Sepsis early warning systems aim to assist clinicians in recognizing and treating sepsis. Historically, these early warning systems have relied on simple clinical rules, such as systemic inflammatory response syndrome criteria, to identify patients with possible sepsis. To date, sepsis early warning systems have not been shown to reliably improve patient outcomes,1 and artificial intelligence (AI) systems such as the widely implemented Epic Sepsis Model (ESM) are beginning to replace them.
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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.
Accepted for Publication: September 26, 2021.
Published: November 19, 2021. doi:10.1001/jamanetworkopen.2021.35286
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Wong A et al. JAMA Network Open.
Corresponding Author: Karandeep Singh, MD, MMSc, Department of Learning Health Sciences, University of Michigan Medical School, 1161H NIB, 300 N Ingalls St, Ann Arbor, MI 48109 (firstname.lastname@example.org).
Author Contributions: Dr Singh had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Wong and Ms Cao contributed equally to this work.
Concept and design: Wong, Cao, Lyons, Dutta, Ötleş, Singh.
Acquisition, analysis, or interpretation of data: Wong, Cao, Lyons, Dutta, Major, Singh.
Drafting of the manuscript: Wong, Cao, Major.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Wong, Cao, Lyons, Singh.
Administrative, technical, or material support: Wong, Ötleş.
Supervision: Dutta, Singh.
Conflict of Interest Disclosures: Dr Lyons reported receiving grants from the National Institutes of Health National Center for Advancing Translational Sciences and the Doris Duke Charitable Foundation and the Big Ideas Award from BJC HealthCare and Washington University. Mr Ötleş reported having a patent pending for the University of Michigan for an artificial intelligence–based approach for the dynamic prediction of the injured patient health state. Dr Singh reported receiving grants from Teva Pharmaceuticals and Blue Cross Blue Shield of Michigan. No other disclosures were reported.
Funding/Support: This study was supported by the University of Michigan Precision Health (Ms Cao), grant KL2TR002346 from the National Institutes of Health National Center for Advancing Translational Sciences (Dr Lyons), the Doris Duke Charitable Foundation Fund to Retain Clinical Scientists (Dr Lyons), and grant T32GM007863 from the National Institutes of Health National Institute of General Medical Sciences (to Mr Ötleş).
Role of the Funder/Sponsor: The University of Michigan Precision Health, the National Institutes of Health, and the Doris Duke Charitable Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:
It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.
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