Evidence map›Paper›PMID 39037814›Full record

SynthesisJAMA network open2024

Sepsis Alert Systems, Mortality, and Adherence in Emergency Departments: A Systematic Review and Meta-Analysis.

Hyung-Jun Kim, Ryoung-Eun Ko, Sung Yoon Lim, Sunghoon Park, Gee Young Suh, Yeon Joo Lee

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 4 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.

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

39 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  3. [S3 guideline on sepsis-prevention, diagnosis, therapy, and follow-up care-update 2025].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hyung-Jun KimDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Ryoung-Eun KoDepartment of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Sung Yoon LimDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Sunghoon ParkDepartment of Pulmonary, Allergy and Critical Care Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.
Gee Young SuhDepartment of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Yeon Joo LeeDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Early detection and management of sepsis are crucial for patient survival. Emergency departments (EDs) play a key role in sepsis management but face challenges in timely response due to high patient volumes. Sepsis alert systems are proposed to expedite diagnosis and treatment initiation per the Surviving Sepsis Campaign guidelines. Objective: To review and analyze the association of sepsis alert systems in EDs with patient outcomes. Data Sources: A thorough search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from January 1, 2004, to November 19, 2023. Study Selection: Studies that evaluated sepsis alert systems specifically designed for adult ED patients were evaluated. Inclusion criteria focused on peer-reviewed, full-text articles in English that reported on mortality, ICU admissions, hospital stay duration, and sepsis management adherence. Exclusion criteria included studies that lacked a control group or quantitative reports. Data Extraction and Synthesis: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Two independent reviewers conducted the data extraction using a standardized form. Any disagreements were resolved through discussion. The data were synthesized using a random-effects model due to the expected heterogeneity among the included studies. Main Outcomes and Measures: Key outcomes included mortality, intensive care unit admissions, hospital stay duration, and adherence to the sepsis bundle. Results: Of 3281 initially identified studies, 22 (0.67%) met inclusion criteria, encompassing 19 580 patients. Sepsis alert systems were associated with reduced mortality risk (risk ratio [RR], 0.81; 95% CI, 0.71 to 0.91) and length of hospital stay (standardized mean difference [SMD], -0.15; 95% CI, -0.20 to -0.11). These systems were also associated with better adherence to sepsis bundle elements, notably in terms of shorter time to fluid administration (SMD, -0.42; 95% CI, -0.52 to -0.32), blood culture (SMD, -0.31; 95% CI, -0.40 to -0.21), antibiotic administration (SMD, -0.34; 95% CI, -0.39 to -0.29), and lactate measurement (SMD, -0.15; 95% CI, -0.22 to -0.08). Electronic alerts were particularly associated with reduced mortality (RR, 0.78; 95% CI, 0.67 to 0.92) and adherence with blood culture guidelines (RR, 1.14; 95% CI, 1.03 to 1.27). Conclusions and Relevance: These findings suggest that sepsis alert systems in EDs were associated with better patient outcomes along with better adherence to sepsis management protocols. These systems hold promise for enhancing ED responses to sepsis, potentially leading to better patient outcomes.

Indexed as

Emergency Service, HospitalSepsisGuideline AdherenceHospital MortalityHumansLength of Stay

Identifiers

PMID39037814
PMCPMC11265133

What OpenQuestion holds

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LicenceCC BY
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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.