predictors_of_Length_of_Stay_in_Intensive_Care_Unit_After_Coronary_Artery_Bypass_Grafting_Development_a_Risk_Scoring_System/14281922

命令行下载

curl -L -o "0102-7638-rbccv-2019-0405-gf01.jpg" "https://ndownloader.figshare.com/files/27118655"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/27118661"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/27118664"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/27118667"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/27118670"
curl -L -o "Table_5.xls" "https://ndownloader.figshare.com/files/27118673"

文件清单

• 0102-7638-rbccv-2019-0405-gf01.jpg（248.88 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（13.82 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）
• Table_5.xls（5.63 KB）

文件总体积 285.23 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 Introduction: To determine predictors of length of stay (LOS) in the intensive care unit (ICU) after coronary artery bypass grafting (CABG) and to develop a risk scoring system were the objectives of this study. Methods: In this retrospective study, 1202 patients’’ medical records after CABG were evaluated by a research-made checklist. Tarone-Ware test was used to determine the predictors of patients’’ LOS in the ICU. Cox regression model was used to determine the risk factors and risk ratios associated with ICU LOS. Results: The mean ICU LOS after CABG was 55.27±17.33 hours. Cox regression model showed that having more than two chest tubes (95% confidence interval [CI] 1.005-1.287, Relative Risk [RR]=1.138), occurrence of atelectasis (95% CI 1.000-3.007, RR=1.734), and occurrence of atrial fibrillation after CABG (95% CI 1.428-2.424, RR=1.861) were risk factors associated with longer ICU LOS. The discrimination power of this set of predictors was demonstrated with an area under the receiver operating characteristic curve and it was 0.69. A simple risk scoring system was developed based on three identified predictors that can raise ICU LOS. Conclusion: The simple risk scoring system developed based on three identified predictors can help to plan more accurately a patient’'s LOS in hospital for CABG and can be useful in managing human and financial resources.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.14281922.v1（source: https://scielo.figshare.com/articles/dataset/Predictors_of_Length_of_Stay_in_Intensive_Care_Unit_After_Coronary_Artery_Bypass_Grafting_Development_a_Risk_Scoring_System/14281922）。