手术部位感染风险评分（SSIRS）

数据集获取（Figshare）

• 源站标题：The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
• DOI：10.1371/journal.pone.0067167
• 发布日期：2013-06-27
• 文件数：9 个
• 文件总体积：1.51 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_The_Surgical_Site_Infection_Risk_Score_SSIRS_A_Model_to_Predict_the_Risk_of_Surgical_Site_Infections_/733242

命令行下载

curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/1103295"
curl -L -o "Table_S1.doc" "https://ndownloader.figshare.com/files/1103299"
curl -L -o "Table_S2.doc" "https://ndownloader.figshare.com/files/1103303"
curl -L -o "Table_S3.doc" "https://ndownloader.figshare.com/files/1103306"
curl -L -o "Table_S4.doc" "https://ndownloader.figshare.com/files/1103311"
curl -L -o "Appendix_S1.doc" "https://ndownloader.figshare.com/files/1103317"
curl -L -o "Appendix_S2.doc" "https://ndownloader.figshare.com/files/1103319"
curl -L -o "Appendix_S3.doc" "https://ndownloader.figshare.com/files/1103325"

文件清单

• Figure_S1.tif（669.38 KB）
• Table_S1.doc（75.26 KB）
• Table_S2.doc（27.65 KB）
• Table_S3.doc（74.24 KB）
• Table_S4.doc（84.99 KB）
• Appendix_S1.doc（25.60 KB）
• Appendix_S2.doc（121.34 KB）
• Appendix_S3.doc（63.49 KB）
• Appendix_S4.doc（371.20 KB）

文件总体积 1.51 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

Background
Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI.
Methods
We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 into a derivation and validation population. We used multivariate logistic regression to determine the independent association of patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days of surgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries having a common first 3 numbers of their CPT code.
Results
Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medical history, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors (smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroid use, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infected wounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, the SSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795–0.805) and calibration.
Conclusion
SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0067167（source: https://plos.figshare.com/articles/dataset/_The_Surgical_Site_Infection_Risk_Score_SSIRS_A_Model_to_Predict_the_Risk_of_Surgical_Site_Infections_/733242）。