疾病严重程度调整对医院标准化死亡率比率的影响——基于缺血性中风入院数据

数据集获取（Figshare）

• 源站标题：The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data
• DOI：10.1371/journal.pone.0216325
• 发布日期：2019-05-21
• 文件数：5 个
• 文件总体积：327.39 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_impact_of_disease_severity_adjustment_on_hospital_standardised_mortality_ratios_Results_from_a_service-wide_analysis_of_ischaemic_stroke_admissions_using_linked_pre-hospital_admissions_and_mortality_data/8160491

命令行下载

curl -L -o "pone.0216325.s001.docx" "https://ndownloader.figshare.com/files/15205121"
curl -L -o "pone.0216325.s002.docx" "https://ndownloader.figshare.com/files/15205124"
curl -L -o "pone.0216325.s003.pdf" "https://ndownloader.figshare.com/files/15205127"
curl -L -o "pone.0216325.s004.pdf" "https://ndownloader.figshare.com/files/15205133"
curl -L -o "pone.0216325.s005.docx" "https://ndownloader.figshare.com/files/15205136"

文件清单

• pone.0216325.s001.docx（48.64 KB）
• pone.0216325.s002.docx（30.07 KB）
• pone.0216325.s003.pdf（66.54 KB）
• pone.0216325.s004.pdf（144.92 KB）
• pone.0216325.s005.docx（37.23 KB）

文件总体积 327.39 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
Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment.
Methods
We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates.
Results
The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7).
Conclusions
Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0216325（source: https://plos.figshare.com/articles/dataset/The_impact_of_disease_severity_adjustment_on_hospital_standardised_mortality_ratios_Results_from_a_service-wide_analysis_of_ischaemic_stroke_admissions_using_linked_pre-hospital_admissions_and_mortality_data/8160491）。