预测ICU患者医疗相关性感染的交互式列线图：一项中国贵州省多中心研究

数据集获取（Figshare）

• 源站标题：An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China
• DOI：10.1371/journal.pone.0219456
• 发布日期：2019-07-15
• 文件数：5 个
• 文件总体积：1.76 MB
• 源站页面：https://plos.figshare.com/articles/dataset/An_interactive_nomogram_to_predict_healthcare-associated_infections_in_ICU_patients_A_multicenter_study_in_GuiZhou_Province_China/8871662

命令行下载

curl -L -o "pone.0219456.s001.pdf" "https://ndownloader.figshare.com/files/16265315"
curl -L -o "pone.0219456.s002.tif" "https://ndownloader.figshare.com/files/16265318"
curl -L -o "pone.0219456.s003.pdf" "https://ndownloader.figshare.com/files/16265324"
curl -L -o "pone.0219456.s004.csv" "https://ndownloader.figshare.com/files/16265327"
curl -L -o "pone.0219456.s005.xlsx" "https://ndownloader.figshare.com/files/16265330"

文件清单

• pone.0219456.s001.pdf（296.19 KB）
• pone.0219456.s002.tif（767.99 KB）
• pone.0219456.s003.pdf（240.04 KB）
• pone.0219456.s004.csv（362.85 KB）
• pone.0219456.s005.xlsx（94.15 KB）

文件总体积 1.76 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

Objective
To develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).
Methods
A multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients’ demographic characteristics, basic clinical features from the previous admission, and their need for bacterial culture during the current admission were extracted from electronic medical records of the hospitals to predict HCAI. Univariate and multivariable analyses were used to identify independent risk factors of HCAI in the training set. The multivariable model’s performance was evaluated in both the training set and the validation set, and an interactive nomogram was constructed according to multivariable regression model. Moreover, the interactive nomogram was used to predict the possibility of a patient developing an HCAI based on their prior admission data. Finally, the clinical usefulness of the interactive nomogram was estimated by decision analysis using the entire dataset.
Results
The nomogram model included factor development (local economic development levels), length of stay (LOS; days of hospital stay), fever (days of persistent fever), diabetes (history of diabetes), cancer (history of cancer) and culture (the need for bacterial culture). The model showed good calibration and discrimination in the training set [area under the curve (AUC), 0.871; 95% confidence interval (CI), 0.848–0.894] and in the validation set (AUC, 0.862; 95% CI, 0.829–0.895). The decision curve demonstrated the clinical usefulness of our interactive nomogram.
Conclusions
The developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0219456（source: https://plos.figshare.com/articles/dataset/An_interactive_nomogram_to_predict_healthcare-associated_infections_in_ICU_patients_A_multicenter_study_in_GuiZhou_Province_China/8871662）。