基于德国国家行政索赔数据的严重脓毒症或脓毒性休克患者住院死亡率风险模型

基于德国行政索赔数据的脓毒症住院死亡率风险预测模型数据集,包含超10万病例的表格数据。

Figshare
2018-03-21 更新
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表格2013 deviation sample2015 hospital mortalityGerman claims dataAUCGerman hospitalsclaims data Background Sepsis2015 validation samplemodelrisk-adjusted sepsis mortality
A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

基本信息

模态
表格
大小
0.000132713 GB
许可
CC BY 4.0
创建/更新时间
2018-03-21
版本
v1

资源简介

基于德国国家行政索赔数据的严重脓毒症或脓毒性休克患者住院死亡率风险模型

【数据集背景】

本数据集源站标题为《A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data》。配套论文《A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data》发表于 PLOS ONE(2018),DOI:10.1371/journal.pone.0194371。源站首次发布:2018-03-20。

【数据内容】

源站原始描述:Background Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals. Methods We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The mode…

【数据结构与技术规格】

文件数 4 个,合计 132.71 KB。S1 Appendix.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,17.78 KB。S1 Fig.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,62.86 KB。S1 Table.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,26.24 KB。数据集 DOI:10.1371/journal.pone.0194371。

【主题与分类】

源站学科分类:Medicine、Biotechnology、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified、Developmental Biology、Marine Biology。主题标签:2013 deviation sample、2015 hospital mortality、German claims data、AUC、German hospitals、claims data Background Sepsis、2015 validation sample、model、risk-adjusted sepsis mortality。

【适用方向】

结构化表格数据可直接用于统计建模、特征工程与队列分析。

【获取与许可】

源站页面:https://plos.figshare.com/articles/dataset/A_risk-model_for_hospital_mortality_among_patients_with_severe_sepsis_or_septic_shock_based_on_German_national_administrative_claims_data/6006290

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Schwarzkopf, Daniel; Fleischmann-Struzek, Carolin; Rüddel, Hendrik; Reinhart, Konrad; Thomas-Rüddel, Daniel O. (2018). A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data. PLOS ONE. Dataset

【使用注意】

· 源站最后修改于 2023-05-30,引用时请注意版本时效。

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下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

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当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取(Figshare)

命令行下载

curl -L -o "S1 Appendix.docx" "https://ndownloader.figshare.com/files/10803929"
curl -L -o "S1 Fig.docx" "https://ndownloader.figshare.com/files/10803932"
curl -L -o "S1 Table.docx" "https://ndownloader.figshare.com/files/10803941"
curl -L -o "S2 Table.docx" "https://ndownloader.figshare.com/files/10803944"

文件清单

  • S1 Appendix.docx(17.78 KB)
  • S1 Fig.docx(62.86 KB)
  • S1 Table.docx(26.24 KB)
  • S2 Table.docx(25.83 KB)

文件总体积 132.71 KB(源站 API 实测)。

数据说明

Background
Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals.
Methods
We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010–2015 was analyzed.
Results
The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant.
Conclusions
The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0194371(source: https://plos.figshare.com/articles/dataset/A_risk-model_for_hospital_mortality_among_patients_with_severe_sepsis_or_septic_shock_based_on_German_national_administrative_claims_data/6006290)。

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