卒中相关性肺炎新型预测因子：单中心分析数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis.PDF
• DOI：10.3389/fneur.2022.857420.s001
• 发布日期：2022-03-30
• 文件数：1 个
• 文件总体积：139.72 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Novel_Predictors_of_Stroke-Associated_Pneumonia_A_Single_Center_Analysis_PDF/19465025

命令行下载

curl -L -o "Data_Sheet_1_Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis.PDF" "https://ndownloader.figshare.com/files/34583201"

文件清单

• Data_Sheet_1_Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis.PDF（139.72 KB）

文件总体积 139.72 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

Stroke-associated pneumonia (SAP) is a common cause of disability or death. Although the researches on SAP have been relatively mature, the method that can predict SAP with great accuracy has not yet been determined. It is necessary to discover new predictors to construct a more accurate predictive model for SAP. We continuously collected 2,366 patients with acute ischemic stroke, and then divided them into the SAP group and non-SAP group. Data were recorded at admission. Through univariate analyses and multivariate regression analyses of the data, the new predictive factors and the predictive model of SAP were determined. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were used to measure their predictive accuracy. Of the 2,366 patients, 459 were diagnosed with SAP. International normalized ratio (INR) (odds ratio = 37.981; 95% confidence interval, 7.487–192.665; P < 0.001), age and dysphagia were independent risk factors of SAP. However, walking ability within 48 h of admission (WA) (odds ratio = 0.395; 95% confidence interval, 0.287–0.543; P < 0.001) was a protective factor of SAP. Different predictors and the predictive model all could predict SAP (P < 0.001). The predictive power of the model (AUC: 0.851) which included age, homocysteine, INR, history of chronic obstructive pulmonary disease (COPD), dysphagia, and WA was greater than that of age (AUC: 0.738) and INR (AUC: 0.685). Finally, we found that a higher INR and no WA could predict SAP in patients with acute ischemic stroke. In addition, we designed a simple and practical predictive model for SAP, which showed relatively good accuracy. These findings might help identify high-risk patients with SAP and provide a reference for the timely use of preventive antibiotics.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.857420.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Novel_Predictors_of_Stroke-Associated_Pneumonia_A_Single_Center_Analysis_PDF/19465025）。