| Literature DB >> 32948623 |
Marcela P Vizcaychipi1, Claire L Shovlin2, Alex McCarthy3, Alice Howard4, Alexander Brown3, Michelle Hayes4, Suveer Singh4, Linsey Christie4, Alice Sisson4, Roger Davies4, Christopher Lockie4, Monica Popescu4, Amandeep Gupta4, James Armstrong4, Hisham Said4, Timothy Peters4, Richard T Keays4.
Abstract
Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: SARS; clinical care; emergency care systems; infectious diseases; management; resuscitation; thrombo-embolic disease; ventilation
Mesh:
Year: 2020 PMID: 32948623 DOI: 10.1136/emermed-2020-210199
Source DB: PubMed Journal: Emerg Med J ISSN: 1472-0205 Impact factor: 2.740