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

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.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

“…1; Prediction Assistant, CoMET-inside, Premier, Inc., Charlotte, North Carolina, United States) that is representative in that it uses vital signs and laboratory test results from the electronic health record (EHR), and its basis is regression models trained on clinical event data. 3,11,12 Additionally, it incorporates continuous cardiorespiratory monitoring data that is analyzed mathematically for illness signatures. For each patient in a clinical care unit, the CoMET monitor assigns a comet-like icon and displays the foldincrease in risk from 1 to 5 of a future critical event.…”

“…2 Many predictive analytics tools can provide a snapshot of the patient's clinical state by integrating multiple pieces of information. 3,4 Moreover, some potentially catastrophic illnesses have prodromal signatures that can be detected by algorithms analyzing continuous cardiorespiratory monitoring. [5][6][7] This approach, called predictive analytics monitoring, does not rely on clinician-initiated data and has a true predictive quality 8 in that a risk estimate based on these measures might rise in a patient with no overt signs or symptoms of illness.For the bedside clinician, predictive analytics monitoring presents a new kind of information and a new paradigm of care.…”

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis

“…A risk score incorporating an assessment of the illness trajectory over a period of time can capture the complex courses of patients with sepsis. Because risk scores from predictive analytics can be assessed serially, the integrated risk score also has potential utility as an index of acuity, as a proxy of responsiveness to therapy (i.e., a response biomarker), and as a way to monitor the overall trajectory of illness (11)(12)(13). By assessing the integrated continuous predictive analytic across the patient trajectory in the form of a highly-dimensional time series, we have the opportunity to pursue patient-centered modeling.…”

The Quest for System-Theoretical Medicine in the COVID-19 Era

“…In consequence, not only a unidirectional but rather a bidirectional relation between bench and bed (physician and patient) could improve efficacy of translational medicine. This could be “vertical transdisciplinarity” that complements “horizontal transdisciplinarity” as conventional interdisciplinarity and that combines scientific knowledge production with physicians and patients observations and experiences ( 49 – 52 ).…”