Value of Bedside Lung Ultrasound in Severe and Critical COVID-19 Pneumonia.

BACKGROUND: Lung ultrasound (LUS) is an effective imaging modality that can differentiate pathological lung from non-diseased lung. We aimed to explore the value of bedside LUS in patients with severe and critical coronavirus disease 2019 (COVID-19)-associated lung injury. METHODS: Sixty-three severe and 33 critical hospitalized subjects with COVID-19 were enrolled in this study. Bedside LUS was performed in all subjects; chest computed tomography was performed on the same day as bedside LUS in 23 cases. The LUS protocol consisted of 12 scanning zones. LUS score based on B-lines and lung consolidation was evaluated. RESULTS: The most common abnormality of LUS was the various forms of B-lines, detected in 93 (96.9%) subjects; as the second most frequent abnormality, 80 (83.3%) subjects exhibited lung consolidation, mainly located in the posterior lung region. Twenty-four (25.0%) subjects had pleural line abnormalities, and 16 (16.7%) had pleural effusion; 78 (81.3%) subjects had ≥ 2 abnormal LUS patterns, and 93 (96.9%) had bilateral lung involvement. The proportion of bilateral or unilateral lung consolidation and pleural effusion in the critical COVID-19 group were higher than that in the severe group (P < .05). The lung consolidation of critical subjects showed a marked increase in most lung areas, including bilateral lateral lung, posterior lung, and left anterior-inferior lung area. The median (interquartile range) LUS scores of critical cases were higher than those of severe cases: left: 14 (12-17) vs 7 (5-12); right: 14 (10-16) vs 8 (3-12); bilateral: 28 (23-31) vs 15 (8-22) (P < .001 for all). There was a good correlation between the LUS score and the chest computed tomography score (r = 0.887, P < .001). CONCLUSIONS: The most common abnormal LUS pattern in subjects with severe and critical COVID-19 pneumonia was B-lines, followed by lung consolidation. Bedside LUS can provide important information for pulmonary involvement in patients with COVID-19.

Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information.

The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.

Prognostic value of bedside lung ultrasound score in patients with COVID-19.

BACKGROUND: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. METHOD: The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. RESULTS: The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. CONCLUSIONS: The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.

Deep Vein Thrombosis in Hospitalized Patients With COVID-19 in Wuhan, China: Prevalence, Risk Factors, and Outcome

BACKGROUND: To investigate deep vein thrombosis (DVT) in hospitalized patients with coronavirus disease 2019 (COVID-19), we performed a single institutional study to evaluate its prevalence, risk factors, prognosis, and potential thromboprophylaxis strategies in a large referral and treatment center. METHODS: We studied a total of 143 patients with COVID-19 from January 29, 2020 to February 29, 2020. Demographic and clinical data, laboratory data, including ultrasound scans of the lower extremities, and outcome variables were obtained, and comparisons were made between groups with and without DVT. RESULTS: Of the 143 patients hospitalized with COVID-19 (age 63±14 years, 74 [51.7%] men), 66 patients developed lower extremity DVT (46.1%: 23 [34.8%] with proximal DVT and 43 [65.2%] with distal DVT). Compared with patients who did not have DVT, patients with DVT were older and had a lower oxygenation index, a higher rate of cardiac injury, and worse prognosis, including an increased proportion of deaths (23 [34.8%] versus 9 [11.7%];P=0.001) and a decreased proportion of patients discharged (32 [48.5%] versus 60 [77.9%];P<0.001). Multivariant analysis showed an association only between CURB-65 (confusion status, urea, respiratory rate, and blood pressure) score 3 to 5 (odds ratio, 6.122;P=0.031), Padua prediction score ≥4 (odds ratio, 4.016;P=0.04), D-dimer >1.0 µg/mL (odds ratio, 5.818;P<0.014), and DVT in this cohort, respectively. The combination of a CURB-65 score 3 to 5, a Padua prediction score ≥4, and D-dimer >1.0 µg/mL has a sensitivity of 88.52% and a specificity of 61.43% for screening for DVT. In the subgroup of patients with a Padua prediction score ≥4 and whose ultrasound scans were performed >72 hours after admission, DVT was present in 18 (34.0%) patients in the subgroup receiving venous thromboembolism prophylaxis versus 35 (66.0%) patients in the nonprophylaxis group (P=0.010). CONCLUSIONS: The prevalence of DVT is high and is associated with adverse outcomes in hospitalized patients with COVID-19. Prophylaxis for venous thromboembolism may be protective in patients with a Padua protection score ≥4 after admission. Our data seem to suggest that COVID-19 is probably an additional risk factor for DVT in hospitalized patients.