RT Journal Article
SR Electronic
T1 A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest X-Rays and clinical variables
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00010-2022
DO 10.1183/23120541.00010-2022
A1 Nicolás Munera
A1 Esteban Garcia-Gallo
A1 Álvaro Gonzalez
A1 José Zea
A1 Yuli V. Fuentes
A1 Cristian Serrano
A1 Alejandra Ruiz-Cuartas
A1 Alejandro Rodriguez
A1 Luis F. Reyes
YR 2022
UL http://openres.ersjournals.com/content/early/2022/04/21/23120541.00010-2022.abstract
AB BACKGROUND Patients with COVID-19 could develop severe disease requiring admission to the Intensive Care Unit (ICU). This manuscript presents a novel method that predicts whether a patient will need admission to the ICU and assess the risk of in-hospital mortality by training a deep learning model that combines a set of clinical variables and features in the Chest-X-Rays.METHODS This was a prospective diagnostic test study. Patients with confirmed SARS-CoV-2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for Chest-X-ray images using an artificial intelligence (AI) tool and a Random Forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.RESULTS A total of 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, the fraction of inspired oxygen - FiO2 on admission, dyspnoea on admission, and obesity. Moreover, the variables associated with hospital mortality were age, the fraction of inspired oxygen - FiO2 on admission, and dyspnoea. When implementing the AI model to interpret the Chest-X-rays and the clinical variable identified by random forest, we developed a model that accurately predicts ICU admission (AUC:0.92±0.04) and hospital mortality (AUC:0.81±0.06) in patients with confirmed COVID-19.CONCLUSIONS This automated Chest-X-ray interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 that might require admission to the ICU.FootnotesThis manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.Conflict of interest: All authors have no conflict of interest