Recurrent Pneumothorax and Pneumomediastinum as a Complication of COVID-19 in a Young Athlete

A novel coronavirus, SARS-CoV-2 was identified as the cause of a cluster of pneumonia cases in Wuhan, China in December 2019. Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide. Numerous studies have shown diverse findings on chest CT scan of the patients with COVID-19. The established well-known features of COVID-19 on chest imaging include bilateral multilobar ground-glass opacification (GGO) predominantly with peripheral distribution, mainly in the lower lobes. Atypical presentation of consolidative opacities superimposed on GGO may be found in a smaller number of cases, mainly in the elderly populations. Pleural and pericardial effusion, lymphadenopathy, cavitation, halo sign on CT scan, and pneumothorax are uncommon but may be seen with disease progression. We report a case of severe COVID-19 in an athlete man with development of bilateral pneumothorax, pneumomediastinum and subcutaneous emphysema during progression of the disease. The only risk factor for severe COVID-19 in our patient was suggested to be chronic use of dexamethasone as anabolic steroids. Our patient also received three sessions of plasmapheresis. Unfortunately, the patient expired due to recurrence of bilateral pneumothorax and pneumomediastinum. © 2022, Iranian Pediatric Hematology and Oncology Society. All rights reserved.

Early prediction of COVID-19 mortality risk based on demographic, vital sign and blood test

Background: Early prediction of the outcome situation of COVID-19 patients can decrease mortality risk by assuring efficient resource allocation and treatment planning. This study introduces a very accurate and fast system for the prediction of COVID-19 outcomes using demographic, vital signs, and laboratory blood test data.

Early prediction of COVID-19 mortality risk based on demographic, vital sign and blood test

Background: Early prediction of the outcome situation of COVID-19 patients can decrease mortality risk by assuring efficient resource allocation and treatment planning. This study introduces a very accurate and fast system for the prediction of COVID-19 outcomes using demographic, vital signs, and laboratory blood test data. Methods: In this analytic study, which is done from May 2020 to June 2021 in Tehran, 41 features of 244 COVID-19 patients were recorded on the first day of admission to the Masih Daneshvari Hospital. These features were categorized into eight different groups, demographic and patient history features, vital signs, and six different groups of laboratory blood tests including complete blood count (CBC), coagulation, kidney, liver, blood gas, and general. In this study, first, the significance of each of the extracted features and then the eight groups of features for prediction of mortality outcomes were considered, separately. Finally, the best combination of different groups of features was assessed. The statistical methods including the area under the receiver operating characteristic curve (AUC-ROC) based on binary Logistic Regression classification algorithm were used for evaluation. Results: The results revealed that red cell distribution width (RDW), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) in CBC features have the highest AUC with values of 85.29, 80.96, 79.94 and 79.70, respectively. Then, blood oxygen saturation level (SPO2) in vital features has a higher AUC with a value of 79.28. Moreover, combinations of features in the CBC group have the highest AUC with a value of 95.57. Then, coagulation and vital signs groups have the highest AUC with values of 85.20 and 83.84, respectively. Finally, triple combinations of features in CBC, vital signs, and coagulation groups have the highest AUC with the value of 96.54. Conclusion: Our proposed system can be used as an assistant acceptable tool for triage of COVID-19 patients to determine which patient will have a higher risk for hospitalization and intensive care in medical environments.