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Background <p>Identifying 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.</p> Methods <p>This 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.</p> Results <p>A 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&#xa0;h significantly decreased 28-d mortality in Cluster A (aOR 0.73; 95% CI, 0.54–0.98, <i>p =</i> 0.037) and Cluster E (aOR 0.59; 95% CI, 0.39–0.89, <i>p =</i> 0.013).</p> Conclusion <p>The 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.</p> Clinical trial number <p>Not applicable.</p>

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Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Daun Jeong,
  • Jong Rul Park,
  • Seung Jin Maeng,
  • Jung Won Choi,
  • Gun Tak Lee,
  • Sung Yeon Hwang,
  • Chulhong Kim,
  • Jong Eun Park,
  • Tae Gun Shin

摘要

Background

Identifying 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.

Methods

This 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.

Results

A 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).

Conclusion

The 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.