Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.

ABSTRACT:

Background

We aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19).

Methods

This was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest mode

SUBMITTER: Kang J 

PROVIDER: S-EPMC10089866 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature