Imperial College London
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Dr Marcela P. Vizcaychipi

Faculty of Medicine, Department of Surgery & Cancer

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 3315 8903m.vizcaychipi Website

 
 
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Location

 

3.21Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Vizcaychipi:2020:10.1136/emermed-2020-210199,
author = {Vizcaychipi, M and Shovlin, C and McCarthy, A and Howard, A and Brown, A and Hayes, M and Singh, S and Christie, L and Sisson, A and Davies, R and Lockie, C and Popescu, M and Gupta, A and Armstrong, J and Said, H and Peters, T and Keays, RT and Consortium, CC},
doi = {10.1136/emermed-2020-210199},
journal = {Emergency Medicine Journal},
pages = {630--636},
title = {Development and implementation of a COVID-19 near real time traffic light system in an acute hospital setting},
url = {http://dx.doi.org/10.1136/emermed-2020-210199},
volume = {37},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Vizcaychipi,M
AU  - Shovlin,C
AU  - McCarthy,A
AU  - Howard,A
AU  - Brown,A
AU  - Hayes,M
AU  - Singh,S
AU  - Christie,L
AU  - Sisson,A
AU  - Davies,R
AU  - Lockie,C
AU  - Popescu,M
AU  - Gupta,A
AU  - Armstrong,J
AU  - Said,H
AU  - Peters,T
AU  - Keays,RT
AU  - Consortium,CC
DO  - 10.1136/emermed-2020-210199
EP  - 636
PY  - 2020///
SN  - 1472-0205
SP  - 630
TI  - Development and implementation of a COVID-19 near real time traffic light system in an acute hospital setting
T2  - Emergency Medicine Journal
UR  - http://dx.doi.org/10.1136/emermed-2020-210199
UR  - https://emj.bmj.com/content/37/10/630
UR  - http://hdl.handle.net/10044/1/92457
VL  - 37
ER  -
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