Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
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DOI：
10.1097/ccm.0000000000004145
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发表时间：
2020-02-01
影响因子：
8.8
通讯作者：
Sharma, Ashish
中科院分区：
文献类型：
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作者：
Reyna, Matthew A.;Josef, Christopher S.;Sharma, Ashish
目的：脓毒症是一个主要的公共卫生问题，具有显著的发病率，死亡率和医疗费用。脓毒症的早期发现和抗生素治疗可改善预后。然而，尽管专业的重症监护协会提出了新的临床标准，有助于脓毒症的识别，早期发现和治疗的基本需求仍然没有得到满足。作为回应，研究人员提出了早期脓毒症检测的算法，但由于不同的患者队列、临床变量和脓毒症标准、预测任务、评估指标和其他差异，直接比较这些方法是不可能的。为了解决这些问题，2019年心脏病学挑战赛中的PhysioNet/Computing促进了自动化开源算法的开发，用于从临床数据中早期检测脓毒症。设计图：参与者将容器化算法提交到基于云的测试环境中，在那里我们使用一种新的基于临床实用程序的评估指标对其二进制分类性能进行评分。我们专门为挑战赛设计了这个评分函数，以奖励早期预测的算法，并对延迟或错过的预测以及错误警报进行惩罚。背景：三个独立医院系统的ICU。我们公开分享了两个系统的数据，并隔离了所有三个系统的数据进行评分。患者：我们收集了超过60，000名ICU患者，患者在ICU停留的每小时有多达40个临床变量。我们应用脓毒症-3临床标准进行脓毒症发作。干预措施：无。测量和主要结果：共有来自学术界和工业界的104个团体参加，提交了853份意见书。此外，基于挑战条目的90篇摘要被接受用于心脏病学计算。结论：不同的计算方法预测败血症的发病前几个小时的临床识别，但不同的医院系统的普遍性仍然是一个挑战。
Objectives: Sepsis is a major public health concern with significant morbidity, mortality, and healthcare expenses. Early detection and antibiotic treatment of sepsis improve outcomes. However, although professional critical care societies have proposed new clinical criteria that aid sepsis recognition, the fundamental need for early detection and treatment remains unmet. In response, researchers have proposed algorithms for early sepsis detection, but directly comparing such methods has not been possible because of different patient cohorts, clinical variables and sepsis criteria, prediction tasks, evaluation metrics, and other differences. To address these issues, the PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. Design: Participants submitted containerized algorithms to a cloud-based testing environment, where we graded entries for their binary classification performance using a novel clinical utility-based evaluation metric. We designed this scoring function specifically for the Challenge to reward algorithms for early predictions and penalize them for late or missed predictions and for false alarms. Setting: ICUs in three separate hospital systems. We shared data from two systems publicly and sequestered data from all three systems for scoring. Patients: We sourced over 60,000 ICU patients with up to 40 clinical variables for each hour of a patient's ICU stay. We applied Sepsis-3 clinical criteria for sepsis onset. Interventions: None. Measurements and Main Results: A total of 104 groups from academia and industry participated, contributing 853 submissions. Furthermore, 90 abstracts based on Challenge entries were accepted for presentation at Computing in Cardiology. Conclusions: Diverse computational approaches predict the onset of sepsis several hours before clinical recognition, but generalizability to different hospital systems remains a challenge.