A New Scoring Model to Diagnose COVID-19 Using Lung Ultrasound in the Emergency Department

BACKGROUND: Several studies have reported the predictors of the prognosis in COVID-19 patients; however, smoking, X-ray findings of pulmonary congestion, and A-profile and areas of consolidation in LUS are independent predictors for COVID-19 infection. The new score had a sensitivity of 93.8% and a specificity of 58% for the prediction of COVID-19. Mortality in COVID-19 patients is significantly correlated with age, fever duration, cardiac history, and B-profile and areas of consolidation in LUS. However, it is negatively correlated with initial O(2) saturation and ejection fraction. This study aimed to design a new scoring model to diagnose COVID-19 using bedside lung ultrasound (LUS) in the emergency department (ED). RESULTS: Eighty-two patients were recruited. Fifty patients (61%) were negative for COVID-19, and 32 (39%) were positive. Sixty-four patients (78%) recovered while 18 patients (22%) died. COVID-19 patients had more AB-profile and more areas of consolidation than the non-COVID-19 group (p<0.001). Smoking, congestion in X-ray, A-profile, and abnormal A line in LUS are independent predictors for COVID-19 infection. The score had a sensitivity of 93.8% and a specificity of 58% for the prediction of COVID-19. Mortality in COVID-19 patients is significantly correlated with age, fever duration, cardiac history, and B-profile and areas of consolidation in LUS. However, it is negatively correlated with initial O(2) saturation and ejection fraction. CONCLUSIONS: In conclusion, the application of our new score can stratify patients presented to ED with suspected COVID-19 pneumonia, considering that it is a good negative test. Moreover, this score may have a good impact on the safety of medical personnel. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05077202. Registered October 14, 2021 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05077202