Wardi Gabriel, Carlile Morgan, Holder Andre, Shashikumar Supreeth, Hayden Stephen R, Nemati Shamim
Department of Emergency Medicine, University of California-San Diego, San Diego, CA; Division of Pulmonary, Critical Care, and Sleep Medicine, University of California-San Diego, San Diego, CA.
Department of Emergency Medicine, University of California-San Diego, San Diego, CA.
Ann Emerg Med. 2021 Apr;77(4):395-406. doi: 10.1016/j.annemergmed.2020.11.007. Epub 2021 Jan 15.
Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.
This was an observational cohort study using data from greater than 180,000 patients from 2 academic medical centers between 2014 and 2019, using multiple definitions of sepsis. The Artificial Intelligence Sepsis Expert algorithm was trained with 40 input variables at the development site to predict delayed septic shock (occurring greater than 4 hours after ED triage) at various prediction windows. We then validated the algorithm at a second site, using transfer learning to demonstrate generalizability of the algorithm.
We identified 9,354 patients with severe sepsis, of whom 723 developed septic shock at least 4 hours after triage. The Artificial Intelligence Sepsis Expert algorithm demonstrated excellent area under the receiver operating characteristic curve (>0.8) at 8 and 12 hours for the prediction of delayed septic shock. Transfer learning significantly improved the test characteristics of the Artificial Intelligence Sepsis Expert algorithm and yielded comparable performance at the validation site.
The Artificial Intelligence Sepsis Expert algorithm accurately predicted the development of delayed septic shock. The use of transfer learning allowed significantly improved external validity and generalizability at a second site. Future prospective studies are indicated to evaluate the clinical utility of this model.
机器学习算法可利用电子病历数据改善对急诊科脓毒症综合征的预测。迁移学习作为机器学习的一个新子领域，可使算法在不同临床场所具有通用性。我们旨在验证人工智能脓毒症专家系统对急诊科治疗的一组患者中延迟性感染性休克的预测能力，并证明迁移学习在第二个场所提高外部有效性的可行性。
这是一项观察性队列研究，使用了2014年至2019年期间来自2个学术医疗中心的超过180,000名患者的数据，采用了多种脓毒症定义。人工智能脓毒症专家算法在开发场所使用40个输入变量进行训练，以预测不同预测窗口下的延迟性感染性休克（在急诊科分诊后4小时以上发生）。然后我们在第二个场所验证该算法，使用迁移学习来证明该算法的通用性。
我们识别出9354例严重脓毒症患者，其中723例在分诊后至少4小时发生感染性休克。人工智能脓毒症专家算法在预测延迟性感染性休克时，在8小时和12小时的受试者操作特征曲线下面积表现出色（>0.8）。迁移学习显著改善了人工智能脓毒症专家算法的测试特征，并在验证场所产生了可比的性能。
人工智能脓毒症专家算法准确预测了延迟性感染性休克的发生。迁移学习的使用显著提高了第二个场所的外部有效性和通用性。未来需要进行前瞻性研究来评估该模型的临床实用性。
