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

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Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
Nature Medicine ( IF 58.7 ) Pub Date : 2022-07-21 , DOI: 10.1038/s41591-022-01894-0
Roy Adams _{1,

2} , Katharine E Henry _{2,

3} , Anirudh Sridharan ₄ , Hossein Soleimani ₅ , Andong Zhan _{2,

3} , Nishi Rawat ₆ , Lauren Johnson ₇ , David N Hager ₈ , Sara E Cosgrove ₈ , Andrew Markowski ₉ , Eili Y Klein ₁₀ , Edward S Chen ₈ , Mustapha O Saheed ₁₀ , Maureen Henley ₇ , Sheila Miranda ₁₁ , Katrina Houston ₇ , Robert C Linton ₄ , Anushree R Ahluwalia ₇ , Albert W Wu _{6,

8,

12,

13,

14} , Suchi Saria _{1,

3,

8,

12,

15}

Affiliation

Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Howard County General Hospital, Columbia, MD, USA.
Health Informatics, University of California, San Francisco, CA, USA.
Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA.
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Suburban Hospital, Bethesda, MD, USA.
Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA.
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Bayesian Health, New York, NY, USA.

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

中文翻译：

实施基于 TREWS 机器学习的败血症早期预警系统后对患者结果的前瞻性、多站点研究

脓毒症的早期识别和治疗与改善患者预后有关。基于机器学习的预警系统可能会缩短识别时间，但很少有系统经过临床评估。在这项前瞻性、多地点的队列研究中，我们检查了患者结果与提供者互动与部署的败血症警报系统之间的关联，该系统称为有针对性的实时早期预警系统 (TREWS)。在研究期间，TREWS 对五家医院的 590,736 名患者进行了监测。我们将分析重点放在 6,877 名脓毒症患者上，这些患者在抗生素治疗开始前被警报识别。调整患者表现和严重程度后，该组患者在警报后 3 小时内由提供者确认警报的患者住院死亡率降低（3.3%，置信区间 (CI) 1.7，5.1%，调整后的绝对减少和 18.7%，CI 9.4，27.0%，调整的相对减少）、器官衰竭和住院时间与未在 3 小时内由提供者确认警报的患者相比。在另外被标记为高风险的患者中，死亡率（4.5%，CI 0.8, 8.3%，调整后的绝对降低）和器官衰竭的改善更大。我们的研究结果表明，早期预警系统有可能及早识别脓毒症患者并改善患者预后，并且可以在警报时识别并优先考虑将从早期治疗中受益最大的脓毒症患者在另外被标记为高风险的患者中，死亡率（4.5%，CI 0.8, 8.3%，调整后的绝对降低）和器官衰竭的改善更大。我们的研究结果表明，早期预警系统有可能及早识别脓毒症患者并改善患者预后，并且可以在警报时识别并优先考虑将从早期治疗中受益最大的脓毒症患者在另外被标记为高风险的患者中，死亡率（4.5%，CI 0.8, 8.3%，调整后的绝对降低）和器官衰竭的改善更大。我们的研究结果表明，早期预警系统有可能及早识别脓毒症患者并改善患者预后，并且可以在警报时识别并优先考虑将从早期治疗中受益最大的脓毒症患者

更新日期：2022-07-22

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