The effect of CT-based cardiothoracic ratio on survival of COVID-19 patients

Objectives: We aimed to investigate the relationship between computed tomography (CT)- based cardiothoracic ratio (CTR) with mortality rates of COVID-19 patients. Method(s): Our study was a single-center retrospective analysis of 484 patients (aged >= 18) who were admitted to our hospital's emergency department. We included only laboratory-confirmed COVID-19 patients who underwent chest CT. Data of demographic information, laboratory findings, survivals, and chest CT imaging findings were recorded. The radiologist calculated CTR by dividing the greatest transverse cardiac diameter by the greatest transverse thoracic diameter on the initial chest CT. Cardiomegaly was defined if "CTR > 0.5". Result(s): Thirty (6.2%) patients were treated as outpatients, and 135/484 (%27.9) patients were treated in the intensive care unit (ICU). A total of 147 /484 (30.4%) patients died. We found a statistical association between cardiomegaly with mortality rates (p < 0.001) and ICU admission (p = 0.008). In multivariate analysis, older age was 1.07-fold (p < 0.001), cardiomegaly 1.75-fold (p = 0.015), history of cerebrovascular diseases 2.929-fold (p = 0.018), and elevated serum LDH level 1.003-fold (p = 0.011) associated with higher risks of mortality. Conclusion(s): Since the presence of cardiomegaly on chest CT is associated with a worse prognosis for COVID-19 patients, more caution should be exercised in the evaluation, follow-up, and treatment of COVID-19 patients with cardiomegaly.Copyright © 2023 by Prusa Medical Publishing.

Pneumopericardium in COVID-19 Infection

Pneumopericardium is a rare medical condition that occurs following trauma, surgery, or other medical interventions. The presence of pneumopericardium after COVID-19 pneumonia has been reported in some cases, and it has been explained that most cases could be self-limited. Here, we describe a 51-year-old man afflicted by pneumopericardium with COVID-19 infection. The patient had pneumopericardium and massive pericardial effusions, necessitating surgical strategies such as pericardial windows. This case highlights the potential severity of COVID-19. We also suggest that cardiologists pay attention to the possibility of pneumopericardium in cases with COVID-19 infection. © 2023, Iranian Heart Association. All rights reserved.

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best treatment opportunity. Thus, we proposed a deep learning-based method using chest X-ray images of the 28th day of oxygen inhalation for the early severity prediction of bronchopulmonary dysplasia in clinic. METHODS: We first adopted a two-step lung field extraction method by combining digital image processing and human-computer interaction to form the one-to-one corresponding image and label. The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Therein, Six-Point cardiothoracic ratio measurement algorithm based on corner detection was designed for the analysis of heart development; and the transfer learning of deep convolutional neural network models were used for the early prediction of clinical severities. RESULTS: The dice and cross-entropy loss value of the training of XSEG-Net network reached 0.9794 and 0.0146. The dice, volumetric overlap error, relative volume difference, precision, and recall were used to evaluate the trained model in testing set with the result being 98.43 ± 0.39%, 0.49 ± 0.35%, 0.49 ± 0.35%, 98.67 ± 0.40%, and 98.20 ± 0.47%, respectively. The errors between the Six-Point cardiothoracic ratio measurement method and the gold standard were 0.0122 ± 0.0084. The deep convolutional neural network model based on VGGNet had the promising prediction performance, with the accuracy, precision, sensitivity, specificity, and F1 score reaching 95.58 ± 0.48%, 95.61 ± 0.55%, 95.67 ± 0.44%, 96.98 ± 0.42%, and 95.61±0.48%, respectively. CONCLUSIONS: These experimental results of the proposed methods in lung field segmentation, cardiothoracic ratio measurement and clinic severity prediction were better than previous methods, which proved that this method had great potential for clinical application.

Radiological cardiothoracic ratio as a potential predictor of right ventricular enlargement in patients with suspected pulmonary embolism due to COVID-19

Objective. Cases of pulmonary embolism are observed in the course of COVID-19. Right ventricular enlargement is a negative prognostic factor of pulmonary embolism. The cardiothoracic ratio is a routine parameter in the cardiac assessment in chest radiology. Purpose. The aim of the study was to determine the usefulness of the radiological cardiothoracic ratio (CTR) as a predictor of right ventricular enlargement in patients with suspected pulmonary embolism during COVID-19. Material and method. The study group consisted of 61 patients with confirmed COVID-19, suspected of pulmonary embolism based on physical examination and laboratory tests (age: 67.18 ± 12.47 years). Computed tomography angiography (CTA) of pulmonary arteries and chest radiograph in AP projection with cardiothoracic ratio assessment were performed in all patients. Right ventricular enlargement was diagnosed by the ratio of right ventricular to left ventricular (RV/LV) dimensions in pulmonary CTA with 2 cut-off points: ≥0.9 and ≥1.0. Heart's silhouette enlargement was found when CTR on the chest radiograph in the projection AP >0.55. Results. The mean values of RV/LV and CTR in the studied group were 0.96 ± 0.23 and 0.57 ± 0.05. Pulmonary embolism was diagnosed in 45.9%. Right ventricular enlargement was documented in 44.3% or 29.5% depending on the adopted criterion RV/LV ≥0.9 or RV/LV ≥1.0. Heart's silhouette enlargement was found in 60.6%. Patients with confirmed pulmonary embolism (PE+) had significantly higher RV/LV ratio and CTR than patients with excluded pulmonary embolism (PE-) (RV/LV: PE+ 1.08 ± 0.24, PE- 0.82 ± 0.12;CTR: PE+ 0.60 ± 0.05, PE- 0.54 ± 0.04;p < 0.05). The correlation analysis showed a statistically significant positive correlation between RV/LV ratio and CTR (r = 0.59, p < 0.05). Based on the ROC curves, CTR values were determined as the optimal cut-off points for the prediction of right ventricular enlargement (RV/LV ≥0.9 or RV/LV ≥1.0), being 0.54 and 0.55, respectively. The sensitivity, specificity, and accuracy of the CTR criterion >0.54 as a predictor of RV/LV ratio ≥0.9 was 0.412, 0.963, and 0.656, respectively, while the CTR criterion >0.55 as a predictor of RV/LV ratio ≥1.0 was 0.488, 0.833, and 0.590, respectively. Conclusions. In patients with suspected pulmonary embolism during COVID-19, radiographic cardiothoracic ratio may be a predictor of right ventricular enlargement, especially a negative predictor of right ventricular enlargement in case of lower CTR values.

Radiological Cardiothoracic Ratio as a Potential Predictor of Right Ventricular Enlargement in Patients with Suspected Pulmonary Embolism Due to COVID-19.

The aim of the study was to determine the usefulness of the radiological cardiothoracic ratio (CTR) as a predictor of right ventricular enlargement in patients with suspected pulmonary embolism during COVID-19. The study group consisted of 61 patients with confirmed COVID-19, suspected of pulmonary embolism based on physical examination and laboratory tests (age: 67.18 ± 12.47 years). Computed tomography angiography (CTA) of pulmonary arteries and chest radiograph in AP projection with cardiothoracic ratio assessment were performed in all patients. Right ventricular enlargement was diagnosed by the ratio of right ventricular to left ventricular (RV/LV) dimensions in pulmonary CTA with two cut-off points: ≥0.9 and ≥1.0. Heart silhouette enlargement was found when CTR on the chest radiograph in the projection AP > 0.55. The mean values of RV/LV and CTR in the studied group were 0.96 ± 0.23 and 0.57 ± 0.05, respectively. Pulmonary embolism was diagnosed in 45.9%. Right ventricular enlargement was documented in 44.3% or 29.5% depending on the adopted criterion RV/LV ≥ 0.9 or RV/LV ≥ 1.0. Heart silhouette enlargement was found in 60.6%. Patients with confirmed pulmonary embolism (PE+) had a significantly higher RV/LV ratio and CTR than patients with excluded pulmonary embolism (PE-) (RV/LV: PE+ 1.08 ± 0.24, PE- 0.82 ± 0.12; CTR: PE+ 0.60 ± 0.05, PE- 0.54 ± 0.04; p < 0.05). The correlation analysis showed a statistically significant positive correlation between the RV/LV ratio and CTR (r = 0.59, p < 0.05). Based on the ROC curves, CTR values were determined as the optimal cut-off points for the prediction of right ventricular enlargement (RV/LV ≥ 0.9 or RV/LV ≥ 1.0), being 0.54 and 0.55, respectively. The sensitivity, specificity, and accuracy of the CTR criterion >0.54 as a predictor of RV/LV ratio ≥0.9 were 0.412, 0.963, and 0.656, respectively, while those of the CTR criterion >0.55 as a predictor of RV/LV ratio ≥1.0 were 0.488, 0.833, and 0.590, respectively. In summary, in patients with suspected pulmonary embolism during COVID-19, the radiographic cardiothoracic ratio can be considered as a prognostic factor for right ventricular enlargement, especially as a negative predictor of right ventricular enlargement in the case of lower CTR values.

Society of Thoracic Radiology Annual Meeting

The proceedings contain 111 papers. The topics discussed include: implementation of a chest CT educational module for COVID-19 using RedCap software: outcomes and applicability for future thoracic imaging education;concurrent biopsy and microwave ablation of suspicious FDG-avid pulmonary nodules: a retrospective analysis;coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes;deep convolutional neural networks for detection of cardiomegaly through calculation of cardiothoracic ratio from chest radiographs;clearing the congestion: comparison of chest radiography and BNP in the diagnosis of heart failure;human and deep learning quantification of COVID-19 severity on chest X-ray prognosticates hospitalization, intubation, and survival;performance of an artificial intelligence-based platform against clinical radiology reports for the evaluation of non-contrast chest CT;and development of a 3D U-net deep-learning model for automated detection of lung nodules on chest CT images: internal and external validation using LIDC and Japanese datasets.

The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19.

RATIONALE AND OBJECTIVES: Cardiac indices can predict disease severity and survival in a multitude of respiratory and cardiovascular diseases. Herein, we hypothesized that CT-measured cardiac indices are correlated with severity of lung involvement and can predict survival in patients with COVID-19. MATERIALS AND METHODS: Eighty-seven patients with confirmed COVID-19 who underwent chest CT were enrolled. Cardiac indices including pulmonary artery-to-aorta ratio (PA/A), cardiothoracic ratio (CTR), epicardial adipose tissue (EAT) thickness and EAT density, inferior vena cava diameter, and transverse-to-anteroposterior trachea ratio were measured by non-enhanced CT. Logistic regression and Cox-regression analyses evaluated the association of cardiac indices with patients' outcome (death vs discharge). Linear regression analysis was used to assess the relationship between the extent of lung involvement (based on CT score) and cardiac indices. RESULTS: Mean (±SD) age of patients was 54.55 (±15.3) years old; 65.5% were male. Increased CTR (>0.49) was seen in 52.9% of patients and was significantly associated with increased odds and hazard of death (odds ratio [OR] = 12.5, p = 0.005; hazard ratio = 11.4, p = 0.006). PA/A >1 was present in 20.7% of patients and displayed a nonsignificant increase in odds of death (OR = 1.9, p = 0.36). Furthermore, extensive lung involvement was positively associated with elevated CTR and increased PA/A (p = 0.001). CONCLUSION: CT-measured cardiac indices might have predictive value regarding survival and extent of lung involvement in hospitalized patients with COVID-19 and could possibly be used for the risk stratification of these patients and for guiding therapy decision-making. In particular, increased CTR is prevalent in patients with COVID-19 and is a powerful predictor of mortality.