Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 1.211
Filter
Add filters

Year range
1.
J Coll Physicians Surg Pak ; 31(1): 14-20, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1068267

ABSTRACT

OBJECTIVE:   To identify utility of chest computed tomography severity score (CT-SS) as an additional tool to COVID-19 pneumonia imaging classification in assessing severity of COVID-19. STUDY DESIGN: Descriptive analytical study Place and Duration of Study: Armed Forces Institute of Radiology and Imaging, (AFIRI) Rawalpindi, from April 2020 to June 2020. METHODOLOGY: Five hundred suspected COVID-19 cases referred for high resolution computed tomography - chest were included in the study. Cases were categorised by radiological findings using COVID-19 pneumonia imaging classification, proposed in the radiological society of North America expert consensus statement on reporting chest CT findings related to COVID-19. CT-SS was calculated for all scans. Patients were clinically classified according to disease severity as per 'Diagnosis And Treatment Program of Pneumonia of New Coronavirus Infection' recommended by China's National Health Commission. The relationships between radiological findings, CT-SS, and clinical severity were explored. RESULTS: Based on the radiological findings, 298 cases were graded as typical, 34 as indeterminate, 15 as atypical, and 153 as negative for pneumonia. The apical and posterior basal segments of lower lobes were most commonly involved. The CT-SS showed higher values in patients of severe group as compared to those in moderate group (p < 0.05). CT-SS threshold for recognising severe COVID-19 was 18.5 (area under curve, 0.960), with 84.3% sensitivity and 92.5% specificity. CONCLUSION: In coherence with COVID-19 pneumonia imaging classification, CT-SS may provide a comprehensive and objective assessment of COVID-19 severity. Key Words: COVID-19, COVID-19 pneumonia, CT-SS, High resolution computed tomography.


Subject(s)
Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Thorax/diagnostic imaging , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Diagnostic Tests, Routine , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Pakistan , Radiography, Thoracic/methods , Tertiary Healthcare , Young Adult
2.
Epidemiol Prev ; 44(5-6 Suppl 2): 216-225, 2020.
Article in English | MEDLINE | ID: covidwho-1068142

ABSTRACT

OBJECTIVES: to explore clinical and epidemiological characteristics associated with an imaging feature of COVID-19 pneumonia at disease onset, in order to identify factors that may be evaluable by general practitioners at patient's home, and which may lead to identify a more severe disease, needing hospitalization. DESIGN: this is a retrospective/prospective observational hospital cohort. SETTING AND PARTICIPANTS: the study population includes all patients consecutively admitted to the emergency department of Città della salute e della scienza University Hospital from 01.03 to 31.05.2020 with a confirmed diagnosis of SARS-CoV-2 infection. MAIN OUTCOME MEASURES: patients were classified in two groups according to the findings of X-ray imaging, lung ultrasound and chest computer tomography, as pneumonia or not pneumonia patients. RESULTS: in multivariable analysis, factors most strongly associated with emergency department admission with pneumonia were age, oxygen saturation <90% (adj OR 4.16 ;95%CI 1.44-12.07), respiratory rate >24 breaths/min (adj OR 6.50; 95%CI 2.36-17.87), fever ≥38° (adj OR 3.05; 95%CI 1.53-6.08) and the presence of gastroenteric symptoms (vomiting and diarrhea). A delay (> 7 days) between the appearance of the initial lung symptoms (cough and dyspnea) and the admission to the emergency department was also related to a higher probability of receiving a positive imaging report (OR 4.99; 95%CI 2,02-12,34). CONCLUSIONS: in order to reorganize the management of COVID-19 patients in Italy, in view of the risk of a second wave of epidemic or of local outbreaks, it would be desirable to relocate the triage, and possibly the patient's care, from hospital to home. In this scenario it is important to identify all symptoms and signs associated with COVID-19 pneumonia that would facilitate the decision-making process of GPs leading to patients hospitalization.


Subject(s)
/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , Comorbidity , Diarrhea/epidemiology , Diarrhea/etiology , Dyspnea/epidemiology , Dyspnea/etiology , Emergency Service, Hospital/statistics & numerical data , Female , Hospitals, University/statistics & numerical data , Humans , Italy/epidemiology , Leukocyte Count , Male , Middle Aged , Nervous System Diseases/epidemiology , Nervous System Diseases/etiology , Oxygen/blood , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/virology , Prospective Studies , Respiratory Rate , Retrospective Studies , Symptom Assessment , Time Factors , Vomiting/epidemiology , Vomiting/etiology
3.
PLoS One ; 16(1): e0245547, 2021.
Article in English | MEDLINE | ID: covidwho-1067419

ABSTRACT

Endemic human coronaviruses (HCoVs) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are members of the family Coronaviridae. Comparing the findings of the infections caused by these viruses would help reveal the novel characteristics of SARS-CoV-2 and provide insight into the unique pathogenesis of SARS-CoV-2 infection. This study aimed to compare the clinical and radiological characteristics of SARS-CoV-2 and endemic HCoVs infection in adult hospitalized patients with community-acquired pneumonia (CAP). This study was performed at a university-affiliated tertiary hospital in the Republic of Korea, between January 1, 2015, and July 31, 2020. A total of 109 consecutive patients who were over 18 years of age with confirmed SARS-CoV-2 and endemic HCoVs were enrolled. Finally, 19 patients with SARS-CoV-2 CAP were compared to 40 patients with endemic HCoV CAP. Flu-like symptoms such as cough, sore throat, headache, myalgia, and prolonged fever were more common in SARS-CoV-2 CAP, whereas clinical findings suggestive of bacterial pneumonia such as dyspnea, leukocytosis with left shift, and increased C-reactive protein were more common in endemic HCoV CAP. Bilateral peripherally distributed ground-glass opacities (GGOs) were typical radiologic findings in SARS-CoV-2 CAP, whereas mixed patterns of GGOs, consolidations, micronodules, and pleural effusion were observed in endemic HCoV CAP. Coinfection was not observed in patients with SARS-CoV-2 CAP, but was observed in more than half of the patients with endemic HCoV CAP. There were distinctive differences in the clinical and radiologic findings between SARS-CoV-2 and endemic HCoV CAP. Further investigations are required to elucidate the mechanism underlying this difference. Follow-up observations are needed to determine if the presentation of SARS-CoV-2 CAP changes with repeated infection.


Subject(s)
/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Aged , /pathology , Cohort Studies , Coinfection/diagnostic imaging , Coinfection/epidemiology , Coinfection/pathology , Coinfection/virology , Community-Acquired Infections , Coronavirus/isolation & purification , Endemic Diseases , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Middle East Respiratory Syndrome Coronavirus/isolation & purification , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Radiography, Thoracic/methods , Republic of Korea/epidemiology , Retrospective Studies , Risk Factors , Thorax/diagnostic imaging
4.
Ann Palliat Med ; 10(1): 560-571, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1063565

ABSTRACT

BACKGROUND: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias. METHODS: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared. RESULTS: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05). CONCLUSIONS: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.


Subject(s)
/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Cytomegalovirus Infections/diagnostic imaging , Female , Humans , Influenza A virus , Influenza B virus , Influenza, Human/diagnostic imaging , Lung/virology , Male , Middle Aged , Pneumonia, Viral/virology , Retrospective Studies
5.
J Transl Med ; 19(1): 29, 2021 01 07.
Article in English | MEDLINE | ID: covidwho-1059725

ABSTRACT

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.


Subject(s)
/methods , /diagnosis , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed/methods , Adult , Aged , /statistics & numerical data , China/epidemiology , Female , High-Throughput Screening Assays/methods , High-Throughput Screening Assays/statistics & numerical data , Humans , Machine Learning , Male , Middle Aged , Models, Statistical , Nomograms , Pandemics , Pneumonia, Viral/epidemiology , Radiographic Image Interpretation, Computer-Assisted/methods , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/statistics & numerical data , Translational Medical Research
6.
Zhonghua Er Ke Za Zhi ; 58(4): 275-278, 2020 Apr 02.
Article in Chinese | MEDLINE | ID: covidwho-1024679

ABSTRACT

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV infection. They were admitted to the Third People's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, 5 were males and 10 females, aged from 4 to 14 years. Five of the 15 children were febrile and 10 were asymptomatic on the first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammatory lesions. Seven cases had small nodular ground glass opacities and 2 cases had speckled ground glass opacities. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. The remaining 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Child , Child, Preschool , China , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Female , Humans , Lung/pathology , Male , Pandemics , RNA, Viral/isolation & purification , Radiography, Thoracic , Retrospective Studies
7.
AJNR Am J Neuroradiol ; 41(9): 1703-1706, 2020 09.
Article in English | MEDLINE | ID: covidwho-1024494

ABSTRACT

Patients with coronavirus disease 2019 (COVID-19) may have symptoms of anosmia or partial loss of the sense of smell, often accompanied by changes in taste. We report 5 cases (3 with anosmia) of adult patients with COVID-19 in whom injury to the olfactory bulbs was interpreted as microbleeding or abnormal enhancement on MR imaging. The patients had persistent headache (n = 4) or motor deficits (n = 1). This olfactory bulb injury may be the mechanism by which the Severe Acute Respiratory Syndrome coronavirus 2 causes olfactory dysfunction.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Olfaction Disorders/etiology , Olfactory Bulb/diagnostic imaging , Pneumonia, Viral/complications , Coronavirus Infections/diagnostic imaging , Humans , Magnetic Resonance Imaging , Olfaction Disorders/diagnostic imaging , Olfactory Bulb/injuries , Pandemics , Pneumonia, Viral/diagnostic imaging , Smell , Taste
8.
Emerg Radiol ; 27(6): 755-759, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1018337

ABSTRACT

Neurological manifestations and complications are increasingly reported in coronavirus disease-19 (COVID-19) patients. Although pulmonary manifestations are more common, patients with severe disease may present with neurological symptoms such as in our case. We describe a case report of a 50-year-old male without previous known comorbidity who was found unresponsive due to COVID-19-related neurological complications. During this pandemic, an emergency radiologist should be well acquainted with various neurological manifestations of COVID-19. In this article, we will discuss the pathogenesis, imaging findings, and differentials of this disease.


Subject(s)
Brain Diseases/diagnostic imaging , Brain Diseases/virology , Coronavirus Infections/complications , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Betacoronavirus , Humans , Magnetic Resonance Imaging , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Tomography, X-Ray Computed
9.
BMC Pulm Med ; 20(1): 129, 2020 May 07.
Article in English | MEDLINE | ID: covidwho-1017172

ABSTRACT

BACKGROUND: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis. METHODS: Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19). RESULTS: Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score >  2. CONCLUSIONS: With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , Tomography, X-Ray Computed
11.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-1011363

ABSTRACT

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Subject(s)
/diagnosis , Decision Support Systems, Clinical , Health , Machine Learning , Pneumonia, Viral/diagnosis , /diagnostic imaging , Diagnosis, Differential , Humans , Middle Aged , Pneumonia, Viral/diagnostic imaging , Support Vector Machine , Tomography, X-Ray Computed
12.
Br J Radiol ; 93(1116): 20200522, 2020 Dec 01.
Article in English | MEDLINE | ID: covidwho-1004389

ABSTRACT

As the COVID-19 pandemic has spread across the globe, questions have arisen about the approach healthcare systems should adopt in order to optimally manage patient influx. With a focus on the impact of COVID-19 on the NHS, we describe the frontline experience of a severely affected hospital in close proximity to London. We highlight a protocol-driven approach, incorporating the use of CT in the rapid triage, assessment and cohorting of patients, in an environment where there was a lack of readily available, onsite RT-PCR testing facilities. Furthermore, the effects of the protocol on the effective streamlining of patient flow within the hospital are discussed, as are the resultant improvements in clinical management decisions within the acute care service. This model may help other healthcare systems in managing this pandemic whilst assessing their own needs and resources.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Triage/methods , Betacoronavirus , Humans , Pandemics , United Kingdom
13.
Br J Radiol ; 94(1119): 20200755, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-999783

ABSTRACT

COVID-19 can cause damage to the lung, which can result in progressive respiratory failure and potential death. Chest radiography and CT are the imaging tools used to diagnose and monitor patients with COVID-19. Lung ultrasound (LUS) during COVID-19 is being used in some areas to aid decision-making and improve patient care. However, its increased use could help improve existing practice for patients with suspected COVID-19, or other lung disease. A limitation of LUS is that it requires practitioners with sufficient competence to ensure timely, safe, and diagnostic clinical/imaging assessments. This commentary discusses the role and governance of LUS during and beyond the COVID-19 pandemic, and how increased education and training in this discipline can be undertaken given the restrictions in imaging highly infectious patients. The use of simulation, although numerical methods or dedicated scan trainers, and machine learning algorithms could further improve the accuracy of LUS, whilst helping to reduce its learning curve for greater uptake in clinical practice.


Subject(s)
/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiology/education , Ultrasonography/methods , Clinical Competence , Humans , Machine Learning , Pandemics , Pneumonia, Viral/virology , Point-of-Care Systems
15.
R I Med J (2013) ; 103(10): 32-33, 2020 Dec 21.
Article in English | MEDLINE | ID: covidwho-995580

ABSTRACT

Co-occurrence of pneumothorax and pneumomediastinum is rare in COVID-19 patients. Positive airway pressure therapy used to improve oxygenation may sometimes worsen clinical outcomes in some patients with severe COVID-19 pneumonia. In this case report, we describe an individual who was diagnosed with COVID-19 and developed bilateral pneumothorax and pneumomediastinum after initiating non-invasive positive airway pressure therapy.


Subject(s)
/therapy , Mediastinal Emphysema/etiology , Pneumonia, Viral/therapy , Pneumothorax/etiology , Positive-Pressure Respiration/adverse effects , Aged , Fatal Outcome , Humans , Iatrogenic Disease , Male , Mediastinal Emphysema/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Pneumothorax/diagnostic imaging , Radiography, Thoracic
16.
Jpn J Radiol ; 38(11): 1007-1011, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-986668

ABSTRACT

OBJECTIVE: The aim of this case series is to describe our experience in diagnosis and management of oncological asymptomatic patients with COVID-19 who underwent 18F-FDG PET/CT. METHODS: From March 9 to March 31, 2020, we identified 5 patients who had PET/CT findings suspicious for COVID-19, but no symptom of infection. RESULTS: The first three patients were administered an SARS-CoV-2 test in a COVID-dedicated center, while the fourth and fifth were tested in our institution, in accordance with a new internal procedure. The SARS-CoV-2 test yielded positive results in all five patients. CONCLUSION: In this COVID-19 emergency, our task as radiologists and nuclear medicine physicians is to be able to identify imaging findings suggestive of the disease and to manage patients without overloading the hospital system.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Coronavirus Infections/diagnostic imaging , Fluorodeoxyglucose F18 , Neoplasms/complications , Neoplasms/diagnostic imaging , Pneumonia, Viral/complications , Pneumonia, Viral/diagnostic imaging , Positron Emission Tomography Computed Tomography/methods , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Radiopharmaceuticals
17.
Crit Care ; 24(1): 700, 2020 12 22.
Article in English | MEDLINE | ID: covidwho-992530

ABSTRACT

BACKGROUND: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. METHOD: The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. RESULTS: The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. CONCLUSIONS: The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.


Subject(s)
/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Ultrasonography/methods , Adult , Aged , Female , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Prognosis , Prospective Studies , /virology , Time-to-Treatment , Tomography, X-Ray Computed
18.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2197-2206, 2020 11.
Article in English | MEDLINE | ID: covidwho-978670

ABSTRACT

Up to April 4, 2020, the novel coronavirus disease-2019 COVID-19 has affected more than 1 099000 patients and has become a major global health concern. World Health Organization (WHO) has defined COVID-19 as a global pandemic. Critical care ultrasound (CCUS) can rapidly acquire the image of lung and other organs and demonstrate the pathophysiological changes to guide precise therapy in COVID-19 pneumonia without radiation or interfering with personal protective equipment. In addition, the application of CCUS can cover the whole courses from the fever clinic to the intensive care unit to improve the treatment. We would like to present the CCUS features about COVID-19 pneumonia and share the application experience of CCUS in Wuhan, China, and hope it works for physicians worldwide to solve the problem and improve the outcome.


Subject(s)
Coronavirus Infections/diagnostic imaging , Critical Care/methods , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Betacoronavirus , China , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Patient Positioning
19.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2230-2240, 2020 11.
Article in English | MEDLINE | ID: covidwho-978669

ABSTRACT

Since the emergence of the COVID-19 pandemic in December of 2019, clinicians and scientists all over the world have faced overwhelming new challenges that not only threaten their own communities and countries but also the world at large. These challenges have been enormous and debilitating, as the infrastructure of many countries, including developing ones, had little or no resources to deal with the crisis. Even in developed countries, such as Italy, health systems have been so inundated by cases that health care facilities became oversaturated and could not accommodate the unexpected influx of patients to be tested. Initially, resources were focused on testing to identify those who were infected. When it became clear that the virus mainly attacks the lungs by causing parenchymal changes in the form of multifocal pneumonia of different levels of severity, imaging became paramount in the assessment of disease severity, progression, and even response to treatment. As a result, there was a need to establish protocols for imaging of the lungs in these patients. In North America, the focus was on chest X-ray and computed tomography (CT) as these are widely available and accessible at most health facilities. However, in Europe and China, this was not the case, and a cost-effective and relatively fast imaging modality was needed to scan a large number of sick patients promptly. Hence, ultrasound (US) found its way into the hands of Chinese and European physicians and has since become an important imaging modality in those locations. US is a highly versatile, portable, and inexpensive imaging modality that has application across a broad spectrum of conditions and, in this way, is ideally suited to assess the lungs of COVID-19 patients in the intensive care unit (ICU). This bedside test can be done with little to no movement of the patients from the unit that keeps them in their isolated rooms, thereby limiting further exposure to other health personnel. This article presents a basic introduction to COVID-19 and the use of the US for lung imaging. It further provides a high-level overview of the existing US technologies that are driving development in current and potential future US imaging systems for lung, with a specific emphasis on portable and 3-D systems.


Subject(s)
Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Betacoronavirus , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Coronavirus Infections/physiopathology , Humans , Imaging, Three-Dimensional , Lung/diagnostic imaging , Lung/pathology , Lung/physiopathology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Pneumonia, Viral/physiopathology
20.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2207-2217, 2020 11.
Article in English | MEDLINE | ID: covidwho-978667

ABSTRACT

Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.


Subject(s)
Coronavirus Infections/diagnostic imaging , Image Interpretation, Computer-Assisted/methods , Lung/diagnostic imaging , Pleura/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Algorithms , Humans , Pandemics , Signal Processing, Computer-Assisted , Support Vector Machine
SELECTION OF CITATIONS
SEARCH DETAIL