A Simplified Point-of-Care Lung Ultrasound Protocol to Detect Coronavirus Disease 2019 in Inpatients: A Prospective Observational Study
Heyne, T. F.; Geisler, B. P.; Negishi, K.; Choi, D. S.; Al Saud, A. A.; Marinacci, L. X.; Smithedajkul, P. Y.; Devaraj, L. R.; Little, B. P.; Mendoza, D. P.; Flores, E. J.; Petranovic, M.; Toal, S. P.; Shokoohi, H.; Liteplo, A. S.
Show abstract
ObjectivesTo 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. MethodsHospital-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. ResultsOf 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. ConclusionPOCUS outperformed CXR to predict positive NAT. POCUS could potentially replace other chest imaging for persons under investigation for COVID-19.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study 95%
- Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan 94%
- Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 92%
Similar papers in this journal
Similar papers in this journal
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm 93%
- Volumetric lung cancer screening reduces unnecessary low-dose computed tomography scans: results from a single-centre prospective trial on 4,119 subjects 92%
- Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications in Kyrgyzstan 90%
Similar papers in this journal
Similar papers in this journal
- Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center 94%
- Chest X-Ray Has Poor Diagnostic Accuracy and Prognostic Significance in COVID-19: A Propensity Matched Database Study 93%
- The Diagnostic Accuracy of Subjective Dyspnea in Detecting Hypoxemia Among Outpatients with COVID-19 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.