Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Ishan Taneja¹, Gregory L Damhorst^1,2, Carlos Lopez-Espina¹, Sihai Dave Zhao³, Ruoqing Zhu³, Shah Khan¹, Karen White⁴, James Kumar⁴, Andrew Vincent⁵, Leon Yeh⁵, Shirin Majdizadeh⁴, William Weir⁴, Scott Isbell⁶, James Skinner⁴, Manubolo Devanand⁴, Syed Azharuddin⁴, Rajamurugan Meenakshisundaram⁴, Riddhi Upadhyay⁴, Anwaruddin Syed⁵, Thomas Bauman⁵, Joseph Devito⁵, Charles Heinzmann⁵, Gregory Podolej⁵, Lanxin Shen¹, Sanjay Sharma Timilsina¹, Lucas Quinlan¹, Setareh Manafirasi¹, Enrique Valera⁷, Bobby Reddy^1,7, Rashid Bashir⁷

¹Prenosis Inc., Chicago, Illinois, USA.

²Department of Medicine, Emory University, Atlanta, Georgia, USA.

³Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.

⁴Biomedical Research Center, Carle Foundation Hospital, Urbana, Illinois, USA.

⁵OSF Saint Francis Medical Center, Peoria, Illinois, USA.

⁶Department of Pathology, Saint Louis University School of Medicine, St. Louis, Missouri, USA.

⁷Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.

PubMed PMC DOI PDF

Abstract

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diag…

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