美国约翰霍普金斯大学Suchi Saria、Albert W. Wu等研究人员合作完成基于TREWS机器学习的败血症早期预警系统的前瞻性多地点研究。相关论文于2022年7月21日发表在《自然—医学》杂志上。

在一项前瞻性、多地点的队列研究中，研究人员报道了患者的结果和医疗机构与被称为靶向实时预警系统（TREWS）的败血症警报系统的互动关系。在研究期间，五家医院的590,736名患者接受了TREWS的监测。研究人员重点分析了6,877名败血症患者，这些患者在开始抗生素治疗前就已被警报识别。在对患者的表现和严重程度进行调整后，该组患者在警报发出后3小时内得到医疗服务提供者的确认，其院内死亡率降低（3.3%，置信区间（CI）为1.7，5.1%，调整后的绝对降低率；18.7%，CI为9.4，27. 与那些在3小时内没有被医疗服务提供者确认的病人相比，死亡率（4.5%，CI 0.8，8.3%，调整后的绝对减少）和器官衰竭的改善在那些被额外标记为高风险的病人中更大。

这些研究结果表明，早期预警系统有可能早期识别败血症患者并改善患者的预后，而且可以在预警时识别那些从早期治疗中获益最大的败血症患者，并将其列为优先事项。

据悉，败血症的早期识别和治疗与改善病人的预后有关。基于机器学习的预警系统可能会减少识别的时间，但很少有系统经过临床评估。

附：英文原文

Title: Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

Author: Adams, Roy, Henry, Katharine E., Sridharan, Anirudh, Soleimani, Hossein, Zhan, Andong, Rawat, Nishi, Johnson, Lauren, Hager, David N., Cosgrove, Sara E., Markowski, Andrew, Klein, Eili Y., Chen, Edward S., Saheed, Mustapha O., Henley, Maureen, Miranda, Sheila, Houston, Katrina, Linton, Robert C., Ahluwalia, Anushree R., Wu, Albert W., Saria, Suchi

Issue&Volume: 2022-07-21

Abstract: Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). During the study, 590,736 patients were monitored by TREWS across five hospitals. We focused our analysis on 6,877 patients with sepsis who were identified by the alert before initiation of antibiotic therapy. Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h. Improvements in mortality rate (4.5%, CI 0.8, 8.3%, adjusted absolute reduction) and organ failure were larger among those patients who were additionally flagged as high risk. Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert Prospective evaluation of a machine learning-based early warning system for sepsis, deployed at five hospitals, showed that interaction of health-care providers with the system was associated with better patient outcomes, including reduced in-hospital mortality.

DOI: 10.1038/s41591-022-01894-0

Source: https://www.nature.com/articles/s41591-022-01894-0