Risk factors for severe COVID-19 in middle-aged patients without comorbidities: a multicentre retrospective study
Peng Wang, Jing Sha, Mei Meng, Cuiyan Wang, Qingchun Yao, Zhongfa Zhang, Wenqing Sun, Xingguang Wang, Guoqiang Qie, Xue Bai, Keke Liu, Yufeng Chu
Abstract
BACKGROUND: Information regarding characteristics and risk factors of COVID-19 amongst middle-aged (40-59 years) patients without comorbidities is scarce. METHODS: We therefore conducted this multicentre retrospective study and collected data of middle-aged COVID-19 patients without comorbidities at admission from three designated hospitals in China. RESULTS: Among 119 middle-aged patients without comorbidities, 18 (15.1%) developed into severe illness and 5 (3.9%) died in hospital. ARDS (26, 21.8%) and elevated D-dimer (36, 31.3%) were the most common complications, while other organ complications were relatively rare. Multivariable regression showed increasing odds of severe illness associated with neutrophil to lymphocyte ratio (NLR, OR, 11.238; 95% CI 1.110-1.382; p < 0.001) and D-dimer greater than 1 µg/ml (OR, 16.079; 95% CI 3.162-81.775; p = 0.001) on admission. The AUCs for the NLR, D-dimer greater than 1 µg/ml and combined NLR and D-dimer index were 0.862 (95% CI, 0.751-0.973), 0.800 (95% CI 0.684-0.915) and 0.916 (95% CI, 0.855-0.977), respectively. SOFA yielded an AUC of 0.750 (95% CI 0.602-0.987). There was significant difference in the AUC between SOFA and combined index (z = 2.574, p = 0.010). CONCLUSIONS: More attention should be paid to the monitoring and early treatment of respiratory and coagulation abnormalities in middle-aged COVID-19 patients without comorbidities. In addition, the combined NLR and D-dimer higher than 1 μg/ml index might be a potential and reliable predictor for the incidence of severe illness in this specific patient with COVID-19, which could guide clinicians on early classification and management of patients, thereby relieving the shortage of medical resource. However, it is warranted to validate the reliability of the predictor in larger sample COVID-19 patients.