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1.
Turk J Med Sci ; 51(4): 1665-1674, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1526879

ABSTRACT

Background/aim: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the predictors of mortality in critically ill patients admitted to the intensive care unit (ICU) are not yet well understood. In this study, we aimed to investigate the risk factors associated with ICU mortality in our hospital. Materials and methods: In this single-centered retrospective study, we enrolled 86 critically ill adult patients with COVID-19 admitted to ICU of Dokuz Eylül University Hospital (Izmir, Turkey) between 18 March 2020 and 31 October 2020. Data on demographic information, preexisting comorbidities, treatments, the laboratory findings at ICU admission, and clinical outcomes were collected. The chest computerized tomography (CT) of the patients were evaluated specifically for COVID-19 and CT score was calculated. Data of the survivors and nonsurvivors were compared with survival analysis to identify risk factors of mortality in the ICU. Results: The mean age of the patients was 71.1 ± 14.1 years. The patients were predominantly male. The most common comorbidity in patients was hypertension. ICU mortality was 62.8%. Being over 60 years old, CT score > 15, acute physiology and chronic health evaluation (APACHE) II score ≥ 15, having dementia, treatment without favipiravir, base excess in blood gas analysis ≤ ­2.0, WBC > 10,000/mm3, D-dimer > 1.6 µg/mL, troponin > 24 ng/L, Na ≥ 145 mmol/L were considered to link with ICU mortality according to Kaplan­Meier curves (log-rank test, p < 0.05). The APACHE II score (HR: 1.055, 95% CI: 1.021­1.090) and chest CT score (HR: 2.411, 95% CI:1.193­4.875) were associated with ICU mortality in the cox proportional-hazard regression model adjusted for age, dementia, favipiravir treatment and troponin. Howewer, no difference was found between survivors and nonsurvivors in terms of intubation timing. Conclusions: COVID-19 patients have a high ICU admission and mortality rate. Studies in the ICU are also crucial in this respect. In our study, we investigated the ICU mortality risk factors of COVID-19 patients. We determined a predictive mortality model consisting of APACHE II score and chest CT score. It was thought that this feasible and practical model would assist in making clinical decisions.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/mortality , Critical Care/methods , Hospital Mortality , Intubation, Intratracheal/methods , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Intensive Care Units , Intubation, Intratracheal/statistics & numerical data , Lung/diagnostic imaging , Male , Middle Aged , Retrospective Studies , Risk Factors , SARS-CoV-2 , Survival Analysis , Time Factors , Turkey/epidemiology , Young Adult
2.
IEEE Rev Biomed Eng ; 14: 16-29, 2021.
Article in English | MEDLINE | ID: covidwho-1501334

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.


Subject(s)
COVID-19/diagnosis , Artificial Intelligence , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/virology , Positron Emission Tomography Computed Tomography/methods , SARS-CoV-2/pathogenicity , Tomography, X-Ray Computed/methods , Ultrasonography/methods
3.
IEEE Rev Biomed Eng ; 14: 4-15, 2021.
Article in English | MEDLINE | ID: covidwho-1501333

ABSTRACT

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.


Subject(s)
COVID-19/diagnosis , SARS-CoV-2/pathogenicity , Artificial Intelligence , Humans , Pandemics/prevention & control , Tomography, X-Ray Computed/methods
4.
J Comput Assist Tomogr ; 44(5): 627-632, 2020.
Article in English | MEDLINE | ID: covidwho-1501243

ABSTRACT

OBJECTIVE: To determine the predictive computed tomography (CT) and clinical features for diagnosis of COVID-19 pneumonia. METHODS: The CT and clinical data including were analyzed using univariate analysis and multinomial logistic regression, followed by receiver operating characteristic curve analysis. RESULTS: The factors including size of ground grass opacity (GGO), GGO with reticular and/or interlobular septal thickening, vascular enlargement, "tree-in-bud" opacity, centrilobular nodules, and stuffy or runny nose were associated with the 2 groups of viral pneumonia, as determined by univariate analysis (P < 0.05). Only GGO with reticular and/or interlobular septal thickening, centrilobular nodules, and stuffy or runny nose remained independent risk factors in multinomial logistic regression analysis. Receiver operating characteristic curve analysis showed that the area under curve of the obtained logistic regression model was 0.893. CONCLUSION: Computed tomography and clinical features including GGO with reticular and/or interlobular septal thickening, absence of centrilobular nodules, and absence of stuffy or runny nose are potential patients with COVID-19 pneumonia.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Lung/diagnostic imaging , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed/methods , Adult , COVID-19 , COVID-19 Testing , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , Predictive Value of Tests , Reproducibility of Results , Retrospective Studies , SARS-CoV-2
5.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
6.
PLoS One ; 16(10): e0258351, 2021.
Article in English | MEDLINE | ID: covidwho-1496507

ABSTRACT

BACKGROUND: Elevated D-dimer is known as predictor for severity of SARS-CoV2-infection. Increased D-dimer is associated with thromboembolic complications, but it is also a direct consequence of the acute lung injury seen in COVID-19 pneumonia. OBJECTIVES: To evaluate the rate of persistent elevated D-dimer and its association with thromboembolic complications and persistent ground glass opacities (GGO) after recovery from COVID-19. METHODS: In this post hoc analysis of a prospective multicenter trial, patients underwent blood sampling, measurement of diffusion capacity, blood gas analysis, and multidetector computed tomography (MDCT) scan following COVID-19. In case of increased D-dimer (>0,5 µg/ml), an additional contrast medium-enhanced CT was performed in absence of contraindications. Results were compared between patients with persistent D-dimer elevation and patients with normal D-dimer level. RESULTS: 129 patients (median age 48.8 years; range 19-91 years) underwent D-Dimer assessment after a median (IQR) of 94 days (64-130) following COVID-19. D-dimer elevation was found in 15% (19/129) and was significantly more common in patients who had experienced a severe SARS-CoV2 infection that had required hospitalisation compared to patients with mild disease (p = 0.049). Contrast-medium CT (n = 15) revealed an acute pulmonary embolism in one patient and CTEPH in another patient. A significant lower mean pO2 (p = 0.015) and AaDO2 (p = 0.043) were observed in patients with persistent D-Dimer elevation, but the rate of GGO were similar in both patient groups (p = 0.33). CONCLUSION: In 15% of the patients recovered from COVID-19, persistent D-dimer elevation was observed after a median of 3 months following COVID-19. These patients had experienced a more severe COVID and still presented more frequently a lower mean pO2 and AaDO2.


Subject(s)
COVID-19/metabolism , Fibrin Fibrinogen Degradation Products/analysis , Adult , Aged , Aged, 80 and over , Biomarkers/blood , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , Male , Middle Aged , Prospective Studies , Pulmonary Embolism/prevention & control , RNA, Viral , Retrospective Studies , SARS-CoV-2/pathogenicity , Severity of Illness Index , Tomography, X-Ray Computed/methods
7.
Comput Math Methods Med ; 2021: 6919483, 2021.
Article in English | MEDLINE | ID: covidwho-1484105

ABSTRACT

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Algorithms , Computational Biology , Databases, Factual/statistics & numerical data , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Early Diagnosis , Humans , Lung/diagnostic imaging , Neural Networks, Computer , Pandemics , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data
8.
Pan Afr Med J ; 39: 273, 2021.
Article in French | MEDLINE | ID: covidwho-1472500

ABSTRACT

Acute mesenteric ischemia (AMI) is due to a sudden decrease or interruption of mesenteric blood flow resulting in inadequate blood supply to the gastrointestinal tract. This causes ischemic and inflammatory lesions often progressing to necrosis in the absence of appropriate treatment. Vascular insufficiency may arise as a result of embolism or arterial thrombosis or venous thrombosis. We here report a rare case of mesenteric venous ischemia caused by coronavirus disease 2019 (COVID-19) in a 33-year-old man in whom diagnosis was based on ultrasound and, in particular, on computed tomography (CT).


Subject(s)
COVID-19/complications , Intestines/blood supply , Mesenteric Ischemia/etiology , Tomography, X-Ray Computed/methods , Venous Thrombosis/complications , Abdominal Pain/etiology , Adult , COVID-19/diagnosis , Humans , Male , Mesenteric Ischemia/diagnostic imaging , Mesentery/blood supply , Portal Vein/diagnostic imaging , SARS-CoV-2 , Venous Thrombosis/diagnosis
9.
Biomed Res Int ; 2021: 5122962, 2021.
Article in English | MEDLINE | ID: covidwho-1467752

ABSTRACT

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method's performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/methods , COVID-19/metabolism , COVID-19/pathology , COVID-19/virology , Data Accuracy , Databases, Factual , Deep Learning , Humans , Image Processing, Computer-Assisted/methods , Models, Theoretical , Reproducibility of Results , Research Design , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
13.
Proc Natl Acad Sci U S A ; 118(43)2021 10 26.
Article in English | MEDLINE | ID: covidwho-1462067

ABSTRACT

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global threat to human health and life. A useful pathological animal model accurately reflecting human pathology is needed to overcome the COVID-19 crisis. In the present study, COVID-19 cynomolgus monkey models including monkeys with underlying diseases causing severe pathogenicity such as metabolic disease and elderly monkeys were examined. Cynomolgus macaques with various clinical conditions were intranasally and/or intratracheally inoculated with SARS-CoV-2. Infection with SARS-CoV-2 was found in mucosal swab samples, and a higher level and longer period of viral RNA was detected in elderly monkeys than in young monkeys. Pneumonia was confirmed in all of the monkeys by computed tomography images. When monkeys were readministrated SARS-CoV-2 at 56 d or later after initial infection all of the animals showed inflammatory responses without virus detection in swab samples. Surprisingly, in elderly monkeys reinfection showed transient severe pneumonia with increased levels of various serum cytokines and chemokines compared with those in primary infection. The results of this study indicated that the COVID-19 cynomolgus monkey model reflects the pathophysiology of humans and would be useful for elucidating the pathophysiology and developing therapeutic agents and vaccines.


Subject(s)
COVID-19/immunology , Disease Models, Animal , Macaca fascicularis/immunology , Primate Diseases/immunology , SARS-CoV-2/immunology , Animals , Antibodies, Viral/blood , Antibodies, Viral/immunology , COVID-19/virology , Female , Humans , Immunoglobulin G/blood , Immunoglobulin G/immunology , Lung/diagnostic imaging , Lung/immunology , Lung/virology , Macaca fascicularis/virology , Male , Primate Diseases/virology , SARS-CoV-2/physiology , Tomography, X-Ray Computed/methods , Virus Shedding/immunology , Virus Shedding/physiology
14.
Pan Afr Med J ; 39: 230, 2021.
Article in French | MEDLINE | ID: covidwho-1464030

ABSTRACT

Introduction: the main purpose of this study is to describe chest computed tomography (CT) findings in 26 patients hospitalized with COVID-19 pneumonia during the first wave of the SARS-CoV-2 pandemic at the University Clinics in Kinshasa (UCK). Methods: we conducted a descriptive study of chest CT findings in 26 patients hospitalized with coronavirus pneumonia at the UCK over a 9-month period, from March 17 to November 17, 2020. Hitachi - CT-scanner 16 slice was used in all our patients. After analyzing lesions, these were divided into lesions suggestive and non-suggestive of SARS-CoV-2 infection. Results: the average age of patients was 53.02 years. Male sex was the most affected (76.9%). Respiratory distress was the most common clinical symptom (61.5%). Arterial hypertension and renal failure were the most common comorbidities (3O% and 6%). Bilateral ground-glass opacities, with a predominantly peripheral distribution, accounted for 69.2% of cases, followed by condensations (57.7%) and crazy paving (19.2%). Severe COVID-19 was most frequently found (34.61%). Distal and proximal pulmonary embolism was the most common complication (11.5%). Among the associated diseases, pleurisy and pulmonary PAH were most frequently found (30.8%). The majority of our patients had parenchymal lung lesions, corresponding to early-stage disease on CT (50%). Conclusion: at the UCK, during the first wave of SARS-CoV-2 pandemic, lesions on CT suggestive of COVID-19 were dominated by plaque-like ground-glass opacities, followed by nonsystematized parenchymatous condensations and crazy paving. The less observed atypical lesions consisted of unilateral, peribronchovascular pseudo-nodular condensations and infection in the remodeled lung. Severe COVID-19 was the most common CT finding. Proximal and distal pulmonary embolism was the most common complication. This study highlights that these findings are consistent with those reported in the literature.


Subject(s)
COVID-19/complications , Hospitalization , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , COVID-19/diagnostic imaging , Child , Child, Preschool , Democratic Republic of the Congo , Female , Humans , Infant , Male , Middle Aged , Pneumonia, Viral/virology , Severity of Illness Index , Sex Distribution , Young Adult
15.
Nutr Metab Cardiovasc Dis ; 31(11): 3227-3235, 2021 10 28.
Article in English | MEDLINE | ID: covidwho-1461718

ABSTRACT

BACKGROUND AND AIMS: It is known that the highest COVID-19 mortality rates are among patients who develop severe COVID-19 pneumonia. However, despite the high sensitivity of chest CT scans for diagnosing COVID-19 in a screening population, the appearance of a chest CT is thought to have low diagnostic specificity. The aim of this retrospective case-control study is based on evaluation of clinical and radiological characteristics in patients with COVID-19 (n = 41) and no-COVID-19 interstitial pneumonia (n = 48) with mild-to-moderate symptoms. METHODS AND RESULTS: To this purpose we compared radiological, clinical, biochemical, inflammatory, and metabolic characteristics, as well as clinical outcomes, between the two groups. Notably, we found similar radiological severity of pneumonia, which we quantified using a disease score based on a high-resolution computed tomography scan (COVID-19 = 18.6 ± 14.5 vs n-COVID-19 = 23.2 ± 15.2, p = 0.289), and comparable biochemical and inflammatory characteristics. However, among patients without diabetes, we observed that COVID-19 patients had significantly higher levels of HbA1c than n-COVID-19 patients (COVID-19 = 41.5 ± 2.6 vs n-COVID-19 = 38.4 ± 5.1, p = 0.012). After adjusting for age, sex, and BMI, we found that HbA1c levels were significantly associated with the risk of COVID-19 pneumonia (odds ratio = 1.234 [95%CI = 1.051-1.449], p = 0.010). CONCLUSIONS: In this retrospective case-control study, we found similar radiological and clinical characteristics in patients with COVID-19 and n-COVID-19 pneumonia with mild-to-moderate symptoms. However, among patients without diabetes HbA1c levels were higher in COVID-19 patients than in no-COVID-19 individuals. Future studies should assess whether reducing transient hyperglycemia in individuals without overt diabetes may lower the risk of SARS-CoV-2 infection.


Subject(s)
COVID-19/diagnostic imaging , Lung Diseases, Interstitial/diagnostic imaging , Pneumonia/diagnostic imaging , Aged , Aged, 80 and over , COVID-19/blood , Case-Control Studies , Diabetes Mellitus/blood , Female , Glycated Hemoglobin A/analysis , Humans , Lung Diseases, Interstitial/blood , Male , Middle Aged , Pneumonia/blood , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed/methods
16.
IEEE Trans Neural Netw Learn Syst ; 32(11): 4781-4792, 2021 11.
Article in English | MEDLINE | ID: covidwho-1455468

ABSTRACT

Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a variety of complex appearances, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. To solve this problem, we propose an adaptive attention network (AANet), which can adaptively extract the characteristic radiographic findings of COVID-19 from the infected regions with various scales and appearances. It contains two main components: an adaptive deformable ResNet and an attention-based encoder. First, the adaptive deformable ResNet, which adaptively adjusts the receptive fields to learn feature representations according to the shape and scale of infected regions, is designed to handle the diversity of COVID-19 radiographic features. Then, the attention-based encoder is developed to model nonlocal interactions by self-attention mechanism, which learns rich context information to detect the lesion regions with complex shapes. Extensive experiments on several public datasets show that the proposed AANet outperforms state-of-the-art methods.


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/classification , Tomography, X-Ray Computed/standards , COVID-19/epidemiology , Databases, Factual/standards , Humans , Tomography, X-Ray Computed/methods , X-Rays
17.
Eur J Radiol ; 129: 109099, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-1454122

ABSTRACT

PURPOSE: The superior diagnostic accuracy of CT makes it an attractive tool for initial trauma imaging. This meta-analysis aimed to assess the evidence regarding the value of whole-body CT (WBCT) as part of the primary survey, in comparison to conventional radiological procedures. METHODS: A comprehensive systematic search of the literature was conducted using keywords applied in Scopus, Cochrane and PubMed databases. Articles were eligible if they contained original data comparing the use of WBCT in the primary survey, with conventional radiological procedures. Outcomes included overall and 24 -h mortality, emergency department (ED) time, intensive care unit (ICU) and hospital length of stay (LOS), and multiple organ dysfunction syndrome/failure (MODS/MOF) incidence. Radiation dose, mechanical ventilation duration and cost were evaluated qualitatively. Analysis was performed with Covidence, MedCalc Version 19.1.3. and Meta-Essentials. RESULTS: Fourteen studies were included. Statistical pooling demonstrated comparable rates between conventional procedures and WBCT (OR = 0.854, CI = 0.715-1.021, p = 0.083) in 63,529 patients across 11 studies. A significant finding favouring WBCT was discovered for ED time (SMD = -0.709, CI -1.198 to -0.220, p = 0.004). Patients experienced similar 24 -h mortality rates (p = 0.450), MODS/MOF incidence (p = 0.274), and hospital (p = 0.541) and ICU LOS (p = 0.457). WBCT is associated with increased radiation dose and mechanical ventilation duration. CONCLUSION: This review demonstrates that WBCT markedly reduces time spent in ED. No significant differences in mortality rate are suggested. WBCT currently entails greater radiation dose and mechanical ventilation time. Further research is necessitated to address limitations of predominately retrospective observational data available.


Subject(s)
Tomography, X-Ray Computed/methods , Whole Body Imaging/methods , Wounds and Injuries/diagnostic imaging , Humans , Middle Aged , Retrospective Studies
18.
J Cardiovasc Med (Hagerstown) ; 22(11): 818-827, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1450783

ABSTRACT

AIMS: Currently, there are few available data regarding a possible role for subclinical atherosclerosis as a risk factor for mortality in Coronavirus Disease 19 (COVID-19) patients. We used coronary artery calcium (CAC) score derived from chest computed tomography (CT) scan to assess the in-hospital prognostic role of CAC in patients affected by COVID-19 pneumonia. METHODS: Electronic medical records of patients with confirmed diagnosis of COVID-19 were retrospectively reviewed. Patients with known coronary artery disease (CAD) were excluded. A CAC score was calculated for each patient and was used to categorize them into one of four groups: 0, 1-299, 300-999 and at least 1000. The primary endpoint was in-hospital mortality for any cause. RESULTS: The final population consisted of 282 patients. Fifty-seven patients (20%) died over a follow-up time of 40 days. The presence of CAC was detected in 144 patients (51%). Higher CAC score values were observed in nonsurvivors [median: 87, interquartile range (IQR): 0.0-836] compared with survivors (median: 0, IQR: 0.0-136). The mortality rate in patients with a CAC score of at least 1000 was significantly higher than in patients without coronary calcifications (50 vs. 11%) and CAC score 1-299 (50 vs. 23%), P < 0.05. After adjusting for clinical variables, the presence of any CAC categories was not an independent predictor of mortality; however, a trend for increased risk of mortality was observed in patients with CAC of at least 1000. CONCLUSION: The correlation between CAC score and COVID-19 is fascinating and under-explored. However, in multivariable analysis, the CAC score did not show an additional value over more robust clinical variables in predicting in-hospital mortality. Only patients with the highest atherosclerotic burden (CAC ≥1000) could represent a high-risk population, similarly to patients with known CAD.


Subject(s)
COVID-19 , Coronary Artery Disease , Coronary Vessels , Hospital Mortality , Vascular Calcification/diagnostic imaging , COVID-19/diagnosis , COVID-19/mortality , Coronary Artery Disease/diagnosis , Coronary Vessels/diagnostic imaging , Coronary Vessels/pathology , Female , Heart Disease Risk Factors , Hospitalization/statistics & numerical data , Humans , Italy/epidemiology , Male , Middle Aged , Predictive Value of Tests , Prognosis , Retrospective Studies , SARS-CoV-2/isolation & purification , Tomography, X-Ray Computed/methods , Vascular Calcification/epidemiology
19.
Crit Care ; 25(1): 214, 2021 06 21.
Article in English | MEDLINE | ID: covidwho-1440944

ABSTRACT

BACKGROUND: Critically ill COVID-19 patients have pathophysiological lung features characterized by perfusion abnormalities. However, to date no study has evaluated whether the changes in the distribution of pulmonary gas and blood volume are associated with the severity of gas-exchange impairment and the type of respiratory support (non-invasive versus invasive) in patients with severe COVID-19 pneumonia. METHODS: This was a single-center, retrospective cohort study conducted in a tertiary care hospital in Northern Italy during the first pandemic wave. Pulmonary gas and blood distribution was assessed using a technique for quantitative analysis of dual-energy computed tomography. Lung aeration loss (reflected by percentage of normally aerated lung tissue) and the extent of gas:blood volume mismatch (percentage of non-aerated, perfused lung tissue-shunt; aerated, non-perfused dead space; and non-aerated/non-perfused regions) were evaluated in critically ill COVID-19 patients with different clinical severity as reflected by the need for non-invasive or invasive respiratory support. RESULTS: Thirty-five patients admitted to the intensive care unit between February 29th and May 30th, 2020 were included. Patients requiring invasive versus non-invasive mechanical ventilation had both a lower percentage of normally aerated lung tissue (median [interquartile range] 33% [24-49%] vs. 63% [44-68%], p < 0.001); and a larger extent of gas:blood volume mismatch (43% [30-49%] vs. 25% [14-28%], p = 0.001), due to higher shunt (23% [15-32%] vs. 5% [2-16%], p = 0.001) and non-aerated/non perfused regions (5% [3-10%] vs. 1% [0-2%], p = 0.001). The PaO2/FiO2 ratio correlated positively with normally aerated tissue (ρ = 0.730, p < 0.001) and negatively with the extent of gas-blood volume mismatch (ρ = - 0.633, p < 0.001). CONCLUSIONS: In critically ill patients with severe COVID-19 pneumonia, the need for invasive mechanical ventilation and oxygenation impairment were associated with loss of aeration and the extent of gas:blood volume mismatch.


Subject(s)
Blood Volume/physiology , COVID-19/diagnostic imaging , COVID-19/metabolism , Lung/diagnostic imaging , Lung/metabolism , Pulmonary Gas Exchange/physiology , Aged , Blood Gas Analysis/methods , COVID-19/epidemiology , Cohort Studies , Critical Illness/epidemiology , Female , Humans , Italy/epidemiology , Male , Middle Aged , Respiration, Artificial/methods , Retrospective Studies , Tomography, X-Ray Computed/methods
20.
Medicine (Baltimore) ; 100(38): e22571, 2021 Sep 24.
Article in English | MEDLINE | ID: covidwho-1437852

ABSTRACT

BACKGROUND: There are few reports on the chest computed tomography (CT) imaging features of children with coronavirus disease 2019 (COVID-19), and most reports involve small sample sizes. OBJECTIVES: To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice. DATA SOURCES: We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu. METHODS: Reports on chest CT imaging features of children with COVID-19 from January 1, 2020 to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software. RESULTS: Thirty-seven articles (1747 children) were included in this study. The heterogeneity of meta-analysis results ranged from 0% to 90.5%. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8%-70.6%), with a rate of 61.0% (95% CI: 50.8%-71.2%) in China and 67.8% (95% CI: 57.1%-78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7%-48.3%), multiple lung lobe lesions was 65.1% (95% CI: 55.1%-67.9%), and bilateral lung lesions was 61.5% (95% CI: 58.8%-72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported 3 cases of white lung, another reported 2 cases of pneumothorax, and another 1 case of bullae. CONCLUSIONS: The lung CT results of children with COVID-19 are usually normal or slightly atypical. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Thorax/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Adolescent , Blister/diagnostic imaging , Blister/epidemiology , Blister/virology , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/virology , Child , Child, Preschool , Data Management , Female , Humans , Incidence , Infant , Lung/pathology , Lung/virology , Male , Pleural Effusion/diagnostic imaging , Pleural Effusion/epidemiology , Pleural Effusion/virology , Pneumothorax/diagnostic imaging , Pneumothorax/epidemiology , Retrospective Studies , SARS-CoV-2/genetics , Solitary Pulmonary Nodule/diagnostic imaging , Solitary Pulmonary Nodule/epidemiology , Solitary Pulmonary Nodule/virology , Tomography, X-Ray Computed/methods , Tomography, X-Ray Computed/trends
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