ABSTRACT
OBJECTIVES: SARS-CoV-2 can cause respiratory diseases with various manifestations. However, little is known about its potential for lung recovery. Lung ultrasound has shown characteristic changes during COVID-19 and has proven to be useful for triage, diagnosis, and therapy. This study investigated how the recovery process from COVID-19 respiratory disease can be monitored using 12-zone lung ultrasound. METHODS: This prospective observational cohort study was conducted in a busy urban emergency department in London, United Kingdom, over a 20-week period between April and October 2020. We followed 24 patients recovering from COVID-19 with varying disease severity using 12-zone lung ultrasound at 2-week intervals and monitored the changes in the prevalence of lung abnormalities previously described in COVID-19 infection (irregular pleura, subpleural consolidation, B-lines, and small localized effusions). RESULTS: Lung ultrasound showed that the lung recovers significantly over 20 weeks postdisease. Individual lung abnormalities also resolved at different rates. The entire rib space occupied by confluent B-lines wane after the acute phase, whereas irregular pleura and subpleural consolidations resolved more gradually. Separate wide B-lines moving with the pleura during respiration may represent more stable features, indicating residual fibrotic changes. Small, localized effusions appear transiently after the initial acute phase of the disease, peaking at approximately 10 weeks after infection. The measured lung abnormalities were strong predictors of perceived shortness of breath during ambulation. CONCLUSION: Lung ultrasound can be a useful tool for long-term monitoring of COVID-19 lung disease, avoiding repeated exposure to ionizing radiation, and may distinguish between acute and past infections.
ABSTRACT
PURPOSE: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient's symptoms and clinical history. METHODS: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. RESULTS: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23-91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55-91.65%) that was higher in the mild phenotype (94.4%; CI 90.0-97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6-6.7, p < 0.0001). CONCLUSION: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients' management during a pandemic surge.