Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: A prospective comparative study with chest computed tomography

Barbieri, Greta; Gargani, Luna; Lepri, Vittoria; Spinelli, Stefano; Romei, Chiara; Liperi, Annalisa De; Chimera, Davide; Pistelli, Francesco; Carrozzi, Laura; Corradi, Francesco; Ghiadoni, Lorenzo; Spataro, Joanne; Malacarne, Margherita; Addante, Elisabetta; Degl'Innocenti, Sabrina Agostini o; Carlo, Paolo De; Gregori, Alessio; Manieri, Sara; Deri, Chiara; Perelli, Sara; Sabattini, Arianna; Salemi, Simonetta; Volpi, Federica; Colligiani, Leonardo; Fanni, Salvatore Claudio; Tavanti, Laura; Pancani, Roberta; Desideri, Massimiliano; Carpenè, Nicoletta; Gabbrielli, Luciano; Celi, Alessandro; Fideli, Antonio; Cappiello, Chiara; Meschi, Claudia; Visconti, Luca; Manfredini, Giovanna; Aquilini, Ferruccio

doi:10.1016/j.ejim.2022.12.002

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…Previous evidence supports that pulmonary fibrotic sequelae after severe COVID-19 pneumonia can be detected by LUS [34,35]. In this study, we observed that LUS supplemented by SWE2D is related to the extent and regression of "fibrotic-like" involvement of the lung lobes after either moderate or severe COVID-19 pneumonia.…”

Section: Discussionsupporting

confidence: 76%

Lung Ultrasound Elastography by SWE2D and “Fibrosis-like” Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study

Paredes-Manjarrez,

Avelar-Garnica,

Balderas-Chairéz

et al. 2023

JCM

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The aim of this study was to assess the shear wave velocity by LUS elastography (SWE2D) for the evaluation of superficial lung stiffness after COVID-19 pneumonia, according to “fibrosis-like” signs found by Computed Tomography (CT), considering the respiratory function. Seventy-nine adults participated in the study 42 to 353 days from symptom onset. Paired evaluations (SWE2D and CT) were performed along with the assessment of arterial blood gases and spirometry, three times with 100 days in between. During the follow-up and within each evaluation, the SWE2D velocity changed over time (MANOVA, p < 0.05) according to the extent of “fibrosis-like” CT signs by lung lobe (ANOVA, p < 0.05). The variability of the SWE2D velocity was consistently related to the first-second forced expiratory volume and the forced vital capacity (MANCOVA, p < 0.05), which changed over time with no change in blood gases. Covariance was also observed with age and patients’ body mass index, the time from symptom onset until hospital admission, and the history of diabetes in those who required intensive care during the acute phase (MANCOVA, p < 0.05). After COVID-19 pneumonia, SWE2D velocity can be related to the extent and regression of “fibrotic-like” involvement of the lung lobes, and it could be a complementary tool in the follow-up after COVID-19 pneumonia.

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Section: Discussionsupporting

confidence: 76%

Lung Ultrasound Elastography by SWE2D and “Fibrosis-like” Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study

Paredes-Manjarrez,

Avelar-Garnica,

Balderas-Chairéz

et al. 2023

JCM

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“…УЗИ легких было изучено в ряде исследований и включено в диагностические протоколы при коронавирусной инфекции [27][28][29]. В диагностике пневмонии УЗИ легких может достигать 75-90 % специфичности и 65-95 % чувствительности [30][31][32][33]. Ультразвуковой метод исследования показал более высокие чувствительность, специфичность и точность диагностики поражения легких по сравнению с рентгеногра фией грудной клетки [34,35].…”

Section: Discussionunclassified

Comparison of ultrasound signs, computed tomography data and morphological examination of the lungs in patients with coronavirus infection: post hoc analysis

Lakhin,

Zhirnova,

Shchegolev

et al. 2023

Annals of Critical Care

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INTRODUCTION: Ultrasound examination (US) of the lungs has shown high efficiency in the diagnosis of COVID-19 pneumonia. The aim of the research was studying the correspondence of computed tomography (CT) US signs of the lungs and morphological data in patients with COVID-19 pneumonia. MATERIALS AND METHODS: The post hoc analysis included 388 patients who simultaneously underwent ultrasound and CT of the lungs. Lung ultrasound was performed according to the 16-zone “Russian Protocol”. Morphological data were obtained from the results of pathoanatomic examination of deceased patients. RESULTS: The comparison of signs detected by CT and ultrasound of the lungs was performed during a multidimensional correspondence analysis. The analysis was carried out using a three-dimensional solution that explained 64.9 % of inertia (p < 0.001). CT signs of “ground glass opacity” (100 %) corresponded to the B-line at ultrasound (100 %), CT of the consolidation sign (44.8 %) — ultrasound signs of consolidation (46.9 %), aerobronchogram of CT (34 %) — aerobronchogram of ultrasound (36.9 %), free liquid CT (11.1 %) — free liquid Ultrasound (13.9 %). CT signs of reticular changes (29.6 %) and “cobblestone pavement” (12.4 %) corresponded to various combinations of ultrasound signs of subpleural consolidation and B-lines. The B-lines were caused by the exudation of fluid and protein molecules into the intraalveolar space against the background of massive death of alveolocytes and formed by the development of intraalveolar edema and the formation of hyaline membranes. The ultrasonic sign of consolidation appeared in the airless zone of the lungs. Subpleural consolidation are caused by thickening and inflammatory infiltration of the pleura, diffuse alveolar damage, with intraalveolar edema, death and decay of alveolocytes, perivascular inflammatory cell reaction/ During treatment for more than 7 days, consolidation in the lung tissue developed due to the disorganization of the organ structure due to the progression of fibrosis. CONCLUSION: Multivariate correspondence analysis showed correlation of CT signs and US signs of the lungs. Morphological analysis showed polymorphism of histological data that caused the formation of ultrasound signs.

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“…As an alternative, lung ultrasound (LUS) is non-ionizing, largely available and not restricted to in-hospital use. Accordingly, it can be used in point-of-care settings for management of COVID-19-related acute respiratory disorders [7][8]. However, at the present, interpreting LUS images can be a challenging task and prone to operator variability.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 score severity prediction using 3D-based deep learning models on lung ultrasound video: could the system stand the test of time and of disease’s evolution?

Faita,

De Rosa,

Barbieri

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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The coronavirus disease 2019 (COVID-19) emerged in December 2019 and rapidly evolved into a global pandemic affecting more than 750 million people and causing more than 6,8 million deaths worldwide. The most important and often deadly manifestation of the disease is pneumonia. Among other imaging modalities, lung ultrasound (LUS) represents an efficient tool for management of COVID-19 patients, as it can be used to classify and monitor lung involvement, as well as to assess disease's severity. However, LUS requires a skilled sonographer to interpret findings. For this reason, we developed a Deep Learning (DL)-powered system, for multi-class automatic assessment of LUS videos in COVID-19 patients. We trained and tested (70,4% accuracy) our system with LUS images acquired from patients of the so-called first and second waves (2020). However, lung disease induced by COVID-19 is changed with time due to virus behavior, presence of vaccines and availability of therapies. Accordingly, we tested again performances of our system with LUS images of COVID-19 patients enrolled in 2022. Surprisingly, the performances of the system dramatically drop to an unsatisfiable accuracy of 48,3%. We speculate that this fact is primarily due to the differences in clinical features of patients hospitalized in 2020 and 2022. However, these findings also highlight a critical point on the management of DL systems designed for prognosis and diagnosis of rapidly-changing diseases. Accordingly, a continuous training update should be considered as the only viable strategy to preserve DL systems reliability and to improve the credibility of DL systems from the medical community.

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Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: A prospective comparative study with chest computed tomography

Cited by 8 publications

References 28 publications

Lung Ultrasound Elastography by SWE2D and “Fibrosis-like” Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study

Lung Ultrasound Elastography by SWE2D and “Fibrosis-like” Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study

Comparison of ultrasound signs, computed tomography data and morphological examination of the lungs in patients with coronavirus infection: post hoc analysis

COVID-19 score severity prediction using 3D-based deep learning models on lung ultrasound video: could the system stand the test of time and of disease’s evolution?

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