Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea

Factors Associated With Radiological Lung Growth Rate After Lobectomy in Patients With Lung Cancer

INTRODUCTION

This study is a retrospective study. This study aims to explore the association between lobectomy in lung cancer patients and subsequent compensatory lung growth (CLG), and to identify factors that may be associated with variations in CLG.

METHODS

207 lung cancer patients who underwent lobectomy at Yunnan Cancer Hospital between January 2020 and December 2020. All patients had stage IA primary lung cancer and were performed by the same surgical team. And computed tomography examinations were performed before and 1 y postoperatively. Based on computed tomography images, the volume of each lung lobe was measured using computer software and manual, the radiological lung weight was calculated. And multiple linear regressions were used to analyze the factors related to the increase in postoperative lung weight.

RESULTS

One year after lobectomy, the radiological lung weight increased by an average of 112.4 ± 20.8%. Smoking history, number of resected lung segments, preoperative low attenuation volume, intraoperative arterial oxygen partial pressure/fraction of inspired oxygen ratio and postoperative visual analog scale scores at 48 h were significantly associated with postoperative radiological lung weight gain.

CONCLUSIONS

Our results suggest that CLG have occurred after lobectomy in adults. In addition, anesthetists should maintain high arterial oxygen partial pressure/fraction of inspired oxygen ratio during one-lung ventilation and improve acute postoperative pain to benefit CLG.

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19

PURPOSE

As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19.

MATERIALS AND METHODS

We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed.

RESULTS

The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model.

CONCLUSIONS

The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs

PURPOSE

To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically.

MATERIALS AND METHODS

This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent.

RESULTS

GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively.

CONCLUSION

The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients

The aim of this study was to compare whole lung CT density histograms to predict critical illness outcome and hospital length of stay in a cohort of 80 COVID-19 patients. CT chest images on segmented lungs were retrospectively analyzed. Functional Principal Component Analysis (FPCA) was used to find the main modes of variations on CT density histograms. CT density features, the CT severity score, the COVID-GRAM score and the patient clinical data were assessed for predicting the patient outcome using logistic regression models and survival analysis. ROC analysis predictors of critically ill status: 87.5th percentile CT density (Q875)—AUC 0.88 95% CI (0.79 0.94), F1-CT—AUC 0.87 (0.77 0.93) Standard Deviation (SD-CT)—AUC 0.86 (0.73, 0.93). Multivariate models combining CT-density predictors and Neutrophil–Lymphocyte Ratio showed the highest accuracy. SD-CT, Q875 and F1 score were significant predictors of hospital length of stay (LOS) while controlling for hospital death using competing risks models. Moreover, two multivariate Fine-Gray regression models combining the clinical variables: age, NLR, Contrast CT factor with either Q875 or F1 CT-density predictors revealed significant effects for the prediction of LOS incidence in presence of a competing risk (death) and acceptable predictive performances (Bootstrapped C-index 0.74 [0.70 0.78]).

Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study

OBJECTIVE

We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images.

METHODS

This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115).

RESULTS

In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035).

CONCLUSIONS

Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

COVID-19 in patients with cancer: can baseline radiologic severity and early evolution predict clinical outcomes?

OBJECTIVE

To determine whether the degree of parenchymal involvement on chest radiograph (CXR) at the time of COVID-19 diagnosis and its early radiologic evolution can predict adverse events including hospitalization, intubation, and death in patients with cancer.

METHODS

Retrospective study of 627 COVID-19-positive patients between March and April 2020, of which 248 had baseline CXR within 72 h of diagnosis and 64 patients had follow-up wihtin72 h. CXRs were classified as abnormal (i.e., radiologic findings suggestive of COVID-19 infection were noted), normal, or indeterminate. Baseline and follow-up severity scores were calculated based on lung regions in abnormal CXRs. Statistical analysis was performed to determine associations between abnormal CXR or severity score with adverse events.

RESULTS

Of 248 patients (median age = 65) with a baseline CXR, 172/248 (69%) had an abnormal baseline study, which was associated with hospitalization (p < 0.001), intubation (p = 0.001), and death (p = 0.005). For patients with solid neoplasms, when adjusted for stage, it was associated with hospitalization (p = 0.0002), intubation (p = 0.019), and death (p = 0.03). The median baseline severity score was 3 (range = 1-10); the greater the score, the higher the likelihood of adverse outcome (p < 0.003 for all). A baseline severity score > 9 predicted > 50% probability of intubation and a score of ≥ 10 predicted > 50% of probability of death. The baseline severity score was not correlated with cancer-related treatment. Early radiologic progression was not correlated with hospitalization, intubation, or death.

CONCLUSION

The degree of parenchymal involvement on CXR within 72 h of COVID-19 diagnosis is associated with adverse outcomes in patients with cancer.

KEY POINTS

• In patients with cancer, the presence and severity of radiologic manifestation of COVID-19 on chest radiographs within 72 h of COVID-19 diagnosis are associated with hospitalization, intubation, and death. • Early radiologic progression on chest radiographs is not correlated with adverse outcomes.

Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.. [PMID: 34671777 PMCID: PMC8528085 DOI: 10.1101/2021.10.11.21264709]

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Value of quantitative analysis in lung computed tomography in patients severely ill with COVID-19

Introduction

Quantitative computed tomography (QCT) is used to objectively assess the degree of parenchymal impairment in COVID-19 pneumonia.

Materials and methods

Retrospective study on 61 COVID-19 patients (severe and non-severe; 33 men, age 63+/-15 years) who underwent a CT scan due to tachypnea, dyspnoea or desaturation. QCT was performed using VCAR software. Patients’ clinical data was collected, including laboratory results and oxygenation support. The optimal cut-off point for CT parameters for predicting death and respiratory support was performed by maximizing the Youden Index in a receiver operating characteristic (ROC) curve analysis.

Results

The analysis revealed significantly greater progression of changes: ground-glass opacities (GGO) (31,42% v 13,89%, p<0.001), consolidation (11,85% v 3,32%, p<0.001) in patients with severe disease compared to non-severe disease. Five lobes were involved in all patients with severe disease. In non-severe patients, a positive correlation was found between severity of GGO, consolidation and emphysema and sex, tachypnea, chest x-ray (CXR) score on admission and laboratory parameters: CRP, D-dimer, ALT, lymphocyte count and lymphocyte/neutrophil ratio. In the group of severe patients, a correlation was found between sex, creatinine level and death. ROC analysis on death prediction was used to establish the cut-off point for GGO at 24.3% (AUC 0.8878, 95% CI 0.7889–0.9866; sensitivity 91.7%, specificity 75.5%), 5.6% for consolidation (AUC 0.7466, 95% CI 0.6009–0.8923; sensitivity 83.3%, specificity 59.2%), and 37.8% for total (GGO+consolidation) (AUC 0.8622, 95% CI 0.7525–0.972; sensitivity 75%, specificity 83.7%). The cut-off point for predicting respiratory support was established for GGO at 18.7% (AUC 0.7611, 95% CI 0.6268–0.8954; sensitivity 87.5%, specificity 64.4%), consolidation at 3.88% (AUC 0.7438, 95% CI 0.6146–0.8729; sensitivity 100%, specificity 46.7%), and total at 23.5% (AUC 0.7931, 95% CI 0.673–0.9131; sensitivity 93.8%, specificity 57.8%).

Conclusion

QCT is a good diagnostic tool which facilitates decision-making regarding intensification of oxygen support and transfer to an intensive care unit in patients severely ill with COVID-19 pneumonia. QCT can make an independent and simple screening tool to assess the risk of death, regardless of clinical symptoms. Usefulness of QCT to predict the risk of death is higher than to assess the indications for respiratory support.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

Objective

To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.

Materials and Methods

Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.

Results

Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively.

Conclusion

CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Prognostic Implications of CT Feature Analysis in Patients with COVID-19: a Nationwide Cohort Study

BACKGROUND

Few studies have classified chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) and analyzed their correlations with prognosis. The present study aimed to evaluate retrospectively the clinical and chest CT findings of COVID-19 and to analyze CT findings and determine their relationships with clinical severity.

METHODS

Chest CT and clinical features of 271 COVID-19 patients were assessed. The presence of CT findings and distribution of parenchymal abnormalities were evaluated, and CT patterns were classified as bronchopneumonia, organizing pneumonia (OP), or diffuse alveolar damage (DAD). Total extents were assessed using a visual scoring system and artificial intelligence software. Patients were allocated to two groups based on clinical outcomes, that is, to a severe group (requiring O₂ therapy or mechanical ventilation, n = 55) or a mild group (not requiring O₂ therapy or mechanical ventilation, n = 216). Clinical and CT features of these two groups were compared and univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors.

RESULTS

Age, lymphocyte count, levels of C-reactive protein, and procalcitonin were significantly different in the two groups. Forty-five of the 271 patients had normal chest CT findings. The most common CT findings among the remaining 226 patients were ground-glass opacity (98%), followed by consolidation (53%). CT findings were classified as OP (93%), DAD (4%), or bronchopneumonia (3%) and all nine patients with DAD pattern were included in the severe group. Uivariate and multivariate analyses showed an elevated procalcitonin (odds ratio [OR], 2.521; 95% confidence interval [CI], 1.001-6.303, P = 0.048), and higher visual CT scores (OR, 1.137; 95% CI, 1.042-1.236; P = 0.003) or higher total extent by AI measurement (OR, 1.048; 95% CI, 1.020-1.076; P < 0.001) were significantly associated with a severe clinical course.

CONCLUSION

CT findings of COVID-19 pneumonia can be classified into OP, DAD, or bronchopneumonia patterns and all patients with DAD pattern were included in severe group. Elevated inflammatory markers and higher CT scores were found to be significant predictors of poor prognosis in patients with COVID-19 pneumonia.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Objective

To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

Materials and Methods

All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.

Results

While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88).

Conclusion

Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Visual and quantitative assessment of COVID-19 pneumonia on chest CT: The relationship with disease severity and clinical findings

BACKGROUND

Lungs are the primary organ of involvement of COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality.

AIM

We aimed to evaluate the visual and quantitative pneumonia severity on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings.

METHODS

We retrospectively evaluated adult COVID-19 patients who underwent chest CT, clinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between variables. The Pearson correlation test analyzed the correlation between variables. The diagnostic performance of the variables was assessed using receiver operating characteristic (ROC) analysis was used.

RESULTS

The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with disease severity (p<0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r=730,p<0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization.

CONCLUSION

The VSQS and QCT scores can help manage the COVID-19 and predict the duration of hospitalization.

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019

BACKGROUND

The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19.

METHODS

The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital.

RESULTS

The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11-97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs.

CONCLUSION

The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

An Assessment of the Novel COVISTRESS Questionnaire: COVID-19 Impact on Physical Activity, Sedentary Action and Psychological Emotion

Globally the COVID-19 pandemic outbreak has triggered an economic downturn and a rise in unemployment. As a result, global communities have had to face physical, health, psychological and socio-economical related stressors. The purpose of this study was to assess and report the impact of isolation and effect of coronavirus on selected psychological correlates associated with emotions. Following ethical approval, a mixed methods observational study was conducted using the validated COVISTRESS questionnaire. Two observational study scenarios were evaluated namely "Prior" to the COVID-19 outbreak and "Currently", i.e., during the COVID-19 pandemic. 10,121 participants from 67 countries completed the COVISTRESS questionnaire. From the questionnaire responses only questions that covered the participant's occupation; sociodemographic details, isolation and impact of coronavirus were selected. Further analyses were performed on output measures that included leisure time, physical activity, sedentary time and emotions. All output measures were evaluated using the Visual Analogue Scale (VAS) with an intensity ranging from 0-100. Descriptive statistics, Wilcoxon signed-rank test and Spearman correlational analysis were applied to the leisure time, physical activity, sedentary time and emotional feeling datasets; p = 0.05 was set as the significance level. Both males and females displayed similar output measures. The Wilcoxon signed rank test showed significant differences with respect to "Prior" COVID-19 and "Currently" for sedentary activity (Z = -40.462, p < 0.001), physical activity (Z = -30.751, p < 0.001) and all other emotional feeling output measures. A moderate correlation between "Prior" COVID-19 and "Currently" was observed among the Males (r = 0.720) in comparison to the Females (r = 0.639) for sedentary activity while weaker correlations (r < 0.253) were observed for physical activity and emotional feeling measurements, respectively. Our study reported incremental differences in the physical and psychological output measures reported, i.e., "Prior" COVID-19 and "Currently". "Prior" COVID-19 and "Currently" participants increased their sedentary habits by 2.98%, and the level of physical activity reduced by 2.42%, depression levels increased by 21.62%, anxiety levels increased by 16.71%, and stress levels increased by 21.8%. There were no correlations (r) between leisure, physical activity and sedentary action (i.e., "Prior" = -0.071; "Currently" = -0.097); no correlations (r) between leisure physical activity and emotion (i.e., -0.071 > r > 0.081) for "Prior"; and poor correlations (r) between leisure, physical activity and sedentary action (i.e., -0.078 > r > 0.167) for "Current". The correlations (r) between sedentary action and emotion for "Prior" and "Currently" were (-0.100 > r > 0.075) and (-0.040 > r > 0.041) respectively. The findings presented here indicate that the COVISTRESS project has created awareness in relation to the physical and psychological impact resulting from the COVID-19 pandemic. The findings have also highlighted individual distress caused by COVID-19 and associated health consequences for the global community.

Predictive criteria of severe cases in COVID-19 patients of early stage: A retrospective observational study

Background

Patients with coronavirus disease 2019 (COVID‐19) often suffer sudden deterioration of disease around 1‐2 weeks after onset. Once the disease progressed to severe phase, clinical prognosis of patients will significantly deteriorate.

Methods

This was a multicenter retrospective study on patients of all adult inpatients (≥18 years old) from Tianyou Hospital (Wuhan, China) and the Fourth Affiliated Hospital, Zhejiang University School of Medicine. All 139 patients had laboratory‐confirmed COVID‐19 in their early stage, which is defined as within 7 days of clinical symptoms. Univariate and multivariate logistic regression models were used to determine the predictive factors in the early detection of patients who may subsequently develop into severe cases.

Results

Multivariable logistic regression analysis showed that the higher level of hypersensitivity C‐reactive protein (OR = 4.77, 95% CI:1.92‐11.87, P = .001), elevated alanine aminotransferase (OR = 6.87, 95%CI:1.56‐30.21, P = .011), and chronic comorbidities (OR = 11.48, 95% CI:4.44‐29.66, P < .001) are the determining risk factors for the progression into severe pneumonia in COVID‐19 patients.

Conclusion

Early COVID‐19 patients with chronic comorbidities, elevated hs‐CRP or elevated ALT are significantly more likely to develop severe pneumonia as the disease progresses. These risk factors may facilitate the early diagnosis of critical patients in clinical practice.