ArticleBMC emergency medicine2026
Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.
Article in BMC emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundIdentifying high-risk patients in emergency departments (EDs) is crucial due to high mortality rate associated with sepsis and for the timely management. This study aimed to conduct unsupervised consensus clustering of patients with suspected sepsis from a heterogeneous ED population.
methodsThis descriptive, retrospective cohort study was conducted between November 2014 and November 2019 on patients with suspected sepsis who visited the ED. Cluster analysis was performed using variables, such as vital signs and clinical laboratory data, to classify patient characteristics. An artificial intelligence model generated a clustering plot based on the distance values of various variables, identifying five distinct clusters, each representing a unique clinical phenotype among the patient subtypes. In addition to analyzing the clinical phenotypes of each cluster, this study examined the prognosis based on antibiotic administration time through subgroup analysis to provide guidance for clinical application.
resultsA total of 14,402 patients were included in this study. Cluster analysis combined with an artificial intelligence model identified five distinct clusters among patients with suspected sepsis. The baseline characteristics and clinical outcomes of each of the five clusters are described in detail. Cluster B comprised the largest proportion of patients with septic shock and exhibited the highest percentage of critical care interventions, including vasopressor use, mechanical ventilation, intensive care unit admission, and 28-d mortality. Cluster E had the lowest rates of these interventions. In multivariable analysis, administration of antibiotics within 3 h significantly decreased 28-d mortality in Cluster A (aOR 0.73; 95% CI, 0.54–0.98, p = 0.037) and Cluster E (aOR 0.59; 95% CI, 0.39–0.89, p = 0.013).
conclusionThe artificial intelligence model identified five distinct clusters of patients with suspected sepsis who presented to the ED and showed different outcome patterns among the phenotypes. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
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.