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

Data de publicació: 01/05/2022 Data Ahead of Print: 09/11/2021

Autors de IIS La Fe

• María Pilar Calvillo Batlles

  Autor

• 

  Luis Martí Bonmatí

  Autor

Autors aliens a IIS La Fe

• Cerdá-Alberich L
• Fonfría-Esparcia C
• Carreres-Ortega A
• Munoz-Nunez, C. F.
• Trilles-Olaso L

Grups d'Investigació

• Investigacion Biomédica en Imagen (GIBI 2-30)

  Investigador Principal

  Leonor Cerdá Alberich

Abstract

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.

Keywords

• Artificial intelligence; COVID-19; Chest X-Ray; Mortality; Predictive models; Prognosis