TY - JOUR
T1 - Predictive Model and Risk Factors for Case Fatality of COVID-19
T2 - A Cohort of 21,392 Cases in Hubei, China
AU - Wu, Ran
AU - Ai, Siqi
AU - Cai, Jing
AU - Zhang, Shiyu
AU - Qian, Zhengmin (Min)
AU - Zhang, Yunquan
AU - Wu, Yinglin
AU - Chen, Lan
AU - Tian, Fei
AU - Li, Huan
AU - Li, Mingyan
AU - Lin, Hualiang
N1 - Publisher Copyright:
© 2020 The Author(s)
PY - 2020/8/28
Y1 - 2020/8/28
N2 - An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 109/L versus (4–10) × 109/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 109/L versus (0.8–4) × 109/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1–4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.
AB - An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 109/L versus (4–10) × 109/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 109/L versus (0.8–4) × 109/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1–4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.
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U2 - 10.1016/j.xinn.2020.100022
DO - 10.1016/j.xinn.2020.100022
M3 - Article
C2 - 33521759
AN - SCOPUS:85094977828
SN - 2666-6758
VL - 1
JO - Innovation
JF - Innovation
IS - 2
M1 - 100022
ER -