Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients. (4th June 2021)

• Record Type: Journal Article
• Authors: Saria, Suchi; Schulam, Peter; Yeh, Brian J.; Burke, Daniel; Mooney, Sean D.; Fong, Christine T.; Sunshine, Jacob E.; Long, Dustin R.; O'Reilly-Shah, Vikas N.
• Is Part Of: Critical care explorations. Volume 3:Number 6(2021)
• Page Start: e0441
• Publication Date: 2021-06-04
• Subjects: coronavirus disease 2019 -- deterioration monitoring -- electronic surveillance -- predictive model
• DOI: 10.1097/CCE.0000000000000441
• Languages: English
• ISSNs: 2639-8028

Abstract

Abstract : Supplemental Digital Content is available in the text. Abstract : OBJECTIVES: To evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management. DESIGN: Retrospective cohort for algorithm training, testing, and validation. SETTING: Eight hospitals across two geographically distinct regions. PATIENTS: Two-thousand fifteen hospitalized coronavirus disease 2019–positive patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Anticipating Respiratory failure in Coronavirus disease (ARC), a clinically interpretable, continuously monitoring prognostic model of acute respiratory failure in hospitalized coronavirus disease 2019 patients, was developed and validated. An analysis of the most important clinical predictors aligns with key risk factors identified by other investigators but contributes new insights regarding the time at which key factors first begin to exhibit aberrency and distinguishes features predictive of acute respiratory failure in coronavirus disease 2019 versus pneumonia caused by other types of infection. Departing from prior work, ARC was designed to update continuously over time as new observations (vitals and laboratory test results) are recorded in the electronic health record. Validation against data from two geographically distinct health systems showed that the proposed model achieved 75% specificity and 77% sensitivity and predicted acute respiratory failure at a median time of 32 hours prior to onset. Over 80% of true-positive alerts occurred in non-ICU settings. CONCLUSIONS: Patients admitted to non-ICU environments with coronavirus disease 2019 are at ongoing risk of clinical progression to severe disease, yet it is challenging to anticipate which patients will develop acute respiratory failure. A continuously monitoring prognostic model has potential to facilitate anticipatory rather than reactive approaches to escalation of care (e.g., earlier initiation of treatments for severe disease or structured monitoring and therapeutic interventions for high-risk patients).