心力衰竭诊断在管理数据库中的有效性：系统综述和荟萃分析

数据集获取（Figshare）

• 源站标题：Validity of Heart Failure Diagnoses in Administrative Databases: A Systematic Review and Meta-Analysis
• DOI：10.1371/journal.pone.0104519
• 发布日期：2014-08-15
• 文件数：6 个
• 文件总体积：276.88 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_Validity_of_Heart_Failure_Diagnoses_in_Administrative_Databases_A_Systematic_Review_and_Meta_Analysis_/1140758

命令行下载

curl -L -o "Table_S1.doc" "https://ndownloader.figshare.com/files/1637889"
curl -L -o "Text_S1.docx" "https://ndownloader.figshare.com/files/1637890"
curl -L -o "Text_S2.docx" "https://ndownloader.figshare.com/files/1637891"
curl -L -o "Text_S3.doc" "https://ndownloader.figshare.com/files/1637892"
curl -L -o "Checklist_S1.doc" "https://ndownloader.figshare.com/files/1637893"
curl -L -o "Checklist_S2.docx" "https://ndownloader.figshare.com/files/1637894"

文件清单

• Table_S1.doc（80.90 KB）
• Text_S1.docx（14.66 KB）
• Text_S2.docx（15.40 KB）
• Text_S3.doc（75.78 KB）
• Checklist_S1.doc（67.07 KB）
• Checklist_S2.docx（23.07 KB）

文件总体积 276.88 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

Objective
Heart failure (HF) is an important covariate and outcome in studies of elderly populations and cardiovascular disease cohorts, among others. Administrative data is increasingly being used for long-term clinical research in these populations. We aimed to conduct the first systematic review and meta-analysis of studies reporting on the validity of diagnostic codes for identifying HF in administrative data.
Methods
MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify HF; or (b) Evaluating the validity of HF codes in administrative data; and © Reporting validation statistics (sensitivity, specificity, positive predictive value [PPV], negative predictive value, or Kappa scores) for HF, or data sufficient for their calculation. Additional articles were located by hand search (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. Using a random-effects model, pooled sensitivity and specificity values were produced, along with estimates of the positive (LR+) and negative (LR−) likelihood ratios, and diagnostic odds ratios (DOR = LR+/LR−) of HF codes.
Results
Nineteen studies published from1999–2009 were included in the qualitative review. Specificity was ≥95% in all studies and PPV was ≥87% in the majority, but sensitivity was lower (≥69% in ≥50% of studies). In a meta-analysis of the 11 studies reporting sensitivity and specificity values, the pooled sensitivity was 75.3% (95% CI: 74.7–75.9) and specificity was 96.8% (95% CI: 96.8–96.9). The pooled LR+ was 51.9 (20.5–131.6), the LR− was 0.27 (0.20–0.37), and the DOR was 186.5 (96.8–359.2).
Conclusions
While most HF diagnoses in administrative databases do correspond to true HF cases, about one-quarter of HF cases are not captured. The use of broader search parameters, along with laboratory and prescription medication data, may help identify more cases.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0104519（source: https://plos.figshare.com/articles/dataset/_Validity_of_Heart_Failure_Diagnoses_in_Administrative_Databases_A_Systematic_Review_and_Meta_Analysis_/1140758）。