Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China

Innovation (Camb). 2020 May 21;1(1):100007. doi: 10.1016/j.xinn.2020.04.007. Epub 2020 May 20.

Abstract

Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict the illness severity of COVID-19. The model includes four parameters: age, BMI, CD4+ lymphocytes and IL-6 levels. The AUC of the model is 0.911.The high risk factors for developing to severe COVID-19 are: age ≥ 55 years, BMI > 27 kg / m2, IL-6 ≥ 20 pg / ml, CD4+ T cell ≤ 400 count / μ L.Among 249 discharged COVID-19 patients, those who recovered after 20 days had a lower count of platelet, a higher level of estimated glomerular filtration rate, and higher level of interleukin-6 and myoglobin than those who recovered within 20 days.

Keywords: CD4+ T lymphocyte; COVID-19; illness severity; prediction model.