Effectiveness of automated alerting system compared to usual care for the management of sepsis

Zhongheng Zhang; Lin Chen; Ping Xu; Qing Wang; Jianjun Zhang; Kun Chen; Casey M Clements; Leo Anthony Celi; Vitaly Herasevich; Yucai Hong

doi:10.1038/s41746-022-00650-5

Effectiveness of automated alerting system compared to usual care for the management of sepsis

NPJ Digit Med. 2022 Jul 19;5(1):101. doi: 10.1038/s41746-022-00650-5.

Authors

Zhongheng Zhang^#¹, Lin Chen^#², Ping Xu^#^{3

4

5}, Qing Wang⁶, Jianjun Zhang³, Kun Chen², Casey M Clements⁷, Leo Anthony Celi^{8

9

10}, Vitaly Herasevich¹¹, Yucai Hong¹²

Affiliations

¹ Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. zh_zhang1984@zju.edu.cn.
² Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China.
³ Emergency Department, Zigong Fourth People's Hospital, Zigong, Sichuan, China.
⁴ Institute of Medical Big Data, Zigong Academy of Artificial Intelligence and Big Data for Medical Science Artificial Intelligence, Zigong, Sichuan, China.
⁵ Key Laboratory of Sichuan Province, Zigong, China.
⁶ Department of Surgery, University of Virginia, Charlottesville, VA, USA.
⁷ Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA.
⁸ Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, USA.
⁹ Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA.
¹⁰ Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, USA.
¹¹ Department of Anesthesiology and Perioperative Medicine, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN, USA.
¹² Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

^# Contributed equally.

Abstract

There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73-1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51-0.90) and ward (RR: 0.71; 95% CI: 0.61-0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39-0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63-0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.