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 models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation. Results A total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively. Conclusions Our prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.

Oscillometry Longitudinal Data on COVID-19 Acute Respiratory Distress Syndrome Treated with Non-Invasive Respiratory Support

“…The CT scan data showed small percentages of collapsed tissue, consistent with previous findings in COVID-19 patients undergoing NI-OS [42] and different types of oxygen support [43][44][45]. A higher percentage of collapsed tissue was reported in intubated COVID-19 ARDS patients [46], but it was still lower than the percentage reported in non-COVID-19 ARDS patients treated with invasive ventilation [47].…”

Quantitative Analysis for Lung Disease on Thin-Section CT

“…In initial studies, the extent was evaluated visually [ 130 ], but many dedicated software packages have been developed, including some using artificial intelligence [ 131 ]. Densitometric analysis has also been used to evaluate the extent of pulmonary lesions [ 132 , 133 , 134 ]. Quantitative CT results have been widely used in clinical settings to determine the appropriate level of care and management strategies, including the need for hospitalization or intensive care.…”

Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning

“…The majority of confirmed cases are classified as mild, whereas a few require hospitalization or even lead to respiratory failure and mortality. 2 , 3 Important for providing appropriate management and follow-up assessments while maximizing the use of limited resources is the timely identification of high-risk patients. 3 It has been established that chest computed tomography (CT) is the imaging modality of choice for rapid identification and monitoring of the disease course in COVID-19 pneumonia.…”

“… 2 , 3 Important for providing appropriate management and follow-up assessments while maximizing the use of limited resources is the timely identification of high-risk patients. 3 It has been established that chest computed tomography (CT) is the imaging modality of choice for rapid identification and monitoring of the disease course in COVID-19 pneumonia. 4 Comparatively to reverse transcription polymerase chain reaction (RT-PCR), chest CT has an extremely high sensitivity for identifying COVID-19 pneumonia.…”