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Health Informatics

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

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To identify the key insights or developments described in this article

1 Credit CME

JAMA Network Open

Published Online: November 19, 2021

Research Letter

Article Information and Disclosures

Andrew Wong, MD¹;

Jie Cao, MPH²;

Patrick G. Lyons, MD, MSc³;

Sayon Dutta, MD, MPH⁴;

Vincent J. Major, PhD⁵;

Erkin Ötleş, MEng^6,7;

Karandeep Singh, MD, MMSc^1,8,9

Author Affiliations

¹Department of Internal Medicine, University of Michigan Medical School, Ann Arbor
²Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor
³John T. Milliken Department of Medicine, Washington University School of Medicine, St Louis, Missouri
⁴Department of Emergency Medicine, Massachusetts General Hospital, Boston
⁵Department of Population Health, New York University Grossman School of Medicine, New York
⁶Medical Scientist Training Program, University of Michigan, Ann Arbor
⁷Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor
⁸Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor
⁹School of Information, University of Michigan, Ann Arbor

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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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Article Information

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 (kdpsingh@umich.edu).

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.

References

Downing NL , Rolnick J , Poole SF , et al. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Qual Saf. 2019;28(9):762-768. doi:10.1136/bmjqs-2018-008765 PubMed Google Scholar Crossref

Wong A , Otles E , Donnelly JP , et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-1070. doi:10.1001/jamainternmed.2021.2626 PubMed Google Scholar Crossref

Ginestra JC , Giannini HM , Schweickert WD , et al. Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Crit Care Med. 2019;47(11):1477-1484. doi:10.1097/CCM.0000000000003803 PubMed Google Scholar Crossref

Eilish MC , Watkins EA . Repairing innovation: a study of integrating AI in clinical care. Data & Society Research Institute. Published September 2020. Accessed August 5, 2021. https://datasociety.net/wp-content/uploads/2020/09/Repairing-Innovation-DataSociety-20200930-1.pdf

Finlayson SG , Subbaswamy A , Singh K , et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283-286. doi:10.1056/NEJMc2104626 PubMed Google Scholar Crossref

Singh K , Valley TS , Tang S , et al Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19. Ann Am Thorac Soc. 2021;18(7):1129-1137. doi:10.1513/AnnalsATS.202006-698OC Google Scholar

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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:

1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
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1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
1.00 CME points in the American Board of Surgery’s (ABS) Continuing Certification program

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