AUTHOR=Guo Lingxi , Wei Dong , Zhang Xinxin , Wu Yurong , Li Qingyun , Zhou Min , Qu Jieming 

TITLE=Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.02752

DOI=10.3389/fmicb.2019.02752

ISSN=1664-302X

ABSTRACT=Objective
The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. 
Methods 
A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. 
Results
76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR=5.20, 95%CI 1.41-12.52, p=0.010; 5 points), lymphocyte≤0.8*109/L (OR=4.53, 95%CI 2.55-8.05, p<0.001; 4 points), bacterial coinfection (OR=3.71, 95%CI 2.11-6.51, p<0.001; 4 points), acute-smoker (OR=3.19, 95% CI 1.34-6.26, p=0.001; 3 points), quit-smoker (OR=2.18, 95% CI 0.99-4.82, p=0.054; 2 points), hypertension (OR=2.39, 95% CI 1.55-4.26, p=0.003; 2 points) and age≥60 years (OR=2.14, 95% CI 1.04-4.39, p=0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs 0.717, p<0.001). 
Conclusions
Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.