Evidence map›Paper›PMID 40660326›Full record

Observational studyCritical care (London, England)2025

Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.

Ben J Gross, Allison Donahue, James S Ford, Xiaolei Lu, Aaron Boussina, Atul Malhotra, Kai Zheng, Shamim Nemati, Gabriel Wardi

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Sepsis, Patient Monitoring and Nursing Research.Journal of multidisciplinary healthcare · 2026
    Article
  4. Review
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Ben J GrossDivision of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
Allison DonahueDepartment of Emergency Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
James S FordDepartment of Emergency Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Xiaolei LuDivision of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
Aaron BoussinaDivision of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
Atul MalhotraDivision of Pulmonology, Critical Care, and Sleep Medicine, University of California, La Jolla, San Diego, USA.
Kai ZhengDepartment of Informatics, University of California, Irvine, Irvine, USA.
Shamim NematiDivision of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
Gabriel WardiDepartment of Emergency Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA. gwardi@health.ucsd.edu.

Funding

Institute for Clinical and Translational ScienceUM1TR004927 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI DAN M COOPER, Eric J. Vilain · 2024 to 2026
$12.2M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring SensorsR35GM143121 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2021 to 2025
$2.2M
RAAB-AI: Reducing Automation and Anchoring Bias in AI SystemsR01LM013998 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2022 to 2026
$1.7M
AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for SepsisR42AI177108 · NIAID · HEALCISIO, INC. · PI JOSEF, CHRISTOPHER · 2023 to 2024
$1.3M
Implementation of Continuum of Care Sepsis Phenotyping and Risk StratificationK23GM146092 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Gabriel Wardi · 2022 to 2026
$898k
National Institute of Allergy and Infectious Diseases R42AI177108National Institute of General Medical Sciences,United States R35GM143121National Institutes of Health,United States K23GM146092National Science Foundation 2405974NCATS NIH HHS UM1 TR004927NIAID NIH HHS R42 AI177108NIGMS NIH HHS K23 GM146092NIGMS NIH HHS R35 GM143121NLM NIH HHS R01 LM013998NLM NIH HHS T15 LM011271University of California, Multicampus Research Programs and Initiatives M25PR9018U.S. National Library of Medicine,United States R01LM013998
6 · The paper itself

Abstract

backgroundThe current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to stratify patients objectively into these groups and describe patient outcomes as a function of antibiotic timing recommendations based on risk stratification using this approach.

methodsWe conducted an observational cohort study using prospectively applied patient data from two large health systems using patient encounters between 2016 and 2024. At the time of clinical suspicion of sepsis, two deep learning (DL) models were used to stratify patients objectively into groups analogous to the SSC risk groups, based on a patient's likelihood of having sepsis and likelihood of developing shock. These risk groups were: (1) shock likely to develop and sepsis probable, (2) shock likely to develop and sepsis possible, (3) shock unlikely to develop and sepsis probable, and (4) shock unlikely to develop and sepsis possible. The primary outcome was short-term mortality, a composite of in-hospital mortality and transition to hospice care, across each risk group.

resultsWe identified 34,087 adult patients with potential sepsis. At the development site, risk group mortality rates (%) and median time to antibiotics [IQR] were as follows: (1) 23.2%, 1.7 [1.0-3.1] hours; (2) 17.7%, 3.0 [1.7-6.2] hours; (3) 5.0%, 2.8 [1.5-5.1] hours; and (4) 1.9%, 4.6 [2.7-8.0] hours. Results from the validation site were similar. Mortality rates were similar for patients with possible sepsis unlikely to develop shock regardless of antibiotic administration within 1, 3 or more hours from triage. For patients with probable sepsis at the development site, regardless of risk of shock, mortality was significantly lower if antibiotics were administered within the first hour from triage.

conclusionsOur data suggest that patients who are at low risk of developing shock and possible sepsis had similar rates of mortality in the 1-hour vs. > 1-hour and 3-hour vs. > 3-hour time to antibiotic administration groups. Thus, a more lenient time to antibiotic administration could allow for more detailed evaluations and judicious administration of antibiotics without impacting patient mortality. Patients with probable sepsis had lower mortality if antibiotics were administered within 1 h from triage, regardless of risk of shock. Additional prospective studies are required to validate these findings and guide optimal antibiotic timing in patients with suspected sepsis.

Indexed as

Anti-Bacterial AgentsDeep LearningSepsisAgedAged, 80 and overCohort StudiesFemaleHospital MortalityHumansMaleMiddle AgedMortalityProspective StudiesRisk AssessmentTime FactorsAnti-Bacterial AgentsArtificial intelligenceClinical decision supportSepsisSeptic shock

Identifiers

PMID40660326
PMCPMC12257722

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Registered trials

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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.