Baumgartl T, Petzold M, Wunderlich M, Hohn M, Archambault D, Lieser M, Dalpke A, Scheithauer S, Marschollek M, Eichel V M, Mutters N T, Consortium Highmed, Landesberger T Von
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):711-721. doi: 10.1109/TVCG.2020.3030437. Epub 2021 Jan 28.
Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak - the patient zero or index patient - requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.
医院内的病原体爆发（即细菌和病毒爆发）可导致高死亡率，并显著增加医院成本。当感染患者数量超过地方病流行水平或特定人群中病原体的通常流行率时，通常会注意到爆发情况。将传播途径追溯到爆发源头——零号病人或指示病例——需要分析微生物数据和患者接触情况。这通常由感染控制专家手动完成。我们提出了一种新颖的可视化分析方法，以支持对传播途径、患者接触情况、爆发进展以及患者住院期间的时间线进行分析。感染控制专家将我们的解决方案应用于德国一家大型医院实际发生的肺炎克雷伯菌爆发事件。通过使用我们的系统，专家们能够将传播途径的分析扩展到更长的时间间隔（即数年的数据而非数天），并涵盖更多病房。此外，该系统能够将分析时间从数天缩短至数小时。在我们的最终研究中，来自德国七家医院的25位专家的反馈证明，我们的解决方案在分析爆发事件方面带来了显著益处。
