行政数据库中心肌梗死诊断有效性的系统综述

数据集获取（Figshare）

• 源站标题：Validity of Myocardial Infarction Diagnoses in Administrative Databases: A Systematic Review
• DOI：10.1371/journal.pone.0092286
• 发布日期：2014-03-28
• 文件数：5 个
• 文件总体积：204.08 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Validity_of_Myocardial_Infarction_Diagnoses_in_Administrative_Databases_A_Systematic_Review_/978187

命令行下载

curl -L -o "Table_S1.docx" "https://ndownloader.figshare.com/files/1439989"
curl -L -o "Text_S1.docx" "https://ndownloader.figshare.com/files/1439990"
curl -L -o "Text_S2.docx" "https://ndownloader.figshare.com/files/1439991"
curl -L -o "Text_S3.doc" "https://ndownloader.figshare.com/files/1439992"
curl -L -o "Checklist_S1.doc" "https://ndownloader.figshare.com/files/1439994"

文件清单

• Table_S1.docx（30.14 KB）
• Text_S1.docx（14.66 KB）
• Text_S2.docx（15.40 KB）
• Text_S3.doc（75.78 KB）
• Checklist_S1.doc（68.10 KB）

文件总体积 204.08 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
Though administrative databases are increasingly being used for research related to myocardial infarction (MI), the validity of MI diagnoses in these databases has never been synthesized on a large scale.
Objective
To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI in administrative data.
Methods
MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify MI; or (b) Evaluating the validity of MI codes in administrative data; and © Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.
Results
Thirty studies published from 1984–2010 were included; most assessed codes from the International Classification of Diseases (ICD)-9th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [≥50%] studies was ≥86%, and PPV in most studies was ≥93%. The PPV was higher in the more-recent studies, and lower when criteria that do not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI).
Conclusions
Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data if hospitalization data is not available to confirm deaths from MI.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0092286（source: https://plos.figshare.com/articles/dataset/_Validity_of_Myocardial_Infarction_Diagnoses_in_Administrative_Databases_A_Systematic_Review_/978187）。