Simplified Lung Ultrasound Examination and Telehealth Feasibility in Early SARS-CoV-2 Infection

BACKGROUND: In COVID-19, inpatient studies have demonstrated that lung ultrasound B-lines relate to disease severity and mortality and can occur in apical regions that can be imaged by patients themselves. However, as illness begins in an ambulatory setting, the aim of this study was to determine the prevalence of apical B-lines in early outpatient infection and then test the accuracy of their detection using telehealth and automated methods. METHODS: Consecutive adult patients (N = 201) with positive results for SARS-CoV-2, at least one clinical risk factor, and mild to moderate disease were prospectively enrolled at a monoclonal antibody infusion clinic. Physician imaging of the lung apices for three B-lines (ultrasound lung comet [ULC]) using 3-MHz ultrasound was performed on all patients for prevalence data and served as the standard for a nested subset (n = 50) to test the accuracy of telehealth methods, including patient self-imaging and automated B-line detection. Patient characteristics, vaccination data, and hospitalizations were analyzed for associations with the presence of ULC. RESULTS: Patients’ mean age was 54 ± 15 years, and all lacked hypoxemia or fever. ULC was present in 55 of 201 patients (27%) at a median of 7 symptomatic days (interquartile range, 5-8 days) and in four of five patients who were later hospitalized (P = .03). Presence of ULC was associated with unvaccinated status (odds ratio [OR], 4.11; 95% CI, 1.85-9.33; P = .001), diabetes (OR, 2.56; 95% CI, 1.08-6.05; P = .03), male sex (OR, 2.14; 95% CI, 1.07-4.37; P = .03), and hypertension or cardiovascular disease (OR, 2.06; 95% CI, 1.02-4.23; P = .04), while adjusting for body mass index > 25 kg/m(2). Telehealth and automated B-line detection had 84% and 82% accuracy, respectively. CONCLUSIONS: In high-risk outpatients, B-lines in the upper lungs were common in early SARS-CoV-2 infection, were related to subsequent hospitalization, and could be detected by telehealth and automated methods.