Lung Ultrasound Findings in Patients Hospitalized With COVID-19

Role of Lung Ultrasound in the Follow-Up of Children with Previous SARS-CoV-2 Infection: A Case-Control Assessment of Children with Long COVID or Fully Recovered

Lung ultrasound (LUS) can detect lower respiratory tract involvement in children with acute SARS-CoV-2 infection. However, its role in follow-up assessments is still unclear. To describe LUS findings in children after SARS-CoV-2 infection, we conducted a prospective study in a population of pediatric patients referred to the post-COVID unit in a tertiary center during the study period from February 2021 to May 2022. Children were classified as recovered from acute infection or with persisting symptoms. LUS was performed in all children and a LUS score (ranging from 0 to 36 points) was calculated according to the Italian Academy of Thoracic Ultrasound. Six hundred forty-seven children (304 females, 47%) were enrolled. The median follow-up evaluation was two months. The median age was 7.9 (IQR: 6) years. At the follow-up evaluation, 251 patients (38.8%) had persistent symptoms, of whom 104 (16.1%) had at least one respiratory symptom. The median LUS level was 2 (IQR: 4). LUS findings and LUS scores did not differ in children with Long COVID compared to the group of children fully recovered from the initial infection. In conclusion, after SARS-CoV-2 infection, LUS was mostly normal or showed minimal artifacts in all groups of children.

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/m2. 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.

Rapid 8-Zone Lung Ultrasound Protocol is Comparable to a Full 12-Zone Protocol for Outcome Prediction in Hospitalized COVID-19 Patients

OBJECTIVES

Safety precautions limit the clinical assessment of hospitalized Coronavirus disease 2019 (COVID-19) patients. The minimal exposure required to perform lung ultrasound (LUS) paired with its high accuracy, reproducibility, and availability make it an attractive solution for initial assessment of COVID-19 patients. We aim to evaluate whether the association between sonographic findings and clinical outcomes among COVID 19 patients is comparable between the validated 12-zone protocol and a shorter, 8-zone protocol, in which the posterior lung regions are omitted.

METHODS

One hundred and one COVID-19 patients hospitalized in a dedicated COVID-19 ward in a tertiary referral hospital were examined upon admission and scored by 2 LUS protocols. The association between the scores and a composite outcome consisting of death, transfer to the intensive care unit (ICU) or initiation of invasive or noninvasive mechanical ventilation was estimated and compared.

RESULTS

LUS scores in both the 8- and the 12-zone protocols were associated with the composite outcome during hospitalization (hazard ratio [HR] 1.21 [1.03-1.42, P = .022] and HR 1.13 [1.01-1.27, P = .037], respectively). The observed difference in the discriminatory ROC-AUC values for the 8- and 12-zone scores was not significant (0.767 and 0.754 [P = .647], respectively).

CONCLUSION

A short 8-zone LUS protocol is as accurate as the previously validated, 12-zone protocol for prognostication of clinical deterioration in nonventilated COVID-19 patients.

Outcomes of Simplified Lung Ultrasound Exam in COVID-19: Implications for Self-Imaging

OBJECTIVES

Lung ultrasound B-lines represent interstitial thickening or edema and relate to mortality in COVID-19. As B-lines can be detected with minimal training using point-of-care ultrasound (POCUS), we examined the frequency, clinical associations, and outcomes of B-lines when found using a simplified POCUS method in acutely ill patients with COVID-19.

METHODS

In this retrospective cohort study, hospital data from COVID-19 patients who had undergone lung imaging during standard echocardiography or POCUS were reviewed for an ultrasound lung comet (ULC) sign, defined as the presence of ≥3 B-lines from images of only the antero-apex of either lung (ULC+). Clinical risk factors, oximetry and radiographic results, and disease severity were analyzed for associations with ULC+. Clinical risk factors and ULC+ were analyzed for associations with hospital mortality or the need for intensive care in multivariable models.

RESULTS

Of N = 160 patients, age (mean ± standard deviation) was 64.8 ± 15.5 years, and 46 (29%) died. ULC+ was present in 100/160 (62%) of patients overall, in 81/103 (79%) of severe-or-greater disease versus 19/57 (33%) of moderate-or-less disease (P < .0001) and was associated with mortality (odds ratio [OR] = 2.4 [95% confidence interval [CI]: 1.1-5.4], P = .02) and the need for intensive care (OR = 5.23 [95% CI: 2.42-12.40], P < .0001). In the multivariable models, symptom duration and severe-or-greater disease were associated with ULC+, and ULC+, diabetes, and symptom duration were associated with the need for intensive care.

CONCLUSIONS

B-lines in the upper chest were common and related to disease severity, intensive care, and hospital mortality in COVID-19. Validation of a simplified lung POCUS exam could provide the evidence basis for a self-imaging application during the pandemic.

Point-of-Care Ultrasound Predicts Clinical Outcomes in Patients With COVID-19

OBJECTIVES

Point-of-care ultrasound (POCUS) detects the pulmonary manifestations of COVID-19 and may predict patient outcomes.

METHODS

We conducted a prospective cohort study at four hospitals from March 2020 to January 2021 to evaluate lung POCUS and clinical outcomes of COVID-19. Inclusion criteria included adult patients hospitalized for COVID-19 who received lung POCUS with a 12-zone protocol. Each image was interpreted by two reviewers blinded to clinical outcomes. Our primary outcome was the need for intensive care unit (ICU) admission versus no ICU admission. Secondary outcomes included intubation and supplemental oxygen usage.

RESULTS

N = 160 patients were included. Among critically ill patients, B-lines (94 vs 76%; P < .01) and consolidations (70 vs 46%; P < .01) were more common. For scans collected within 24 hours of admission (N = 101 patients), early B-lines (odds ratio [OR] 4.41 [95% confidence interval, CI: 1.71-14.30]; P < .01) or consolidations (OR 2.49 [95% CI: 1.35-4.86]; P < .01) were predictive of ICU admission. Early consolidations were associated with oxygen usage after discharge (OR 2.16 [95% CI: 1.01-4.70]; P = .047). Patients with a normal scan within 24 hours of admission were less likely to require ICU admission (OR 0.28 [95% CI: 0.09-0.75]; P < .01) or supplemental oxygen (OR 0.26 [95% CI: 0.11-0.61]; P < .01). Ultrasound findings did not dynamically change over a 28-day scanning window after symptom onset.

CONCLUSIONS

Lung POCUS findings detected within 24 hours of admission may provide expedient risk stratification for important COVID-19 clinical outcomes, including future ICU admission or need for supplemental oxygen. Conversely, a normal scan within 24 hours of admission appears protective. POCUS findings appeared stable over a 28-day scanning window, suggesting that these findings, regardless of their timing, may have clinical implications.

Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images

Purpose

In this study we aimed to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of SARS-CoV-2 virus syndrome on Lung ultrasonography (LUS).

Method

A CAD system is developed based on a transfer learning of a residual network (ResNet) to extract features on LUS and help radiologists to distinguish SARS-CoV-2 virus syndrome from healthy and non-SARS-CoV-2 pneumonia. A publicly available LUS dataset for SARS-CoV-2 virus syndrome consisting of 3909 images has been employed. Six radiologists with different experiences participated in the experiment. A comprehensive LUS data set was constructed and employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-score, are used to evaluate the performance of the proposed CAD approach. The performances of the radiologists with and without the help of CAD are also evaluated quantitively. The p-values of the t-test shows that with the help of the CAD system, both junior and senior radiologists significantly improve their diagnosis performance on both balanced and unbalanced datasets.

Results

Experimental results indicate the proposed CAD approach and the machine features from it can significantly improve the radiologists’ performance in the SARS-CoV-2 virus syndrome diagnosis. With the help of the proposed CAD system, the junior and senior radiologists achieved F1-score values of 91.33% and 95.79% on balanced dataset and 94.20% and 96.43% on unbalanced dataset. The proposed approach is verified on an independent test dataset and reports promising performance.

Conclusions

The proposed CAD system reports promising performance in facilitating radiologists’ diagnosis SARS-CoV-2 virus syndrome and might assist the development of a fast, accessible screening method for pulmonary diseases.