Objectives: This study aimed to identify the clinical features of cardiac injury complicating with acute kidney injury (AKI) and its risk for a fatal outcome in patients infected with coronavirus disease 2019 (COVID-19) pneumonia. Methods: Initial signs and symptoms and clinical laboratory, radiological, and treatment information were obtained from seven hospitals in China from January 23, 2020, to March 15, 2020. Results: Of 438 patients, 36 (8.22%) displayed isolated cardiac injury, 17 (3.88%) had isolated AKI, and 17 (3.88%) displayed cardiac injury complicating with AKI. Compared with patients without cardiac injury or AKI, patients with isolated cardiac injury, isolated AKI, and cardiac injury complicating with AKI were older (55, 65, 74 vs. 48 years, P < 0.0001) and critically severe. More patients displayed fatigue, dyspnea, and comorbidities in the group with cardiac injury complicating with AKI. Moreover, the indexes reflecting myocardial, renal, liver, and coagulation dysfunctions and infection-related factors were significantly different among the four groups. After adjustment for covariates, patients with cardiac injury complicating with AKI had a higher hazard ratio for mortality (6.64; 95% confidence interval, 1.51–29.30). Conclusion: Cardiac injury complicating with kidney injury significantly increased the risk for in-hospital mortality in COVID-19 pneumonia patients. Therefore, early detection at admission and careful monitoring of myocardial and renal injury through biomarkers during hospitalization is recommended to reduce the harm to patients.

PURPOSE: The purpose of this study was to investigate the clinical and baseline computed tomography (CT) features and their correlation in patients infected with the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MATERIALS AND METHODS: Clinical and chest baseline CT data of patients infected with the Delta variant of SARS-CoV-2 from July to August 2021 were collected. First, the correlation between the clinical data and baseline CT results was analyzed according to CT positivity or negativity. Then, subgroup analysis was performed between different age distributions and clinical characteristics. Next, the CT characteristics and clinical data of all baseline CT-positive patients were collected, and the correlations between CT characteristics and age, vaccination status, and chronic disease were analyzed. Lesions in patients with baseline CT positivity were evaluated by semi-quantitative scoring to analyze the correlations between the semi-quantitative scores and vaccination status and age distribution. RESULTS: A total of 221 nucleic acid-positive patients with the SARS-CoV-2 Delta variant were included, of whom 107 patients were baseline CT positive and 114 were baseline CT negative. Baseline CT positivity was associated with age distribution, and baseline CT positivity was most common in patients aged >60 years (P < 0.001), but not with vaccination status or gender. The results of the subgroup analysis according to age distribution indicated that different age distribution subgroups had different vaccination statuses, and the majority of patients aged <18 years and >60 years were unvaccinated (90.5%, 19/21, and 57.3%, 63/110, respectively). In contrast, most patients aged 18–60 years had received two doses of the vaccine (61.1%, 55/90) (P < 0.001). Different age distribution subgroups had different clinical infection types. Asymptomatic and mild cases were most common in patients aged ≤60 years, and moderate and severe or critical cases were most common in patients aged >60 years. For baseline CT-positive patients, the extent of lung involvement was associated with age, vaccination status, and chronic disease. The number of involved lobes was higher in patients who were unvaccinated or who had received one injection, who were aged >60 years or had chronic disease. There was a statistical difference in CT semi-quantitative scores between the different age subgroups. Compared with patients aged < 60 years, patients aged >60 years had higher semi-quantitative scores (P < 0.001). However, there was no statistical difference between the different vaccination groups. CONCLUSIONS: Age had a large effect on baseline CT positivity, CT characteristics, and semi-quantitative CT scores in patients infected with the Delta variant.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear. METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed. RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na⁺, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593. CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.

OBJECTIVE: As hospital admission rate is high during the COVID-19 pandemic, hospital length of stay (LOS) is a key indicator of medical resource allocation. This study aimed to elucidate specific dynamic longitudinal computed tomography (CT) imaging changes for patients with COVID-19 over in-hospital and predict individual LOS of COVID-19 patients with Delta variant of SARS-CoV-2 using the machine learning method. MATERIALS AND METHODS: This retrospective study recruited 448 COVID-19 patients with a total of 1761 CT scans from July 14, 2021 to August 20, 2021 with an averaged hospital LOS of 22.5 ± 7.0 days. Imaging features were extracted from each CT scan, including CT morphological characteristics and artificial intelligence (AI) extracted features. Clinical features were obtained from each patient's initial admission. The infection distribution in lung fields and progression pattern tendency was analyzed. Then, to construct a model to predict patient LOS, each CT scan was considered as an independent sample to predict the LOS from the current CT scan time point to hospital discharge combining with the patients' corresponding clinical features. The 1761 follow-up CT data were randomly split into training set and testing set with a ratio of 7:3 at patient-level. A total of 85 most related clinical and imaging features selected by Least Absolute Shrinkage and Selection Operator were used to construct LOS prediction model. RESULTS: Infection-related features were obtained, such as the percentage of the infected region of lung, ground-glass opacity (GGO), consolidation and crazy-paving pattern, and air bronchograms. Their longitudinal changes show that the progression changes significantly in the earlier stages (0–3 days to 4–6 days), and then, changes tend to be statistically subtle, except for the intensity range between (−470 and −70) HU which exhibits a significant increase followed by a continuous significant decrease. Furthermore, the bilateral lower lobes, especially the right lower lobe, present more severe. Compared with other models, combining the clinical, imaging reading, and AI features to build the LOS prediction model achieved the highest R² of 0.854 and 0.463, Pearson correlation coefficient of 0.939 and 0.696, and lowest mean absolute error of 2.405 and 4.426, and mean squared error of 9.176 and 34.728 on the training and testing set. CONCLUSION: The most obvious progression changes were significantly in the earlier stages (0–3 days to 4–6 days) and the bilateral lower lobes, especially the right lower lobe. GGO, consolidation, and crazy-paving pattern and air bronchograms are the most main CT findings according to the longitudinal changes of infection-related features with LOS (day). The LOS prediction model of combining clinical, imaging reading, and AI features achieved optimum performance.

Echinococcosis is a zoonotic parasitic disease caused by the larval stages of the Taeniid cestode species within the genus echinococcosis. The most commonly affected organ is the liver. Hepatic hydatid cysts have various complications, including superinfection and biliary, intrathoracic, and abdominal rupture. Intrabiliary rupture is the most common complication of hepatic hydatid cysts and is associated with high morbidity and mortality. Urgent imaging diagnosis and surgical management are required in these cases.

Osteomyelitis of the maxilla with orbital cellulitis, an uncommon and life-threatening disease, can be misdiagnosed. We here report a 13-year-old boy, possibly with combined immunodeficiency disease, who presented with osteomyelitis of the maxilla and orbital cellulitis after tooth extraction. Computed tomography demonstrated thickening of the left maxillary bone. Magnetic resonance imaging showed inflammation in the left maxillary bone and retrobulbar space. Metagenomic analysis of an aspiration biopsy resulted in a diagnosis of infection with Porphyromonas endodontalis. He was successfully treated with metronidazole.

A patient with primary skeletal muscle lymphoma underwent plain and contrast-enhanced computed tomography (CT) and a pathologic diagnosis was made. The affected muscles were diffusely swollen, with recognizable outlines and clear borders. Contrast-enhanced CT showed mild-to-moderate enhancement, and the spaces surrounding the muscle and subcutaneous fat were narrowed and blurred. Primary skeletal muscle lymphoma is relatively rare and not very specific in its imaging manifestations. The final diagnosis depends on a biopsy of the lesion and immunohistochemistry.