Post-acute COVID syndrome (long COVID): What should radiographers know and the potential impact for imaging services

Alghamdi, Fahad; Owen, Rebecca; Ashton, Ruth; Obotiba, A.; Meertens, Robert; Hyde, Emma; Faghy, Mark A; Knapp, Karen; Rogers, P.; Strain, W. David

doi:10.1016/j.radi.2022.08.009

Cited by 4 publications

(3 citation statements)

References 92 publications

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“…Patients infected with SARS-CoV-2, the virus that causes COVID-19, can experience long-term effects even if the virus is no longer detected with standard methods. 1 These effects are often referred to as ‘long COVID’, ‘post-COVID-19 syndrome’ or ‘postacute sequelae of SARS-CoV-2’, 2–4 and definitions may vary. According to the Centers for Disease Control and Prevention (CDC), patients with long COVID-19 continue to experience symptoms for ≥4 weeks after the initial infection with SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

Thematic analysis to explore patients’ experiences with long COVID-19: a conceptual model of symptoms and impacts on daily lives

Rofail,

Somersan-Karakaya,

Choi

et al. 2024

BMJ Open

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ObjectivesThere is limited qualitative research on patients’ experiences with long COVID-19, and how specific symptoms impact their daily lives. The study aimed to understand patients’ lived experiences of long COVID-19 and to develop a conceptual model representing the symptoms and their impact on overall quality of life.SettingQualitative study consisting of a comprehensive literature review, and in-depth clinician and patient semistructured interviews.ParticipantsForty-one adult patients with long COVID-19, of whom 18 (44%) were recruited through Regeneron Pharmaceuticals’s clinical trials and 23 (56%) through recruitment agencies; 85.4% were female and 73.2% were White. Five independent clinicians treating patients with long COVID-19 were interviewed. Concept saturation was also assessed.Primary and secondary outcomesInterview transcripts were analysed thematically to identify concepts of interest spontaneously mentioned by patients, including symptoms and their impacts on daily life, to guide the development of the conceptual model.ResultsFindings from the literature review and clinician and patient interviews resulted in the development of a conceptual model comprising two overarching domains: symptoms (upper respiratory tract, lower respiratory tract, smell and taste, systemic, gastrointestinal, neurocognitive and other) and impacts (activities of daily living, instrumental activities of daily living, physical impacts, emotional, social/leisure activities and professional impacts). Saturation was achieved for the reported impacts. The symptoms reported were heterogenic; neurocognitive symptoms, such as numbness, ringing in ears, haziness, confusion, forgetfulness/memory problems, brain fog, concentration, difficulties finding the right word and challenges with fine motor skills, were particularly pertinent for several months.ConclusionThe conceptual model, developed based on patient experience data of long COVID-19, highlighted numerous symptoms that impact patients’ physical and mental well-being, and suggests humanistic unmet needs. Prospective real-world studies are warranted to understand the pattern of long COVID-19 experienced in larger samples over longer periods of time.

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Section: Introductionmentioning

confidence: 99%

Thematic analysis to explore patients’ experiences with long COVID-19: a conceptual model of symptoms and impacts on daily lives

Rofail,

Somersan-Karakaya,

Choi

et al. 2024

BMJ Open

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“…The proposed framework is by no means perfect, but we hope the TRUDLMIA can contribute to the fight against pandemics, including post-acute COVID syndrome (long COVID) [49,50], and establish a viable path for future ones. In our future work, we are interested in investigating the use of Vision Transformers [51] to replace the CNNs used in the TRUDLMIA framework.…”

Section: Discussionmentioning

confidence: 99%

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis

Ma,

He,

Sinha

et al. 2023

Sensors

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Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to 21%) and model trust (up to 5%). We expect that the proposed framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises, improving patient outcomes, increasing diagnostic accuracy, and enhancing the overall quality of healthcare delivery.

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“…In cases of comorbidity with preexisting cardiopulmonary diseases, there is also an overlap with symptoms [ 5 ]. There has been growing evidence of residual lung changes in convalescent COVID-19 patients in the long term [ 6 , 7 , 8 , 9 , 10 , 11 ]. However, few studies classify and correlate persistent abnormalities with a distinct course of the SARS-CoV-2 infection with CT scores.…”

Section: Introductionmentioning

confidence: 99%

What Remains up to 7 Months after Severe and Moderate Pneumonia in Non-Vaccinated Patients with Long COVID? Results of a CT Study

Kurys-Denis,

Grzywa-Celińska,

Podgórska

et al. 2023

JCM

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Background: There is a growing evidence of long-lasting lung changes after COVID-19. Our aim was to assess the degree of lung injury and evaluate the recovery process of 4–7-month-non-vaccinated convalescent patients discharged from hospital after moderate and severe COVID-19 pneumonia, who presented with symptoms of long-COVID. Methods: On control lung CT after mean 5-month recovery period, we classified and determined the prevalence of residual radiological abnormalities in 39 symptomatic patients. To assess the advancement of the persisting changes we used the total severity score (TSS) and the chest CT score and then correlated the results with clinical data. Results and conclusions: On follow-up CT images, 94.9% of patients showed persistent radiological abnormalities. The most frequent changes were ground-glass opacities (74.4%), reticular pattern (64.1%), fibrotic changes (53.8%), nodules (33.3%), bronchiectasis (15.4%), vascular enlargement (10.3%), and cavitation (5.1%). The median TSS score was 4.1 points (interquartile range 3), whereas the median of the chest CT score 5.4 points (interquartile range of 4.5). No significant differences were observed between sex subgroups and between the severe and moderate course groups. There were no association between both CT scores and the severity of the initial disease, indicating that, mean 5 months after the disease, pulmonary abnormalities reduced to a similar stage in both subgroups of severity.

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Post-acute COVID syndrome (long COVID): What should radiographers know and the potential impact for imaging services

Cited by 4 publications

References 92 publications

Thematic analysis to explore patients’ experiences with long COVID-19: a conceptual model of symptoms and impacts on daily lives

Thematic analysis to explore patients’ experiences with long COVID-19: a conceptual model of symptoms and impacts on daily lives

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis

What Remains up to 7 Months after Severe and Moderate Pneumonia in Non-Vaccinated Patients with Long COVID? Results of a CT Study

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