Application and internal validation of lung ultrasound score in COVID-19 setting: The ECOVITA observational study.

Abstract

Background: The severe acute respiratory syndrome Coronarovirus-2 associated still causes a significant number of deaths and hospitalizations mainly by the development of respiratory failure. We aim to validate lung ultrasound score in order to predict mortality and the severity of the clinical course related to the need of respiratory support.

Methods: In this prospective multicenter hospital-based cohort study, all adult patients with diagnosis of SARS-CoV-2 infection, performed by real-time reverse transcription polymerase chain reaction were included. Upon admission, all patients underwent blood gas analysis and lung ultrasound by expert operators. The acquisition of ultrasound scan was performed on 12 peculiar anatomic landmarks of the chest. Lung ultrasound findings were classified according to a scoring method, ranging 0 to 3: Score 0: normal A-lines. Score 1: multiple separated B-lines. Score 2: coalescent B-lines, alteration of pleural line. Score 3: consolidation area.

Results: One thousand and seven patients were included in statistical analysis (male 62.4 %, mean age 66.3). Oxygen support was needed in 811 (80.5 %) patients. The median ultrasound score was 24 and the risk of having more invasive respiratory support increased in relation to higher values score computed. Lung ultrasound score showed negative strong correlation (rho: -0.71) with the P/F ratio and a significant association with in-hospital mortality (OR 1.11, 95 %CI 1.07-1.14; p < 0.001), even after adjustment with the following variables (age, sex, P/F ratio, SpO2, lactate, hypertension, chronic renal failure, diabetes, and obesity).

Conclusions: The novelty of this research corroborates and validates the 12-field lung ultrasound score as tool for predicting mortality and severity clinical course in COVID-19 patients. Baseline lung ultrasound score was associated with in-hospital mortality and requirement of intensive respiratory support and predict the risk of IOT among COVID-19 patients.