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{Heldt:2021:10.1038/s41598-021-83784-y,
author = {Heldt, FS and Vizcaychipi, MP and Peacock, S and Cinelli, M and McLachlan, L and Andreotti, F and Jovanovic, S and Durichen, R and Lipunova, N and Fletcher, RA and Hancock, A and McCarthy, A and Pointon, RA and Brown, A and Eaton, J and Liddi, R and Mackillop, L and Tarassenko, L and Khan, RT},
doi = {10.1038/s41598-021-83784-y},
journal = {Scientific Reports},
pages = {1--13},
title = {Early risk assessment for COVID-19 patients from emergency department data using machine learning},
url = {http://dx.doi.org/10.1038/s41598-021-83784-y},
volume = {11},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients’ initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42–0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient’s oxyg
AU  - Heldt,FS
AU  - Vizcaychipi,MP
AU  - Peacock,S
AU  - Cinelli,M
AU  - McLachlan,L
AU  - Andreotti,F
AU  - Jovanovic,S
AU  - Durichen,R
AU  - Lipunova,N
AU  - Fletcher,RA
AU  - Hancock,A
AU  - McCarthy,A
AU  - Pointon,RA
AU  - Brown,A
AU  - Eaton,J
AU  - Liddi,R
AU  - Mackillop,L
AU  - Tarassenko,L
AU  - Khan,RT
DO  - 10.1038/s41598-021-83784-y
EP  - 13
PY  - 2021///
SN  - 2045-2322
SP  - 1
TI  - Early risk assessment for COVID-19 patients from emergency department data using machine learning
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-021-83784-y
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000621416400073&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.nature.com/articles/s41598-021-83784-y
UR  - http://hdl.handle.net/10044/1/91478
VL  - 11
ER  -
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