Hasso Plattner Institute, Rudolf-Breitscheid-Straße 187, Potsdam, 14482, Brandenburg, Germany.
Institute of Medical Informatics at Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Berlin, Germany.
Comput Biol Med. 2024 Dec;183:109244. doi: 10.1016/j.compbiomed.2024.109244. Epub 2024 Oct 24.
Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algorithms that can determine which alarms are clinically relevant and which are not.
This paper presents a computer-aided method to automatically determine whether and which interventions followed an alarm. Our algorithm annotates a large data set of oxygen saturation alarms. Previous studies only presented analyses on smaller data sets of manually annotated alarms. Future research can use our large data set of labelled alarms to train machine learning models, for example for alarm prioritisation.
We propose an alarm annotation algorithm that can efficiently label oxygen saturation alarms from respiratory alarm management by actionability. This algorithm is based on an alarm annotation guideline and works on data from 1961 patients from the hospital information system recorded 06/2019-06/2021. The algorithm analyses a pre-defined time frame after an alarm to determine whether an intervention followed or not. The resulting data set can be used to train machine learning models that predict alarm actionability.
Our open-source algorithm is the first to create a large data set of around 2.5 million relevance-annotated alarms in mere hours. A task that would take years using manual annotation. Our algorithm denotes about 9% of the alarms as actionable. This is in line with previous research. The data set also shows which respiratory management interventions medical staff used to counteract the cause of an alarm.
The data set can be a starting point to reduce the number of unnecessary oxygen saturation alarms. For example, it can serve as a training data set for machine learning models that assess future alarms. The algorithm might be re-used to annotate other alarm data sets as well.
重症监护病房（ICU）中过多的不必要警报是导致警报疲劳的主要原因之一：医护人员负担过重，无法做出适当的反应。这会危及患者和医护人员的安全。目前，还没有算法可以确定哪些警报具有临床相关性，哪些不具有。
本文提出了一种计算机辅助方法，用于自动确定警报是否以及采取了哪些干预措施。我们的算法对大量血氧饱和度警报数据进行了注释。以前的研究仅对人工注释的较小警报数据集进行了分析。未来的研究可以使用我们带有标签的大型警报数据集来训练机器学习模型，例如用于警报优先级排序。
我们提出了一种警报注释算法，该算法可以有效地根据可操作性对呼吸警报管理中的血氧饱和度警报进行注释。该算法基于一个警报注释指南，并使用来自医院信息系统的 1961 名患者的数据（记录于 2019 年 6 月至 2021 年 6 月）进行操作。该算法分析警报发生后的预定义时间框架，以确定是否采取了干预措施。生成的数据集可用于训练机器学习模型，以预测警报的可操作性。
我们的开源算法是第一个在短短几个小时内创建了一个包含约 250 万条相关警报的大型数据集的算法。这是一个使用手动注释需要数年时间才能完成的任务。我们的算法将约 9%的警报标记为可操作。这与以前的研究结果一致。该数据集还显示了医护人员用来对抗警报原因的呼吸管理干预措施。
该数据集可以作为减少不必要的血氧饱和度警报数量的起点。例如，它可以作为评估未来警报的机器学习模型的训练数据集。该算法也可以重新用于注释其他警报数据集。
