tetano
Editor, Senior Moderator
J Thorac Dis
. 2023 Mar 31;15(3):1506-1516.
doi: 10.21037/jtd-22-1076. Epub 2023 Mar 9.
Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features
Jieun Kang[SUP] #[/SUP][SUP] 1 [/SUP], Jiyeon Kang[SUP] #[/SUP][SUP] 1 [/SUP], Woo Jung Seo[SUP] 1 [/SUP], So Hee Park[SUP] 1 [/SUP], Hyung Koo Kang[SUP] 1 [/SUP], Hye Kyeong Park[SUP] 1 [/SUP], JongHoon Hyun[SUP] 2 [/SUP], Je Eun Song[SUP] 2 [/SUP], Yee Gyung Kwak[SUP] 2 [/SUP], Ki Hwan Kim[SUP] 3 [/SUP], Yeon Soo Kim[SUP] 4 [/SUP], Sung-Soon Lee[SUP] 1 [/SUP], Hyeon-Kyoung Koo[SUP] 1 [/SUP]
Affiliations
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.
Keywords: Coronavirus disease 2019 (COVID-19); machine-learning; prediction model; quantitative computed tomography (quantitative CT); respiratory failure.
. 2023 Mar 31;15(3):1506-1516.
doi: 10.21037/jtd-22-1076. Epub 2023 Mar 9.
Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features
Jieun Kang[SUP] #[/SUP][SUP] 1 [/SUP], Jiyeon Kang[SUP] #[/SUP][SUP] 1 [/SUP], Woo Jung Seo[SUP] 1 [/SUP], So Hee Park[SUP] 1 [/SUP], Hyung Koo Kang[SUP] 1 [/SUP], Hye Kyeong Park[SUP] 1 [/SUP], JongHoon Hyun[SUP] 2 [/SUP], Je Eun Song[SUP] 2 [/SUP], Yee Gyung Kwak[SUP] 2 [/SUP], Ki Hwan Kim[SUP] 3 [/SUP], Yeon Soo Kim[SUP] 4 [/SUP], Sung-Soon Lee[SUP] 1 [/SUP], Hyeon-Kyoung Koo[SUP] 1 [/SUP]
Affiliations
- PMID: 37065603
- PMCID: PMC10089866
- DOI: 10.21037/jtd-22-1076
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.
Keywords: Coronavirus disease 2019 (COVID-19); machine-learning; prediction model; quantitative computed tomography (quantitative CT); respiratory failure.