Predictive algoRithm for EValuation and Intervention in SEpsis

Overview

In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

Full Title of Study: “Prediction of Severe Sepsis Using a Machine Learning Algorithm”

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Non-Randomized
    • Intervention Model: Factorial Assignment
    • Primary Purpose: Diagnostic
    • Masking: None (Open Label)
  • Study Primary Completion Date: August 30, 2017

Interventions

  • Other: Severe Sepsis Prediction
    • Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
  • Other: Severe Sepsis Detection
    • Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Arms, Groups and Cohorts

  • Experimental: With InSight
    • Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.
  • Active Comparator: Without Insight
    • Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.

Clinical Trial Outcome Measures

Primary Measures

  • In-hospital mortality
    • Time Frame: Through study completion, an average of 30 days

Secondary Measures

  • Hospital length of stay
    • Time Frame: Through study completion, an average of 30 days

Participating in This Clinical Trial

Inclusion Criteria

  • All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible. Exclusion Criteria:

  • All patients younger than 18 years of age will be excluded.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Dascena
  • Collaborator
    • Cabell Huntington Hospital
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Hoyt Burdick, Principal Investigator, Cabell Huntington Hospital

References

Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.

Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.

Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.

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