Neutralization Assessments Reveal High Cardiothoracic Ratio and Old Age as Independent Predictors of Low Neutralizing Antibody Titers in Hemodialysis Patients Receiving a Single Dose of COVID-19 Vaccine

Background: Data are lacking regarding predictors of quantification of neutralizing antibodies (nAbs) based on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 50% neutralization titer (NT50) after a single dose of COVID-19 vaccine in hemodialysis (HD) patients. Methods: This prospective single-center study enrolled 200 HD patients and 82 healthy subjects to estimate antibodies against the SARS-CoV-2 viral spike protein 1 and receptor-binding domain after a first dose of a COVID-19 vaccine (ChAdOx1 or mRNA-1273), measured by enzyme-linked immunosorbent assay and applied spline-based generalized additive model regression analysis to predict NT50 converted to international units. Results: After the first dose of ChAdOx1, multiple linear regression showed that age (p = 0.011) and cardiothoracic ratio (p = 0.002) were negatively associated with NT50. Older age (OR = 0.958, p = 0.052) and higher cardiothoracic ratio (OR < 0.001, p = 0.037) could predict negative humoral response (NT50 < 35.13 IU/mL). NT50 was lower in HD patients compared with healthy controls receiving ChAdOx1 (10.68 vs. 43.01 IU/m, p < 0.001) or mRNA-1273 (36.39 vs. 262.2 IU/mL, p < 0.001). ChAdOx1 elicited lower GMTs than mRNA-1273 in the HD cohort (10.68 vs. 36.39 IU/mL, p < 0.001) and in healthy controls (43.01 vs. 262.22 IU/mL, p < 0.001). Conclusion: High cardiothoracic ratio and old age could independently predict a decline in nAb titers in an HD cohort vaccinated with a single dose of ChAdOx1.

A Clinical Evaluation Study of Cardiothoracic Ratio Measurement Using Artificial Intelligence

Background Artificial Intelligence, particularly the Deep Learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on Chest x-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7,517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9,386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet+VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet+VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced operating time by almost ten-fold (1.07 ± 2.62 secs vs 10.6 ± 1.5 sec) compared to manual operation. Conclusion Due to its exceptional accuracy and speed, the AlbuNet+VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

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.

The relationship between umbilical–portal–systemic venous shunt and outcome in 11 fetuses

Objective: To investigate the relationship between congenital umbilical–portal–systemic venous shunt (UPSVs) and fetal outcome. Methods: The ultrasonographic and genetic characteristics of 11 cases of UPSVS were retrospectively analyzed and followed up to postnatal. Results: Four cases of ductus venosus -- systemic shunt (DVSS), one case of extrahepatic portal -- systemic shunt (EHPSS), and one case of umbilical systemic shunt (USS) combined with intrahepatic portal-systemic shunt (IHPSS), six cases of intrahepatic portal-systemic shunt (IHPSS) were observed. chromosomal abnormalities were observed in 9.1% (1/11), other ultrasonic abnormalities in 54.5% (6/11), cardiothoracic ratio increase in 45.5% (5/11), fetal growth restriction in 36.4% (4/11), edema was in 9.1% (1/11) and live birth was in 72.7% (8/11). Conclusion: The incidence of IHPSS is the highest and the outcome is the best. Shunt of DVSS and IHPSS can be closed spontaneously after birth. When the prenatal diagnosis is congenital UPSVs, chromosomal abnormalities and other ultrasonic abnormalities should be required further examination, and the growth and development of the fetus should be closely monitored.

Additional Value of Non-contrast Chest CT in the Prediction of Adverse Cardiovascular Events in Patients With Novel Coronavirus Disease 2019 (COVID-19)

Background: Coronavirus disease 2019 (COVID-19) has outbroken in China and subsequently spread worldwide since the end of 2019. Chest computed tomography (CT) plays an important role in the diagnosis of lung diseases, but its value in the diagnosis of cardiac injury remains unknown.Methods: We enrolled 241 consecutive hospitalized patients (aged 61 ± 16 years, 115 males) with laboratory-confirmed COVID-19 at Renmin Hospital of Wuhan University from January 11 to March 2, 2020. They were divided into two groups according to whether major adverse cardiovascular events (MACEs) occurred during the follow-up. The anteroposterior diameter of the left atrium (LAD), the length of the left ventricle (LV), and cardiothoracic ratio (CTR) were measured. The values of myocardial CT were also recorded.Results: Of 241 patients, 115 patients (47.7%) had adverse cardiovascular events. Compared with no MACEs, patients with MACEs were more likely to have bilateral lesions (95.7% vs. 86.5%, p = 0.01). In multivariable analysis, bronchial wall thickening would increase the odds of MACEs by 13.42 (p = 0.01). LAD + LV and CTR was the best predictor for MACEs (area under the curve = 0.88, p &lt; 0.001) with a sensitivity of 82.6% and a specificity of 80.2%. Plasma high-sensitivity troponin I levels in patients with cardiac injury showed a moderate negative correlation with minimum CT value (R2 = −0.636, p &lt; 0.001).Conclusions: Non-contrast chest CT can be a useful modality for detection cardiac injury and provide additional value to predict MACEs in COVID-19 patients.

Atrial fibrillation is an independent predictor of cardiovascular events in patients with primary aldosteronism

Background A higher risk of cardiovascular events has been reported in patients with hypertension due to primary aldosteronism (PA) than essential hypertension. This study sought to determine the independent predictors for the risk of cardiovascular events in hypertensive patients with PA. Methods The Japan Primary Aldosteronism Study (JPAS) has retrospectively recruited 3,654 patients with PA between January 2006 and January 2019 as a nationwide registry and we evaluated the differences between patients with and without AF from these data. The patients underwent general laboratory test, electrocardiography, echocardiography, diagnostic confirmatory tests including saline-loading, captopril-challenge, and upright furosemide-loading tests and adrenal venous sampling (AVS). We evaluated the cardiovascular events including myocardial infarction, stroke, heart failure and renal failure, with a mean follow-up duration of approximately 4 years. Results The prevalence of AF was 2.4% (88/ 3,654). PA patients with AF were older (60.3 vs 52.8 years old), more male (77.3% vs 46.6%) and had longer duration of hypertension (14.3 vs 8.3 years) than those without AF. Each prevalence of cerebral infarction (12.5% vs 3.5%), chronic kidney disease (12.5% vs 4.8%), coronary artery disease (CAD) (10.2% vs 1.7%), heart failure (5.7% vs 0.7%) and left ventricular hypertrophy evaluated by echocardiography (46.4% vs 31.9%) was higher in PA patients with AF. Patients with AF had more kinds of antihypertensive drugs (1.3 vs 1.1). There was no significant difference of basal plasma aldosterone concentration (PAC), plasma renin activity, each confirmatory tests, lateralized ratio in AVS after stimulation with adrenocorticotropic hormone (ACTH) and laterality between the 2 groups. Logistic regression analysis showed that age, cardiothoracic ratio (CTR), past history of CAD and heart failure were independent determinants for AF. PA patients with AF had higher rates of cardiovascular events compared to those without AF (Figure, P&lt;0.005). Multivariate Cox regression analyses demonstrated AF in addition to adrenal PAC before ACTH stimulation, age, hypokalemia and duration of hypertension as independent prognostic factors for cardiovascular events (hazard ratio [HR] 1.993, 95% confidence interval [CI] 1.042–3.815, P&lt;0.05; HR 1.ehab724.231608, 95% CI 1.ehab724.231604–1.ehab724.231612, P&lt;0.0005; HR 1.03, 95% CI 1.012–1.048, P&lt;0.005; HR 1.748, 95% CI 1.242–2.461, P&lt;0.005; HR 1.029, 95% CI 1.013–1.044, P&lt;0.0005, respectively). Conclusions This study provides evidence that comorbid AF is associated with older age, male sex, X-ray CTR and prevalence of CAD and heart failure. Furthermore, AF is an independent predictor of cardiovascular events in patients with PA, in addition to the adrenal venous concentration of aldosterone, hypokalemia, older age and duration of hypertension. Earlier recognition and intervention of AF can prevent cardiovascular events in PA. FUNDunding Acknowledgement Type of funding sources: None. Figure 1

Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.