Complications in COVID-19 patients: Characteristics of pulmonary embolism

Cau, Riccardo; Pacielli, Alberto; Homayounieh, Fatemeh; Vaudano, Paolo Giacomo; Arru, Chiara; Crivelli, Paola; Stranieri, Giuseppe; Suri, Jasjit S.; Mannelli, Lorenzo; Conti, Maurizio; Mahammedi, Abdelkader; Kalra, Mannudeep K.; Saba, Luca

doi:10.1016/j.clinimag.2021.05.016

Cited by 34 publications

(51 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the evidence of direct invasion of the endothelium by SARS-CoV-2 ( 85 ) as well as the associated hypercoagulable state and increased risk of deep vein thrombosis and pulmonary embolism in acute infection ( 86 ), there is concern that venous thromboembolism (VTE) may play a role in a subset of patients with PASC. The prevalence of VTE in hospitalized acute COVID-19 patients may be as high as 25% ( 87 , 88 ) and one study found the rate of pulmonary embolism to be 24% for patients on the general wards and 49% for patients in the ICU ( 89 ). A meta-analysis of 3342 patients showed that pulmonary embolism and deep venous thrombosis occurred in 16.5% and 14.8%, primarily in the acute phase of infection, and more commonly in those admitted to the intensive care unit ( 90 ).…”

Section: Post-acute Sequelae Of Covid-19mentioning

confidence: 99%

CT of Post-Acute Lung Complications of COVID-19

et al. 2021

View full text Add to dashboard Cite

The acute course of coronavirus disease 2019 (COVID-19) is variable and ranges from asymptomatic infection to fulminant respiratory failure. Patients recovering from COVID-19 can have persistent symptoms and computed tomography (CT) abnormalities of variable severity. At 3 months after acute infection, a subset of patients will have CT abnormalities that include ground glass abnormalities (GGO) and subpleural bands with concomitant pulmonary function abnormalities. At 6 months after acute infection, some patients have persistent CT changes to include the resolution of GGOs seen in the early recovery phase and the persistence or development of changes suggestive of fibrosis such as reticulation with or without parenchymal distortion. Predictors of post-COVID lung disease include need for intensive care unit (ICU) admission, mechanical ventilation, higher inflammatory markers, longer hospital stay and a diagnosis of acute respiratory distress syndrome (ARDS). Treatments of post-COVID lung disease are being investigated with anti-fibrotic agents being investigated for the prevention of post-COVID lung fibrosis. The etiology of post-COVID lung disease may be a sequela of prolonged mechanical ventilation, COVID-induced ARDS or direct injury from the virus. Future research is needed to determine the long-term persistence of post-COVID lung disease, its impact on patients and ways to prevent or treat it.

show abstract

Section: Post-acute Sequelae Of Covid-19mentioning

confidence: 99%

CT of Post-Acute Lung Complications of COVID-19

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Let A ai (m, n) and A gt (n) represent the lung area using AI model 'm' and GT, respectively, for the image 'n'. Considering N as the total number of CT scans, the corresponding mean AI for model m and GT can be represented as A ai (m) and A gt , as defined in Equation ( 3), and the FoM can be expressed in Equation (5).…”

Section: Bland-altman Plots For Lung Long Axis Errormentioning

confidence: 99%

“…As per the WHO's statistics dated 28 July 2021, more than 196 million people have been infected with Acute Respiratory Distress Syndrome (ARDS), and nearly 4.2 million have lost their lives due to this virus [1]. This SARS-CoV-2 virus affects the respiratory system, damages lungs, travels through the entire body, and causes myocardial infarction or coronary artery syndrome [2,3] or worsening diabetes [4] or causing pulmonary embolism [5]. It was seen that comorbidity had a severe influence on COVID-19 [6].…”

Section: Introductionmentioning

confidence: 99%

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Suri¹,

Agarwal²,

Pathak

et al. 2021

Diagnostics

Self Cite

View full text Add to dashboard Cite

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

show abstract

“…If the total number of the image is represented by N in the database, A ai (m, n) represents lung area for in the image "n" using the AI model "m", A ai (m) represents the mean lung area corresponding to the AI model "m," and mean area of the GT binary mask is represented by A gt , then mathematically A ai (m) and A gt can be computed as shown in Equation (2).…”

Section: Lung Quantificationmentioning

confidence: 99%

“…More than 231 million people have been infected worldwide, and nearly 4.7 million people have died due to COVID-19 [1]. Although this "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) virus specifically targets the pulmonary and vascular system, it has the potential to travel through the body and lead to complications such as pulmonary embolism [2], myocardial infarction, stroke, or mesenteric ischemia [3][4][5]. Comorbidities such as diabetes mellitus, hypertension, and obesity substantially increase the severity and mortality of COVID-19 [6,7].…”

Section: Introductionmentioning

confidence: 99%

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

Self Cite

View full text Add to dashboard Cite

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

show abstract

Complications in COVID-19 patients: Characteristics of pulmonary embolism

Cited by 34 publications

References 51 publications

CT of Post-Acute Lung Complications of COVID-19

CT of Post-Acute Lung Complications of COVID-19

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Contact Info

Product

Resources

About