Evidence map›Paper›PMID 35864252›Full record

ArticleNature medicine2022

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

Roy Adams, Katharine E Henry, Anirudh Sridharan, Hossein Soleimani, Andong Zhan, Nishi Rawat, Lauren Johnson, David N Hager, Sara E Cosgrove, Andrew Markowski and 10 more

2 registry-linked trialsAbstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 182 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
182citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07223684 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Surgical Critical Care Initiative (SC2i) Tissue and Data Acquisition Protocol (TDAP) in Burn Patients Improving the Robustness and Generalizability of Post-Burn Sepsis Prediction With the Post-Burn Sepsis Digital Twin

TypeobservationalSponsorHenry M. Jackson Foundation for the Advancement of Military MedicineRan2026 to 2027Enrolled80ConditionsSepsis, Burn Injury, Wound Healing, Critical IllnessArmsObservational Cohort (No Intervention)
NCT07249762 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Surgical Critical Care Initiative (SC2i) Tissue and Data Acquisition Protocol (TDAP) in Burn Patients Improving the Robustness and Generalizability of Post-burn Sepsis Prediction With the Post-Burn Sepsis Digital Twin. Group 1

TypeobservationalSponsorHenry M. Jackson Foundation for the Advancement of Military MedicineRan2026 to 2027Enrolled40ConditionsSepsis, Burn Injury, Wound Healing, Critical IllnessArmsObservational Cohort (No Intervention)
3 · Its place in the literature

Who cites it

182 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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122 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Roy AdamsMalone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
Katharine E HenryDepartment of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-0446-9717
Anirudh SridharanHoward County General Hospital, Columbia, MD, USA.
Hossein SoleimaniHealth Informatics, University of California, San Francisco, CA, USA.
Andong ZhanDepartment of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Nishi RawatArmstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Lauren JohnsonDepartment of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA.
David N HagerDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Sara E CosgroveDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Andrew MarkowskiSuburban Hospital, Bethesda, MD, USA.
Eili Y KleinDepartment of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-1304-5289
Edward S ChenDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Mustapha O SaheedDepartment of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Maureen HenleyDepartment of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA.
Sheila MirandaDepartment of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA.
Katrina HoustonDepartment of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA.
Robert C LintonHoward County General Hospital, Columbia, MD, USA.
Anushree R AhluwaliaDepartment of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA.
Albert W WuArmstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA. awu@jhu.edu.ORCID http://orcid.org/0000-0001-6189-7120
Suchi SariaMalone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.ORCID http://orcid.org/0000-0002-7667-5210

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

SepsisCohort StudiesHospital MortalityHumansMachine LearningProspective StudiesRetrospective Studies

Identifiers

PMID35864252

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.