Prognostic value of bedside lung ultrasound score in patients with COVID-19 [post]

Li Ji, Chunyan Cao, Ying Gao, Wen Zhang, Yuji Xie, Yilian Duan, Shuangshuang Kong, Manjie You, Rong Ma, Lili Jiang, Jie Liu, Zhenxing Sun (+8 others)
2020 unpublished
BackgroundBedside lung ultrasound (LUS) has emerged as a useful and noninvasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19.MethodsThe LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score
more » ... ased on B-lines, lung consolidation and pleural line abnormalities was evaluated.ResultsPatients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable ( hazard ratio [HR] : 1.05, 95% confidence intervals [CI]: 1.02~1.08; P < 0.001; Akaike Information Criterion [AIC] =272; C-index = 0.903) or as a categorical variable (HR: 10.76, 95% CI: 2.75~42.05; P = 0.001; AIC =272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model ( AIC =286; C-index = 0. 866). An LUS score cut-off >12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively.ConclusionsThe LUS score is a powerful predictor of adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.
doi:10.21203/rs.3.rs-55111/v1 fatcat:naiooiuvojbhnitj5gzxz6bs7m