Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19

PURPOSE: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe...

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Detalles Bibliográficos
Autores principales: Jung, Hae-Min, Yang, Rochelle, Gefter, Warren B., Ghesu, Florin C., Mailhe, Boris, Mansoor, Awais, Grbic, Sasa, Comaniciu, Dorin, Vogt, Sebastian, Mortani Barbosa, Eduardo J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
Image Processing
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203354/
https://www.ncbi.nlm.nih.gov/pubmed/35721308
http://dx.doi.org/10.1117/1.JMI.9.3.034003
Descripción
Sumario:PURPOSE: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. APPROACH: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). RESULTS: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ([Formula: see text] [0.58 to 0.76]; [Formula: see text]). Model II performed the best ([Formula: see text] [0.71 to 0.86]; [Formula: see text]). Model III was equivalent ([Formula: see text] [0.67 to 0.84]; [Formula: see text]). Both models II and III outperformed model I ([Formula: see text] [0.02 to 0.19], [Formula: see text]; [Formula: see text] [0.01 to 0.15], [Formula: see text] , respectively). Model II and III results did not change significantly when POv was replaced by POa. CONCLUSIONS: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

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