每季度住院患者特征数据

数据集获取（Figshare）

• 源站标题：Inpatient stays characteristics per quarter.
• DOI：10.1371/journal.pone.0303543.t001
• 发布日期：2024-05-15
• 文件数：1 个
• 文件总体积：17.92 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Inpatient_stays_characteristics_per_quarter_/25830296

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/46353017"

文件清单

• Table 1.xls（17.92 KB）

文件总体积 17.92 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

Background
Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts.
Methods
A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen’s Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference.
Results
Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89.
Conclusion
Seasonal and secular trends can be controlled as potential confounders to imp

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0303543.t001（source: https://plos.figshare.com/articles/dataset/Inpatient_stays_characteristics_per_quarter_/25830296）。