Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting.
Adult
Age Factors
Aged
COVID-19
Coronavirus Infections
/ diagnosis
Disease Progression
Emergency Medical Tags
/ trends
Emergency Service, Hospital
/ statistics & numerical data
Female
Hospital Mortality
/ trends
Hospitals, University
Humans
Male
Middle Aged
Pandemics
Patient Care Team
/ organization & administration
Patient Selection
Pneumonia, Viral
/ diagnosis
Precision Medicine
/ statistics & numerical data
Risk Assessment
Severity of Illness Index
Sex Factors
Thromboembolism
/ diagnosis
United Kingdom
SARS
clinical care
emergency care systems
infectious diseases
management
resuscitation
thrombo-embolic disease
ventilation
Journal
Emergency medicine journal : EMJ
ISSN: 1472-0213
Titre abrégé: Emerg Med J
Pays: England
ID NLM: 100963089
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
received:
09
06
2020
revised:
15
08
2020
accepted:
19
08
2020
pubmed:
20
9
2020
medline:
7
10
2020
entrez:
19
9
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32948623
pii: emermed-2020-210199
doi: 10.1136/emermed-2020-210199
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
630-636Investigateurs
Abela Christopher
(A)
Al-Hindawi Ahmed
(AH)
Allam Joanna
(A)
Arrica Mauro
(A)
At Christelle
(A)
Bargados Javier
(B)
Beach Madeleine
(B)
Beveridge Ian
(B)
Bodagh Neil
(B)
Brooks Peter
(B)
Brown Alexander
(B)
Browning Tom
(B)
Bruce Charo
(B)
Chima Kiran
(C)
Cofie James
(C)
Collier Harriet
(C)
Collier Jonathan
(C)
Collins Declan
(C)
Collins Karen
(C)
Conway Deirdre
(C)
Cordrey Victoria
(C)
Cormack Caroline
(C)
Courtney Alona
(C)
Cox Mark
(C)
Sarah Cox
(S)
Cuddihy Joshua
(C)
Dalrymple Aleck
(D)
Deol Paramjeet
(D)
Dob Daryl
(D)
Dunn Juliet
(D)
Durbridge Jackeline
(D)
Eccles Simon
(E)
Elbadaoui Jana
(E)
Elsawahli Muna
(E)
El-Wahab Niveen
(EW)
Evans Philippa
(E)
Fee Noel
(F)
Forman Emma
(F)
Frunza Gabriela
(F)
Gallagher Susan
(G)
Ganatra Rea
(G)
Gandhi Ajay
(G)
Gary Davies
(G)
Glicksman Clare
(G)
Gonzales Joseph
(G)
Greaney Lisa
(G)
Greenhalgh Samuel
(G)
Gregson Samuel
(G)
Haire Kevin
(H)
Hanger Sofia
(H)
Haque Seleena
(H)
Hare Alison
(H)
Hensher Charlie
(H)
Herincs Maria
(H)
Howard Alice
(H)
Howard Martine
(H)
Isabel Jones
(I)
Jandziol Andrzej
(J)
Jawad Mo
(J)
Jeans John
(J)
Jennings Jo
(J)
Julve Max
(J)
Khera Jacyntha Kaur
(KJ)
Kotecha Ami
(K)
Kulkarni Manisha
(K)
Lamont Holly
(L)
Lee Corina
(L)
Phillip Lee
(P)
Lever William
(L)
Lignos Leda Li Alex
(LL)
Liyanage Ganga
(L)
Luff Samantha
(L)
Lui Wanda
(L)
Malietzis Georgios
(M)
Margiotta Georgina
(M)
Matasova Zuzanna
(M)
McNaughton Daniel
(M)
Meduoye Ayo
(M)
Mills Hannah
(M)
Milne Alex
(M)
Morosin Marco
(M)
Morton Sarah
(M)
Murray Kenneth
(M)
Nelson Quentin
(N)
Neriman Saaman
(N)
Newell Lisa
(N)
Norman Bernard
(N)
Norton Emma
(N)
Nurdin Ben
(N)
Onuorah Catherine
(O)
Osman Leyla
(O)
O'Sullivan Catherine
(O)
Parikh Chandni
(P)
Parwez Saqib
(P)
Patil Shashank
(P)
Peroos Sherina
(P)
Pickering Elspeth
(P)
Pillay Kris
(P)
Pilling Rob
(P)
Porter-Moore Martin
(PM)
Potparic Olivera
(P)
Richardson Kate
(R)
Roa John
(R)
Roderick Eleanor
(R)
Russell Katherine
(R)
Sabharwal Atika
(S)
Samani Amee
(S)
Sedov Aleksei
(S)
Silvey Natalie
(S)
Simon Jonathan
(S)
Smellie James
(S)
Smith Rebecca-Lea
(S)
Snell Andrew
(S)
Sokhi Jagdish
(S)
Szubert Ewelina
(S)
Thomas Ben
(T)
Thornton John
(T)
Vieira Jose Lopes
(VJ)
Villapalos Jorge Leon
(VJ)
Volger Annette
(V)
Waddell Paul
(W)
Wall Josh
(W)
Wannap Kate
(W)
Ward Patrick
(W)
Wardhere Ilhan
(W)
Weigert Andrea
(W)
Westall Helen
(W)
Wilk Maria
(W)
Williams Andrew
(W)
Williams Jessica
(W)
Wynn-Jones William
(WJ)
Yentis Steve
(Y)
Young Noel
(Y)
Informations de copyright
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.