Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System

John P Davis; Dustin A Wessells; J Randall Moorman

doi:10.1097/CCE.0000000000000294

Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring-A New Kind of Illness Scoring System

Crit Care Explor. 2020 Dec 18;2(12):e0294. doi: 10.1097/CCE.0000000000000294. eCollection 2020 Dec.

Authors

John P Davis^{1

2}, Dustin A Wessells^{2

3}, J Randall Moorman^{2

4

5

6}

Affiliations

¹ Department of Surgery, University of Virginia, Charlottesville, VA.
² Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA.
³ School of Medicine, University of Virginia, Charlottesville, VA.
⁴ Department of Internal Medicine, University of Virginia, Charlottesville, VA.
⁵ Department of Physiology, University of Virginia, Charlottesville, VA.
⁶ Department of Biomedical Engineering, University of Virginia, Charlottesville, VA.

Abstract

Coronavirus disease 2019 can lead to sudden and severe respiratory failure that mandates endotracheal intubation, a procedure much more safely performed under elective rather than emergency conditions. Early warning of rising risk of this event could benefit both patients and healthcare providers by reducing the high risk of emergency intubation. Current illness severity scoring systems, which usually update only when clinicians measure vital signs or laboratory values, are poorly suited for early detection of this kind of rapid clinical deterioration. We propose that continuous predictive analytics monitoring, a new approach to bedside management, is more useful. The principles of this new practice anchor in analysis of continuous bedside monitoring data, training models on diagnosis-specific paths of deterioration using clinician-identified events, and continuous display of trends in risks rather than alerts when arbitrary thresholds are exceeded.

Keywords: coronavirus disease 2019; machine learning; predictive monitoring; sepsis.