Objectives: To assess the diagnostic performance of lung point-of-care ultrasound (POCUS) compared to either a positive nucleic acid test (NAT) or a COVID-19-typical pattern on computed tomography (CT) and to evaluate opportunities to simplify a POCUS algorithm .
Methods: Hospital-admitted adult inpatients with (1) either confirmed or suspected COVID-19 and (2) a completed or ordered CT within the preceding 24 hours were recruited . Twelve lung zones were scanned with a handheld POCUS machine . POCUS, CT, and X-ray (CXR) images were reviewed independently by blinded experts . A simplified POCUS algorithm was developed via machine learning .
Results: Of 79 enrolled subjects , 26.6% had a positive NAT and 31.6% had a CT typical for COVID-19 . The receiver operator curve (ROC) for a 12-zone POCUS protocol had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for typical CT. A simplified four-zone protocol had an AUC of 0.862 for typical CT and 0.862 for positive NAT . CT had an AUC of 0.815 for positive NAT; CXR had AUCs of 0.793 for positive NAT and 0.733 for typical CT . Performance of the four-zone protocol was superior to CXR for positive NAT (p=0.0471). Using a two-point cutoff system, the four-zone POCUS protocol had a sensitivity of 0.920 and 0.904 compared to CT and NAT, respectively, at the lower cutoff; it had a specificity of 0.926 and 0.948 at the higher cutoff, respectively . Conclusion: POCUS outperformed CXR to predict positive NAT . POCUS could potentially replace other chest imaging for persons under investigation for COVID-19.