[Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray].

Autor:	Calvillo-Batllés P; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España., Cerdá-Alberich L; Grupo de Investigación Biomédica en Imagen (GIBI2), Instituto de Investigación Sanitaria La Fe, Valencia, España., Fonfría-Esparcia C; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España., Carreres-Ortega A; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España., Muñoz-Núñez CF; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España., Trilles-Olaso L; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España., Martí-Bonmatí L; Servicio de Radiología, Hospital Universitario y Politécnico La Fe, Valencia, España.; Grupo de Investigación Biomédica en Imagen (GIBI2), Instituto de Investigación Sanitaria La Fe, Valencia, España.
Jazyk:	Spanish; Castilian
Zdroj:	Radiologia [Radiologia] 2022 May-Jun; Vol. 64 (3), pp. 214-227. Date of Electronic Publication: 2021 Nov 09.
DOI:	10.1016/j.rx.2021.09.011
Abstrakt:	Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model. Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics. Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour. (© 2021 SERAM. Published by Elsevier España, S.L.U. All rights reserved.)
Databáze:	MEDLINE
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