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Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis.

Daun Jeong1,2, Minyoung Choi3, Seung Jin Maeng3

  • 1Division of Critical Care Medicine, Department of Emergency Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong-si, Gyeonggi-do, Republic of Korea.

Clinical and Experimental Emergency Medicine
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Summary

Machine learning identified distinct patient clusters in sepsis, revealing varied vasopressor needs and mortality risks. This approach may help guide targeted sepsis treatment strategies.

Keywords:
Cluster analysisEmergency departmentIntensive care unitsSepsisSeptic shock

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Area of Science:

  • Critical Care Medicine
  • Data Science in Healthcare
  • Translational Research

Background:

  • Sepsis presents complex, heterogeneous clustering patterns, posing a significant clinical challenge.
  • Effective management requires understanding patient subgroups and their specific needs.

Purpose of the Study:

  • To investigate the association between vasopressor administration and machine learning-derived clusters.
  • To analyze clusters based on initial vital signs and lactate in emergency department (ED) and intensive care unit (ICU) settings.

Main Methods:

  • Retrospective cohort analysis using KOSS Registry and MIMIC-IV database.
  • K-means clustering applied to vital signs and lactate levels to identify patient clusters.
  • Primary outcome: vasopressor administration; secondary outcomes: second vasopressor administration and 28-day mortality.

Main Results:

  • Three distinct clusters were identified in both cohorts.
  • Cluster 3 in both KOSS and MIMIC-IV cohorts showed lowest mean arterial pressure (MAP) and highest diastolic shock index (DSI).
  • This cluster was associated with significantly higher rates of vasopressor use and 28-day mortality.

Conclusions:

  • Machine learning-derived clusters based on initial data reveal distinct patterns of vasopressor use and mortality in sepsis.
  • This clustering approach shows potential for guiding timely and targeted vasopressor therapy.
  • Prospective validation is needed to confirm the clinical utility of this machine learning-based strategy.