The Importance of Radiological Patterns and Small Airway Disease in Long-Term Follow-Up of Postacute COVID-19: A Preliminary Study.

Postacute COVID-19 has become a relevant public health problem, and radiological and pulmonary function tests are tools that help physicians in decision-making. The objectives of this study are to characterize the findings and patterns on a chest radiograph (CXR) and computed tomography (CT) that are most important in the postacute phase and to evaluate how these changes correlate with clinical data, spirometry, and impulse oscillometry (IOS). This was a retrospective study of 29 patients who underwent CXR, CT, spirometry, and IOS. The inclusion criteria were age >18 years and persistent respiratory symptoms after four weeks. The exclusion criteria were radiological exams with low technical quality and non-COVID-19 acute lung diseases. The inferential analysis was carried out with the chi-square (χ 2) or Fisher's exact test to evaluate the interrelationships between the clinical and COVID-19 variables according to spirometry, IOS, CT, and CXR. In our sample, 19 patients were women (65.5%). The predominance of abnormal spirometry was associated with CT's moderate/severe degree of involvement (p = 0.017; 69.2%, CI 95%: 44.1%-94.3%). There was no significant association between IOS and tomographic and radiographic parameters. A significant association was found between the classifications of the moderate/severe and normal/mild patterns on CT and CXRs (p = 0.003; 93.3%, CI 95%: 77.8%-100%). Patients with moderate/severe impairment on CXR were associated with a higher frequency of hospitalization (p = 0.033; 77.8%, CI 95%: 58.6%-97.0%) and had significantly more moderate/severe classifications in the acute phase than the subgroup with normal/mild impairment on CXR (p = 0.017; 88.9%, CI 95%: 74.4%-100%). In conclusion, the results of this study show that CXR is a relevant examination and may be used to detect nonspecific alterations during the follow-up of post-COVID-19 patients. Small airway disease is an important finding in postacute COVID-19 syndrome, and we postulate a connection between this pattern and the persistently low-level inflammatory state of the lung.

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning.

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19.

An emergency system for monitoring pulse oximetry, peak expiratory flow, and body temperature of patients with COVID-19 at home: Development and preliminary application.

BACKGROUND: COVID-19 is characterized by a rapid change in the patient's condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes. METHODOLOGY/PRINCIPAL FINDINGS: The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient's condition and help to send medical recommendations. The home unit allows the data transmission from the patient to the hospital, which is performed using a cell phone application. The hospital unit includes a virtual instrument developed in LabVIEW® environment that can provide a real-time monitoring of the oxygen saturation (SpO2), beats per minute (BPM), body temperature (BT), and peak expiratory flow (PEF). Abnormal events may be fast and automatically identified. After the design details are described, the system is validated by a 30-day home monitoring study in 12 controls and 12 patients with COVID-19 presenting asymptomatic to mild disease. Patients presented reduced SpO2 (p<0.0001) and increased BPM values (p<0.0001). Three patients (25%) presented PEF values between 50 and 80% of the predicted. Three of the 12 monitored patients presented events of desaturation (SpO2<92%). The experimental results were in close agreement with the involved pathophysiology, providing clear evidence that the proposed system can be a useful tool for the remote monitoring of patients with COVID-19. CONCLUSIONS: An emergency system for home monitoring of patients with COVID-19 was developed in the current study. The proposed system allowed us to quickly respond to early abnormalities in these patients. This system may contribute to conserving hospital resources for those most in need while simultaneously enabling early recognition of patients under acute deterioration, requiring urgent assessment.

Chest computed tomography in COVID-19 pneumonia: a retrospective study of 155 patients at a university hospital in Rio de Janeiro, Brazil.

OBJECTIVE: To define diagnostic criteria for coronavirus disease 2019 (COVID-19) on computed tomography (CT); to study the correlation between CT and polymerase chain reaction (PCR) testing for infection with severe acute respiratory syndrome coronavirus 2; and to determine whether the extent of parenchymal involvement and the need for mechanical ventilation are associated with the CT findings and clinical characteristics of patients with COVID-19. MATERIALS AND METHODS: This was a retrospective study of 155 patients with COVID-19 treated between March and May 2020. We attempted to determine whether the CT findings correlated with age and clinical variables, as well as whether the need for mechanical ventilation correlated with the extent of the pulmonary involvement. RESULTS: On average, the patients with COVID-19 were older than were those without (mean age, 54.8 years vs. 45.5 years; p = 0.031). The most common CT finding (seen in 88.6%) was ground-glass opacity, which correlated significantly with a diagnosis of COVID-19 (p = 0.0001). The CT findings that correlated most strongly with the need for mechanical ventilation were parenchymal bands (p = 0.013), bronchial ectasia (p = 0.046), and peribronchovascular consolidations (p = 0.012). The presence of one or more comorbidities correlated significantly with more extensive parenchymal involvement (p = 0.023). For the diagnosis of COVID-19, CT had a sensitivity of 84.3%, a specificity of 36.7%, and an accuracy of 73.5% (p = 0.012 vs. PCR). CONCLUSION: The patterns of CT findings are useful for the diagnosis of COVID-19 and the evaluation of disease severity criteria. The presence of any comorbidity is associated with greater severity of COVID-19.

OBJETIVO: Definir os critérios diagnósticos da COVID-19 na tomografia computadorizada (TC), estudar a concordância entre a TC e o PCR e determinar a associação da extensão do envolvimento parenquimatoso e ventilação mecânica com os achados tomográficos e características clínicas da amostra. MATERIAIS E MÉTODOS: Estudo de 155 pacientes com COVID-19 atendidos entre março e maio de 2020. Tentamos determinar se os achados da TC se correlacionavam com a idade e variáveis clínicas, bem como se a necessidade de ventilação mecânica se correlacionava com a extensão do envolvimento pulmonar. RESULTADOS: A faixa etária dos pacientes com COVID-19 (54,8 anos) foi maior do que a dos pacientes sem a doença (45,5 anos) (p = 0,031). Opacidades em vidro fosco foram as alterações mais frequentes (88,6%; p = 0,0001). Bandas parenquimatosas (p = 0,013), ectasia brônquica (p = 0,046) e consolidações peribroncovasculares (p = 0,012) foram mais frequentes nos pacientes que necessitaram de ventilação mecânica. Pacientes com comorbidades apresentaram envolvimento parenquimatoso mais extenso (p = 0,023). A TC apresentou sensibilidade de 84,3%, especificidade de 36,7% e acurácia de 73,5% (p = 0,012). CONCLUSÃO: Os padrões de TC são úteis para o diagnóstico de COVID-19 e avaliação dos critérios de gravidade da doença. Pacientes com comorbidades estão associados a condições clínicas mais graves.

Comparison Between Lung Ultrasound and Computed Tomographic Findings in Patients With COVID-19 Pneumonia.

OBJECTIVES: The aim of this study was to describe findings from lung ultrasound (LUS) and computed tomography (CT) in health professionals with coronavirus disease 2019 pneumonia and to evaluate the associations of the findings of both tests. METHODS: This cross-sectional observational study evaluated 45 health professionals who were initially seen in screening tents and had a diagnosis of coronavirus disease 2019 as confirmed by a reverse transcription polymerase chain reaction and lung involvement diagnosed by LUS. Subsequently, these individuals were admitted to the hospital, where chest CT was performed. Aeration scores were obtained for the LUS examinations based on the following findings: more than 2 B-lines, coalescent B-lines, and subpleural consolidations. A subjective assessment of the extent of lung disease on CT was performed on the basis of the percentage of lung parenchyma involvement as follows: 25% or less, 25% to 50%, and greater than 50%. RESULTS: Regarding LUS signs, more than 2 B-lines, coalescent B-lines, and subpleural consolidations were present in 73.3%, 68.2%, and 24.4% of cases, respectively. The main findings on CT were ground glass opacities, a crazy-paving pattern, and consolidations (66.7%, 20%, and 20% of cases); 17.8% of cases had examinations without abnormalities. Patients with more than 2 B-lines on LUS had more ground glass opacity areas on CT (P = .0007), whereas patients with subpleural consolidations on LUS had more consolidations on CT (P < .0001). In addition, patients with higher LUS aeration scores had more extensive disease on CT (P < .0001). CONCLUSIONS: Lung ultrasound can detect lung injury even in the presence of normal CT results. There are associations between the abnormalities detected by both methods, and a relationship also exists between LUS aeration scores and the disease extent on CT.