Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests.

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

Epidemiologic and clinical characteristics of 42 deaths caused by SARS-CoV-2 infection in Wuhan, China: A retrospective study.

This study described the epidemiologic and clinical characteristics of patients who died from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and pointed out the potential risk factors associated with fatal outcomes. Retrospective data from 42 death cases due to SARS-CoV-2 infection at Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, China was analyzed. Demographics, clinical detection, laboratory findings, and treatments of the deceased were collected and analyzed. The average time between onset of symptoms and admission to the hospitals was 11 ± 5 days of hospitalization. Among the deceased, 60% were with co-morbidities. All of them were having fever and bilateral pneumonia on computed tomography, abnormal infection-related biomarkers, and renal impairment. Abnormal blood coagulation parameters that appeared in more than half of them, were consistent with disseminated intravascular coagulation. All of the patients were treated in the ICU. Based on the fact that SARS-CoV-2 infection carries a risk of mortality, we may infer a few older male patients with underlying comorbidities are likely to have the increased risk. Impaired consciousness level, markers of renal impairment and coagulation abnormalities may be poor prognostic factors.