Cargando…

Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores

AIMS: To identify the most accurate early warning score (EWS) for predicting an adverse outcome in COVID-19 patients admitted to the emergency department (ED). METHODS: In adult consecutive patients admitted (March 1-April 15, 2020) to the ED of a major referral centre for COVID-19, we retrospective...

Descripción completa

Detalles Bibliográficos
Autores principales: Covino, Marcello, Sandroni, Claudio, Santoro, Michele, Sabia, Luca, Simeoni, Benedetta, Bocci, Maria Grazia, Ojetti, Veronica, Candelli, Marcello, Antonelli, Massimo, Gasbarrini, Antonio, Franceschi, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480278/
https://www.ncbi.nlm.nih.gov/pubmed/32918985
http://dx.doi.org/10.1016/j.resuscitation.2020.08.124
_version_ 1783580393895624704
author Covino, Marcello
Sandroni, Claudio
Santoro, Michele
Sabia, Luca
Simeoni, Benedetta
Bocci, Maria Grazia
Ojetti, Veronica
Candelli, Marcello
Antonelli, Massimo
Gasbarrini, Antonio
Franceschi, Francesco
author_facet Covino, Marcello
Sandroni, Claudio
Santoro, Michele
Sabia, Luca
Simeoni, Benedetta
Bocci, Maria Grazia
Ojetti, Veronica
Candelli, Marcello
Antonelli, Massimo
Gasbarrini, Antonio
Franceschi, Francesco
author_sort Covino, Marcello
collection PubMed
description AIMS: To identify the most accurate early warning score (EWS) for predicting an adverse outcome in COVID-19 patients admitted to the emergency department (ED). METHODS: In adult consecutive patients admitted (March 1-April 15, 2020) to the ED of a major referral centre for COVID-19, we retrospectively calculated NEWS, NEWS2, NEWS-C, MEWS, qSOFA, and REMS from physiological variables measured on arrival. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and the area under the receiver operating characteristic (AUROC) curve of each EWS for predicting admission to the intensive care unit (ICU) and death at 48 h and 7 days were calculated. RESULTS: We included 334 patients (119 [35.6%] females, median age 66 [54-78] years). At 7 days, the rates of ICU admission and death were 56/334 (17%) and 26/334 (7.8%), respectively. NEWS was the most accurate predictor of ICU admission within 7 days (AUROC 0.783 [95% CI, 0.735-0.826]; sensitivity 71.4 [57.8-82.7]%; NPV 93.1 [89.8-95.3]%), while REMS was the most accurate predictor of death within 7 days (AUROC 0.823 [0.778–0.863]; sensitivity 96.1 [80.4-99.9]%; NPV 99.4[96.2–99.9]%). Similar results were observed for ICU admission and death at 48 h. NEWS and REMS were as accurate as the triage system used in our ED. MEWS and qSOFA had the lowest overall accuracy for both outcomes. CONCLUSION: In our single-centre cohort of COVID-19 patients, NEWS and REMS measured on ED arrival were the most sensitive predictors of 7-day ICU admission or death. EWS could be useful to identify patients with low risk of clinical deterioration.
format Online
Article
Text
id pubmed-7480278
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-74802782020-09-09 Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores Covino, Marcello Sandroni, Claudio Santoro, Michele Sabia, Luca Simeoni, Benedetta Bocci, Maria Grazia Ojetti, Veronica Candelli, Marcello Antonelli, Massimo Gasbarrini, Antonio Franceschi, Francesco Resuscitation Clinical Paper AIMS: To identify the most accurate early warning score (EWS) for predicting an adverse outcome in COVID-19 patients admitted to the emergency department (ED). METHODS: In adult consecutive patients admitted (March 1-April 15, 2020) to the ED of a major referral centre for COVID-19, we retrospectively calculated NEWS, NEWS2, NEWS-C, MEWS, qSOFA, and REMS from physiological variables measured on arrival. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and the area under the receiver operating characteristic (AUROC) curve of each EWS for predicting admission to the intensive care unit (ICU) and death at 48 h and 7 days were calculated. RESULTS: We included 334 patients (119 [35.6%] females, median age 66 [54-78] years). At 7 days, the rates of ICU admission and death were 56/334 (17%) and 26/334 (7.8%), respectively. NEWS was the most accurate predictor of ICU admission within 7 days (AUROC 0.783 [95% CI, 0.735-0.826]; sensitivity 71.4 [57.8-82.7]%; NPV 93.1 [89.8-95.3]%), while REMS was the most accurate predictor of death within 7 days (AUROC 0.823 [0.778–0.863]; sensitivity 96.1 [80.4-99.9]%; NPV 99.4[96.2–99.9]%). Similar results were observed for ICU admission and death at 48 h. NEWS and REMS were as accurate as the triage system used in our ED. MEWS and qSOFA had the lowest overall accuracy for both outcomes. CONCLUSION: In our single-centre cohort of COVID-19 patients, NEWS and REMS measured on ED arrival were the most sensitive predictors of 7-day ICU admission or death. EWS could be useful to identify patients with low risk of clinical deterioration. Elsevier B.V. 2020-11 2020-09-09 /pmc/articles/PMC7480278/ /pubmed/32918985 http://dx.doi.org/10.1016/j.resuscitation.2020.08.124 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Clinical Paper
Covino, Marcello
Sandroni, Claudio
Santoro, Michele
Sabia, Luca
Simeoni, Benedetta
Bocci, Maria Grazia
Ojetti, Veronica
Candelli, Marcello
Antonelli, Massimo
Gasbarrini, Antonio
Franceschi, Francesco
Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title_full Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title_fullStr Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title_full_unstemmed Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title_short Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores
title_sort predicting intensive care unit admission and death for covid-19 patients in the emergency department using early warning scores
topic Clinical Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480278/
https://www.ncbi.nlm.nih.gov/pubmed/32918985
http://dx.doi.org/10.1016/j.resuscitation.2020.08.124
work_keys_str_mv AT covinomarcello predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT sandroniclaudio predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT santoromichele predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT sabialuca predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT simeonibenedetta predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT boccimariagrazia predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT ojettiveronica predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT candellimarcello predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT antonellimassimo predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT gasbarriniantonio predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores
AT franceschifrancesco predictingintensivecareunitadmissionanddeathforcovid19patientsintheemergencydepartmentusingearlywarningscores