Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging

Li, Kunwei; Liu, Xueguo; Yip, Rowena; Yankelevitz, David F.; Henschke, Claudia I.; Geng, Yayuan; Fang, Yijie; Li, Wenjuan; Pan, Cunxue; Chen, Xiaojun; Qin, Ping; Zhong, Yao; Liu, Kunfeng; Li, Shaolin

doi:10.1016/j.clinimag.2021.02.003

Cited by 14 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a cut-off value of 22.6%, the percentage of pneumonia volume showed good performance (AUC of 0.868) for predicting critical illness with a sensitivity and specificity of 81.3% and 80.6%, respectively. Li et al also found that the proportion of lungs with pneumonia measured by a deep learning-based algorithm predicted COVID-19 patients who later developed severe disease ( 24 ). They suggested that a CT scan performed as early as five days after the initial onset of symptoms can be used to identify patients who may progress to severe disease.…”

Section: Discussionmentioning

confidence: 99%

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

Kang¹,

Kang²,

Seo³

et al. 2023

J Thorac Dis

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Kang¹,

Kang²,

Seo³

et al. 2023

J Thorac Dis

View full text Add to dashboard Cite

show abstract

“…A variety of quantitative techniques have been used to quantify COVID-19 infection in the early phase, ranging from simple densitometry to deep learning methods (95,96). Quantitative CT (QCT) assessment of severity of COVID-19 infection in the early phase of infection is an independent predictor of ICU admission and of mortality (97)(98)(99). QCT may also be used to assess sequential change in lung volumes and pulmonary opacity (99).…”

Section: Potential Role For Quantitative Imaging In Post-covid Lung Diseasementioning

confidence: 99%

CT of Post-Acute Lung Complications of COVID-19

et al. 2021

View full text Add to dashboard Cite

The acute course of coronavirus disease 2019 (COVID-19) is variable and ranges from asymptomatic infection to fulminant respiratory failure. Patients recovering from COVID-19 can have persistent symptoms and computed tomography (CT) abnormalities of variable severity. At 3 months after acute infection, a subset of patients will have CT abnormalities that include ground glass abnormalities (GGO) and subpleural bands with concomitant pulmonary function abnormalities. At 6 months after acute infection, some patients have persistent CT changes to include the resolution of GGOs seen in the early recovery phase and the persistence or development of changes suggestive of fibrosis such as reticulation with or without parenchymal distortion. Predictors of post-COVID lung disease include need for intensive care unit (ICU) admission, mechanical ventilation, higher inflammatory markers, longer hospital stay and a diagnosis of acute respiratory distress syndrome (ARDS). Treatments of post-COVID lung disease are being investigated with anti-fibrotic agents being investigated for the prevention of post-COVID lung fibrosis. The etiology of post-COVID lung disease may be a sequela of prolonged mechanical ventilation, COVID-induced ARDS or direct injury from the virus. Future research is needed to determine the long-term persistence of post-COVID lung disease, its impact on patients and ways to prevent or treat it.

show abstract

“…To appraise COVID-19 infection in the initial phase, different techniques are used, including densitometry and deep learning methods [ 30 , 31 ]. Early phase quantitative CT assessment of severity is an autonomous forecaster of ICU hospitalization and mortality [ [32] , [33] , [34] ]. It could as well be used to appraise serial variations in lung volumes and pulmonary opacity [ 34 ].…”

Section: Discussionmentioning

confidence: 99%