Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model - Ma, Xiaoxu; Li, Ang; Jiao, Mengfan; Shi, Qingmiao; An, Xiaocai; Feng, Yonghai; Xing, Lihua; Liang, Hongxia; Chen, Jiajun; Li, Huiling; Li, Juan; Ren, Zhigang; et al. (Frontiers in Public Health, 2020)
Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, 6 fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0·907 (95% CI 0·831-0·983). Mixed model constructed with combination of age, demographics, symptoms and laboratory findings at admission had better performance (p= 0.021) with a generalized AUC of 0·9852 (95% CI 0·961-1). We chose 0·441 as death prediction threshold (with 0·85 sensitivity and 0·987 specificity) and validated the model in 429 mild cases, 6 fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.