Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.

<h4>Background and aims</h4>Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients.<h4>Methods</h4>Retrospective study of 430 patients admitted...

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Main Authors: Mar Riveiro-Barciela, Moisés Labrador-Horrillo, Laura Camps-Relats, Didac González-Sans, Meritxell Ventura-Cots, María Terrones-Peinador, Andrea Nuñez-Conde, Mónica Martínez-Gallo, Manuel Hernández, Andrés Antón, Antonio González, Ricardo Pujol-Borrell, Fernando Martínez-Valle
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0244627
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spelling doaj-ff1b70cade2549ca8f71e6d9471a99f52021-03-04T12:45:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024462710.1371/journal.pone.0244627Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.Mar Riveiro-BarcielaMoisés Labrador-HorrilloLaura Camps-RelatsDidac González-SansMeritxell Ventura-CotsMaría Terrones-PeinadorAndrea Nuñez-CondeMónica Martínez-GalloManuel HernándezAndrés AntónAntonio GonzálezRicardo Pujol-BorrellFernando Martínez-Valle<h4>Background and aims</h4>Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients.<h4>Methods</h4>Retrospective study of 430 patients admitted in Vall d'Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another with also follow-up analytical results. Calibration was performed by a 1000-bootstrap replication model.<h4>Results</h4>249 were male, mean age 57.9 years. Overall, 135 (31.4%) required high-flow-oxygen-support. The baseline predictive model showed a ROC of 0.800 based on: SpO2/FiO2 (adjusted Hazard Ratio-aHR = 8), chest x-ray (aHR = 4), prior immunosuppressive therapy (aHR = 4), obesity (aHR = 2), IL-6 (aHR = 2), platelets (aHR = 0.5). The cut-off of 11 presented a specificity of 94.8%. The second model included changes on the analytical parameters: ferritin (aHR = 7.5 if ≥200ng/mL) and IL-6 (aHR = 18 if ≥64pg/mL) plus chest x-ray (aHR = 2) showing a ROC of 0.877. The cut-off of 12 exhibited a negative predictive value of 92%.<h4>Conclusions</h4>SpO2/FiO2 and chest x-ray on admission or changes on inflammatory parameters as IL-6 and ferritin allow us early identification of COVID-19 patients at risk of high-flow-oxygen-support that may benefit from a more intensive disease management.https://doi.org/10.1371/journal.pone.0244627
collection DOAJ
language English
format Article
sources DOAJ
author Mar Riveiro-Barciela
Moisés Labrador-Horrillo
Laura Camps-Relats
Didac González-Sans
Meritxell Ventura-Cots
María Terrones-Peinador
Andrea Nuñez-Conde
Mónica Martínez-Gallo
Manuel Hernández
Andrés Antón
Antonio González
Ricardo Pujol-Borrell
Fernando Martínez-Valle
spellingShingle Mar Riveiro-Barciela
Moisés Labrador-Horrillo
Laura Camps-Relats
Didac González-Sans
Meritxell Ventura-Cots
María Terrones-Peinador
Andrea Nuñez-Conde
Mónica Martínez-Gallo
Manuel Hernández
Andrés Antón
Antonio González
Ricardo Pujol-Borrell
Fernando Martínez-Valle
Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
PLoS ONE
author_facet Mar Riveiro-Barciela
Moisés Labrador-Horrillo
Laura Camps-Relats
Didac González-Sans
Meritxell Ventura-Cots
María Terrones-Peinador
Andrea Nuñez-Conde
Mónica Martínez-Gallo
Manuel Hernández
Andrés Antón
Antonio González
Ricardo Pujol-Borrell
Fernando Martínez-Valle
author_sort Mar Riveiro-Barciela
title Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
title_short Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
title_full Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
title_fullStr Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
title_full_unstemmed Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.
title_sort simple predictive models identify patients with covid-19 pneumonia and poor prognosis.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description <h4>Background and aims</h4>Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients.<h4>Methods</h4>Retrospective study of 430 patients admitted in Vall d'Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another with also follow-up analytical results. Calibration was performed by a 1000-bootstrap replication model.<h4>Results</h4>249 were male, mean age 57.9 years. Overall, 135 (31.4%) required high-flow-oxygen-support. The baseline predictive model showed a ROC of 0.800 based on: SpO2/FiO2 (adjusted Hazard Ratio-aHR = 8), chest x-ray (aHR = 4), prior immunosuppressive therapy (aHR = 4), obesity (aHR = 2), IL-6 (aHR = 2), platelets (aHR = 0.5). The cut-off of 11 presented a specificity of 94.8%. The second model included changes on the analytical parameters: ferritin (aHR = 7.5 if ≥200ng/mL) and IL-6 (aHR = 18 if ≥64pg/mL) plus chest x-ray (aHR = 2) showing a ROC of 0.877. The cut-off of 12 exhibited a negative predictive value of 92%.<h4>Conclusions</h4>SpO2/FiO2 and chest x-ray on admission or changes on inflammatory parameters as IL-6 and ferritin allow us early identification of COVID-19 patients at risk of high-flow-oxygen-support that may benefit from a more intensive disease management.
url https://doi.org/10.1371/journal.pone.0244627
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