德国败血症质量网络数据集
德国败血症质量网络评估研究的补充数据表(PDF),包含败血症死亡率影响分析及实施障碍调查。

基本信息
资源简介
德国败血症质量网络数据集
【一句话概览】
本数据集是 figshare 公开的文本数据集,收录 1 个文件、合计 15.92 KB,主题涵盖 sepsis、mortality、quality improvement、risk adjustment、administrative claims,可用于医学自然语言处理(实体识别、信息抽取等),CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupted Time Series Analysis.pdf》。配套论文《The German Quality Network Sepsis: study protocol for the evaluation of a quality collaborative on decreasing sepsis-related mortality in a quasi-experimental difference-in-differences design》发表于 Implementation Science(2018),DOI:10.1186/s13012-017-0706-5。源站首次发布:2022-04-27。
【数据内容】
源站原始描述:BackgroundSepsis is one of the leading causes of preventable deaths in hospitals. This study presents the evaluation of a quality collaborative, which aimed to decrease sepsis-related hospital mortality. MethodsThe German Quality Network Sepsis (GQNS) offers quality reporting based on claims data, peer reviews, and support for establishing continuous quality management and staff education. This study evaluates the effects of participating in the GQNS during the intervention period (April 2016–Ju…
【数据结构与技术规格】
文件数 1 个,合计 15.92 KB。Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupt.pdf:application/pdf,15.92 KB。数据集 DOI:10.3389/fmed.2022.882340.s003。
【主题与分类】
源站学科分类:Radiology and Organ Imaging、Foetal Development and Medicine、Obstetrics and Gynaecology、Primary Health Care、Medical and Health Sciences not elsewhere classified、Dermatology。主题标签:sepsis、mortality、quality improvement、risk adjustment、administrative claims、interdisciplinary health team、diagnosis-related groups (DRG)。
【适用方向】
文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面:https://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_German_Quality_Network_Sepsis_Evaluation_of_a_Quality_Collaborative_on_Decreasing_Sepsis-Related_Mortality_in_a_Controlled_Interrupted_Time_Series_Analysis_pdf/19663263
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Schwarzkopf, Daniel; Rüddel, Hendrik; Brinkmann, Alexander; Fleischmann-Struzek, Carolin; Friedrich, Marcus E.; Glas, Michael; et al. (2022). Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupted Time…
【使用注意】
· 源站最后修改于 2023-06-01,引用时请注意版本时效。
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。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放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)
- 源站标题:Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupted Time Series Analysis.pdf
- DOI:10.3389/fmed.2022.882340.s003
- 发布日期:2022-04-27
- 文件数:1 个
- 文件总体积:15.92 KB
- 源站页面:https://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_German_Quality_Network_Sepsis_Evaluation_of_a_Quality_Collaborative_on_Decreasing_Sepsis-Related_Mortality_in_a_Controlled_Interrupted_Time_Series_Analysis_pdf/19663263
命令行下载
curl -L -o "Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupt.pdf" "https://ndownloader.figshare.com/files/34920738"
文件清单
Data_Sheet_3_The German Quality Network Sepsis: Evaluation of a Quality Collaborative on Decreasing Sepsis-Related Mortality in a Controlled Interrupt.pdf(15.92 KB)
文件总体积 15.92 KB(源站 API 实测)。
数据说明
BackgroundSepsis is one of the leading causes of preventable deaths in hospitals. This study presents the evaluation of a quality collaborative, which aimed to decrease sepsis-related hospital mortality.
MethodsThe German Quality Network Sepsis (GQNS) offers quality reporting based on claims data, peer reviews, and support for establishing continuous quality management and staff education. This study evaluates the effects of participating in the GQNS during the intervention period (April 2016–June 2018) in comparison to a retrospective baseline (January 2014–March 2016). The primary outcome was all-cause risk-adjusted hospital mortality among cases with sepsis. Sepsis was identified by International Classification of Diseases (ICD) codes in claims data. A controlled time series analysis was conducted to analyze changes from the baseline to the intervention period comparing GQNS hospitals with the population of all German hospitals assessed via the national diagnosis-related groups (DRGs)-statistics. Tests were conducted using piecewise hierarchical models. Implementation processes and barriers were assessed by surveys of local leaders of quality improvement teams.
ResultsSeventy-four hospitals participated, of which 17 were university hospitals and 18 were tertiary care facilities. Observed mortality was 43.5% during baseline period and 42.7% during intervention period. Interrupted time-series analyses did not show effects on course or level of risk-adjusted mortality of cases with sepsis compared to the national DRG-statistics after the beginning of the intervention period (p = 0.632 and p = 0.512, respectively). There was no significant mortality decrease in the subgroups of patients with septic shock or ventilation >24 h or predefined subgroups of hospitals. A standardized survey among 49 local quality improvement leaders in autumn of 2018 revealed that most hospitals did not succeed in implementing a continuous quality management program or relevant measures to improve early recognition and treatment of sepsis. Barriers perceived most commonly were lack of time (77.6%), staff shortage (59.2%), and lack of participation of relevant departments (38.8%).
Conclu
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fmed.2022.882340.s003(source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_3_The_German_Quality_Network_Sepsis_Evaluation_of_a_Quality_Collaborative_on_Decreasing_Sepsis-Related_Mortality_in_a_Controlled_Interrupted_Time_Series_Analysis_pdf/19663263)。
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