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

Ishan Taneja, Gregory L. Damhorst, Carlos Lopez-Espina, Sihai Dave Zhao, Ruoqing Zhu, Shah Khan, Karen White, James Kumar, Andrew Vincent, Leon Yeh, Shirin Majdizadeh, William Weir, Scott Isbell, James Skinner, Manubolo Devanand, Syed Azharuddin, Rajamurugan Meenakshisundaram, Riddhi Upadhyay, Anwaruddin Syed, Thomas BaumanJoseph Devito, Charles Heinzmann, Gregory Podolej, Lanxin Shen, Sanjay Sharma Timilsina, Lucas Quinlan, Setareh Manafirasi, Enrique Valera, Bobby Reddy, Rashid Bashir

Research output: Contribution to journalArticlepeer-review


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 diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

Original languageEnglish (US)
Pages (from-to)1578-1589
Number of pages12
JournalClinical and Translational Science
Issue number4
StatePublished - Jul 2021

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)


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