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Author Notes:

Corresponding author: shamim.nemati@emory.edu.


Research Funding:

SN is funded by the National Institutes of Health, award # K01ES025445.

QL is partially supported by the Surgical Critical Care Initiative (SC2i), funded by the Department of Defense’s Defense Health Program – Joint Program Committee 6 / Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001).

The opinions or assertions contained herein are the private ones of the author/speaker and are not to be construed as official or reflecting the views of the Department of Defense, the Uniformed Services University of the Health Sciences or any other agency of the U.S. Government.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Cardiac & Cardiovascular Systems
  • Cardiovascular System & Cardiology
  • Machine learning
  • ECG
  • Dynamics
  • Sepsis
  • Infection
  • Critical care

Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics


Journal Title:

Journal of Electrocardiology


Volume 50, Number 6


, Pages 739-743

Type of Work:

Article | Post-print: After Peer Review


Sepsis remains a leading cause of morbidity and mortality among intensive care unit (ICU) patients. For each hour treatment initiation is delayed after diagnosis, sepsis-related mortality increases by approximately 8%. Therefore, maximizing effective care requires early recognition and initiation of treatment protocols. Antecedent signs and symptoms of sepsis can be subtle and unrecognizable (e.g., loss of autonomic regulation of vital signs), causing treatment delays and harm to the patient. In this work we investigated the utility of high-resolution blood pressure (BP) and heart rate (HR) times series dynamics for the early prediction of sepsis in patients from an urban, academic hospital, meeting the third international consensus definition of sepsis (sepsis-III) during their ICU admission. Using a multivariate modeling approach we found that HR and BP dynamics at multiple time-scales are independent predictors of sepsis, even after adjusting for commonly measured clinical values and patient demographics and comorbidities. Earlier recognition and diagnosis of sepsis has the potential to decrease sepsis-related morbidity and mortality through earlier initiation of treatment protocols.

Copyright information:

© 2017 Elsevier Inc.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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