• Military medicine · Jan 2022

    Observational Study

    Validation of a Machine Learning Model for Early Shock Detection.

    • Yuliya Pinevich, Adam Amos-Binks, Christie S Burris, Gregory Rule, Marija Bogojevic, Isaac Flint, Brian W Pickering, Christopher P Nemeth, and Vitaly Herasevich.
    • Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA.
    • Mil Med. 2022 Jan 4; 187 (1-2): 82-88.

    ObjectivesThe objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review.DesignWe performed a single-center diagnostic performance study.Patients And SettingA prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units.Measurements And Main ResultsDuring the study time, 5,384 out of 6,630 hospital admissions were eligible. During the same period, the 4TDS shock model sent 825 alerts and 632 were eligible. Among 632 hospital admissions with alerts, 287 were screened positive and 345 were negative. Among 4,752 hospital admissions without alerts, 78 were screened positive and 4,674 were negative. The area under the receiver operating characteristics curve for the 4TDS shock model was 0.86 (95% CI 0.85-0.87%). The 4TDS shock model demonstrated a sensitivity of 78.6% (95% CI 74.1-82.7%) and a specificity of 93.1% (95% CI 92.4-93.8%). The model showed a positive predictive value of 45.4% (95% CI 42.6-48.3%) and a negative predictive value of 98.4% (95% CI 98-98.6%).ConclusionsWe successfully validated an ML model to detect circulatory shock in a prospective observational study. The model used only vital signs and showed moderate performance compared to the gold standard of clinician EMR review when applied to an ICU patient cohort.© The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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