Bartenschlager Christina C, Brunner Jens O, Kubiciel Michael, Heller Axel R
Applied Data Science in Healthcare, Nürnberg School of Health, Ohm University of Applied Sciences Nuremberg, 90489, Nürnberg, Germany.
Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, University Hospital of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany.
Med Klin Intensivmed Notfmed. 2025 May;120(4):307-315. doi: 10.1007/s00063-024-01162-8. Epub 2024 Aug 2.
The explicit prohibition of discontinuing intensive care unit (ICU) treatment that has already begun by the newly established German Triage Act in favor of new patients with better prognoses (tertiary triage) under crisis conditions may prevent saving as many patients as possible and therefore may violate the international well-accepted premise of undertaking the "best for the most" patients. During the COVID-19 pandemic, authorities set up lockdown measures and infection-prevention strategies to avoid an overburdened health-care system. In cases of situational overload of ICU resources, when transporting options are exhausted, the question of a tertiary triage of patients arises.
We provide data-driven analyses of score- and non-score-based tertiary triage policies using simulation and real-world electronic health record data in a COVID-19 setting. Ten different triage policies, for example, based on the Simplified Acute Physiology Score (SAPS II), are compared based on the resulting mortality in the ICU and inferential statistics.
Our study shows that score-based tertiary triage policies outperform non-score-based tertiary triage policies including compliance with the German Triage Act. Based on our simulation model, a SAPS II score-based tertiary triage policy reduces mortality in the ICU by up to 18 percentage points. The longer the queue of critical care patients waiting for ICU treatment and the larger the maximum number of patients subject to tertiary triage, the greater the effect on the reduction of mortality in the ICU.
A SAPS II score-based tertiary triage policy was superior in our simulation model. Random allocation or "first come, first served" policies yield the lowest survival rates, as will adherence to the new German Triage Act. An interdisciplinary discussion including an ethical and legal perspective is important for the social interpretation of our data-driven results.
新制定的德国分诊法明确禁止在危机情况下为了预后更好的新患者（三级分诊）而中断已经开始的重症监护病房（ICU）治疗，这可能会阻碍拯救尽可能多的患者，因此可能违反国际上广为接受的“为最多的患者提供最佳治疗”这一前提。在新冠疫情期间，当局制定了封锁措施和感染预防策略，以避免医疗系统不堪重负。在ICU资源出现情况性过载、转运选择用尽的情况下，就会出现患者三级分诊的问题。
我们在新冠疫情背景下，利用模拟和真实世界的电子健康记录数据，对基于评分和非评分的三级分诊政策进行数据驱动分析。比较了十种不同的分诊政策，例如基于简化急性生理学评分（SAPS II）的政策，并根据ICU中的死亡率和推断统计进行分析。
我们的研究表明，基于评分的三级分诊政策优于包括符合德国分诊法在内的非评分三级分诊政策。基于我们的模拟模型，基于SAPS II评分的三级分诊政策可将ICU死亡率降低多达18个百分点。等待ICU治疗的重症患者队列越长，接受三级分诊的患者最大数量越大，对降低ICU死亡率的效果就越显著。
在我们的模拟模型中，基于SAPS II评分的三级分诊政策表现更优。随机分配或“先到先得”政策的生存率最低，遵守新的德国分诊法也是如此。包括伦理和法律视角在内的跨学科讨论对于从社会层面解读我们的数据驱动结果很重要。
