Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

Galli, Maria Giulia; Djurić, Olivera; Besutti, Giulia; Ottone, Marta; Amidei, Lucia; Bitton, Lee; Bonilauri, Carlotta; Boracchia, Luca; Campanale, Sergio; Curcio, Vittoria; Lucchesi, Davide Maria Francesco; Mulas, Cesare Salvatore; Santi, Francesca De; Ferrari, Anna; Rossi, Paolo Giorgi; Luppi, F

doi:10.1136/bmjopen-2021-052665

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…Moreover, as shown by Galli M.G. et al [ 35 ], quantifying the lung parenchyma affected by Sars-Cov-2 and knowing the time from symptom onset are the main determinants of hospital readmission in COVID-19 patients, resulting in a useful and helpful implementation of AI-based software for the future.…”

Section: Discussionmentioning

confidence: 99%

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

et al. 2022

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Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

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Section: Discussionmentioning

confidence: 99%

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

et al. 2022

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“…A multidisciplinary team (pulmonologists, infectious disease specialists, intensive care specialists and emergency physicians) established a six-class classification based on patient features, vital signs, medical history, symptoms, blood test results and instrumental findings to manage patients presenting to the ER [ 34 ]. Six clinical phenotypes were identified.…”

Section: Methodsmentioning

confidence: 99%

Complement activation predicts negative outcomes in COVID-19: The experience from Northen Italian patients.

Meroni

Croci

Lonati

et al. 2023

Autoimmunity Reviews

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“…Protocols for safe discharge of low risk COVID-19 patients from the ED combining clinical characteristics and imaging results have been used with good effect ( 141 , 142 ). The use of LUS as part of these clinical pathways improved prediction of hospitalisation and mortality.…”

Section: Discussionmentioning

confidence: 99%

Point-of-care ultrasound use in COVID-19: a narrative review

Chua,

Boon,

Yeoh

et al. 2024

Ann Transl Med

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Background and Objective The coronavirus disease 2019 (COVID-19) pandemic that began in early 2020 resulted in significant mortality from respiratory tract infections. Existing imaging modalities such as chest X-ray (CXR) lacks sensitivity in its diagnosis while computed tomography (CT) scan carries risks of radiation and contamination. Point-of-care ultrasound (POCUS) has the advantage of bedside testing with higher diagnostic accuracy. We aim to describe the various applications of POCUS for patients with suspected severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the emergency department (ED) and intensive care unit (ICU). Methods We performed literature search on the use of POCUS in the diagnosis and management of COVID-19 in MEDLINE, Embase and Scopus databases using the following search terms: “ultrasonography”, “ultrasound”, “COVID-19”, “SARS-CoV-2”, “SARS-CoV-2 variants”, “emergency services”, “emergency department” and “intensive care units”. Search was performed independently by two reviewers with any discrepancy adjudicated by a third member. Key Content and Findings Lung POCUS in patients with COVID-19 shows different ultrasonographic features from pulmonary oedema, bacterial pneumonia, and other viral pneumonia, thus useful in differentiating between these conditions. It is more sensitive than CXR, and more accessible and widely available than CT scan. POCUS can be used to diagnose COVID-19 pneumonia, screen for COVID-19-related pulmonary and extrapulmonary complications, and guide management of ICU patients, such as timing of ventilator weaning based on lung POCUS findings. Conclusions POCUS is a useful and rapid point-of-care modality that can be used to aid in diagnosis, management, and risk stratification of COVID-19 patients in different healthcare settings.

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Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

Cited by 6 publications

References 29 publications

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Complement activation predicts negative outcomes in COVID-19: The experience from Northen Italian patients.

Point-of-care ultrasound use in COVID-19: a narrative review

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