TY - JOUR
T1 - Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
AU - Adams, Roy
AU - Henry, Katharine E.
AU - Sridharan, Anirudh
AU - Soleimani, Hossein
AU - Zhan, Andong
AU - Rawat, Nishi
AU - Johnson, Lauren
AU - Hager, David N.
AU - Cosgrove, Sara E.
AU - Markowski, Andrew
AU - Klein, Eili Y.
AU - Chen, Edward S.
AU - Saheed, Mustapha O.
AU - Henley, Maureen
AU - Miranda, Sheila
AU - Houston, Katrina
AU - Linton, Robert C.
AU - Ahluwalia, Anushree R.
AU - Wu, Albert W.
AU - Saria, Suchi
N1 - Funding Information:
We thank Y. Ahmad, M. Yeo and Y. Karklin whose work significantly contributed to early iterations of the development of the deployed system. Further, we thank R. Demski, K. D’Souza, A. Kachalia, A. Chen and clinical and quality stakeholders who contributed to tool deployment, education and championing the work. We gratefully acknowledge the following sources of funding: the Gordon and Betty Moore Foundation (award no. 3926), the National Science Foundation (NSF) Future of Work at the Human-technology Frontier (award no. 1840088) and the Alfred P. Sloan Foundation research fellowship (2018). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, the NSF of the US Government.
Funding Information:
We thank Y. Ahmad, M. Yeo and Y. Karklin whose work significantly contributed to early iterations of the development of the deployed system. Further, we thank R. Demski, K. D’Souza, A. Kachalia, A. Chen and clinical and quality stakeholders who contributed to tool deployment, education and championing the work. We gratefully acknowledge the following sources of funding: the Gordon and Betty Moore Foundation (award no. 3926), the National Science Foundation (NSF) Future of Work at the Human-technology Frontier (award no. 1840088) and the Alfred P. Sloan Foundation research fellowship (2018). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, the NSF of the US Government.
Funding Information:
Under a license agreement between Bayesian Health and the Johns Hopkins University, K.E.H., S.S. and Johns Hopkins University are entitled to revenue distributions. In addition, the university owns equity in Bayesian Health. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. S.S. also has grants from the Gordon and Betty Moore Foundation, the NSF, the National Institutes of Health, Defense Advanced Research Projects Agency, the Food and Drug Administration and the American Heart Association; she is a founder of and holds equity in Bayesian Health; she is the scientific advisory board member for PatientPing; and she has received honoraria for talks from a number of biotechnology, research and health-tech companies. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. D.N.H. discloses salary support and funding to his institution from the Marcus Foundation for the conduct of the vitamin C, thiamine and steroids in sepsis trial. S.E.C. received consulting fees from Basilea for work on an infection adjudication committee for a Staphylococcus aureus bacteremia trial. The remaining authors declare no disclosures of conflicts of interest.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85134542980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134542980&partnerID=8YFLogxK
U2 - 10.1038/s41591-022-01894-0
DO - 10.1038/s41591-022-01894-0
M3 - Article
C2 - 35864252
AN - SCOPUS:85134542980
SN - 1078-8956
VL - 28
SP - 1455
EP - 1460
JO - Nature medicine
JF - Nature medicine
IS - 7
ER -