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Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Heidi Huang, Anna Lynn-Palevsky, Ritankar Das
doi: https://doi.org/10.1101/224014
Hoyt Burdick
1Cabell Huntington Hospital, Huntington, WV
2Marshall University School of Medicine, Huntington, WV
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Eduardo Pino
1Cabell Huntington Hospital, Huntington, WV
2Marshall University School of Medicine, Huntington, WV
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Denise Gabel-Comeau
1Cabell Huntington Hospital, Huntington, WV
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Carol Gu
3Dascena Inc., Hayward, CA
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Heidi Huang
3Dascena Inc., Hayward, CA
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Anna Lynn-Palevsky
3Dascena Inc., Hayward, CA
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  • For correspondence: anna@dascena.com
Ritankar Das
3Dascena Inc., Hayward, CA
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Abstract

Introduction Sepsis is a major health crisis in US hospitals, and several clinical identification systems have been designed to help care providers with early diagnosis of sepsis. However, many of these systems demonstrate low specificity or sensitivity, which limits their clinical utility. We evaluate the effects of a machine learning algodiagnostic (MLA) sepsis prediction and detection system using a before-and-after clinical study performed at Cabell Huntington Hospital (CHH) in Huntington, West Virginia. Prior to this study, CHH utilized the St. John’s Sepsis Agent (SJSA) as a rules-based sepsis detection system.

Methods The Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE) study was carried out between July 1, 2017 and August 30, 2017. All patients over the age of 18 who were admitted to the emergency department or intensive care units at CHH were monitored during the study. We assessed pre-implementation baseline metrics during the month of July, 2017, when the SJSA was active. During implementation in the month of August, 2017, SJSA and the MLA concurrently monitored patients for sepsis risk. At the conclusion of the study period, the primary outcome of sepsis-related in-hospital mortality and secondary outcome of sepsis-related hospital length of stay were compared between the two groups.

Results Sepsis-related in-hospital mortality decreased from 3.97% to 2.64%, a 33.5% relative decrease (P = 0.038), and sepsis-related length of stay decreased from 2.99 days in the pre-implementation phase to 2.48 days in the post-implementation phase, a 17.1% relative reduction (P < 0.001).

Conclusion Reductions in patient mortality and length-of-stay were observed with use of a machine learning algorithm for early sepsis detection in the emergency department and intensive care units at Cabell Huntington Hospital, and may present a method for improving patient outcomes.

Trial Registration ClinicalTrials.gov, NCT03235193, retrospectively registered on July 27th 2017.

  • List of Abbreviations

    AUROC
    area under the receiver operating characteristic
    CHH
    Cabell Huntington Hospital
    EHR
    electronic health record
    ICU
    intensive care unit
    IRB
    institutional review board
    LOS
    length of stay
    MEWS
    modified early warning score
    MLA
    machine learning algodiagnostic
    ROC
    receiver operating characteristic
    SE
    standard error
    SIRS
    systemic inflammatory response syndrome
    SJSA
    St. John’s Sepsis Agent
    qSOFA
    quick sequential organ failure assessment
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    Posted January 15, 2018.
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    Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study
    Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Heidi Huang, Anna Lynn-Palevsky, Ritankar Das
    bioRxiv 224014; doi: https://doi.org/10.1101/224014
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    Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study
    Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Heidi Huang, Anna Lynn-Palevsky, Ritankar Das
    bioRxiv 224014; doi: https://doi.org/10.1101/224014

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