Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 236
Filter
Add filters

Year range
1.
J Coll Physicians Surg Pak ; 30(8): 785-789, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-745630

ABSTRACT

OBJECTIVE: To investigate airway abnormalities on chest CT in adult patients with COVID-19 pneumonia. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China, from January to April, 2020. METHODOLOGY: CT scan images were analysed retrospectively. The main CT findings, including pulmonary opacities, airway wall visibility, wall thickening, luminal changes, and the formation of mucus plugs were evaluated. Airway segments were classified into three types based on the spatial relationship between conducting airways and pulmonary opacities. RESULTS: A total of 275 lesions were detected in 52 patients. Of these, 170 (61.82%) lesions were associated with 243 airway segments, including segments enclosed within lesions (type I, 152, 62.55%), crossing the lesions (type II, 51, 20.99%), and abutting the lesions (type III, 40, 16.46%). The bronchial walls of 154 (63.37%) segments were ill-defined; whereas, the walls of 89 (36.63%) segments were well-defined; in the latter group, 62 (69.66%) showed mild thickening. The bronchial lumen of 183 (75.31%) segments presented mild bronchiectasis and 60 (24.69%) segments appeared normal. Mucus plug was detected in one segment (0.41%). There were no cases of bronchial stenosis, and all bronchial segments located in normal lung regions appeared normal. The appearance of 196 (80.66%) affected bronchi was completely restored before hospital discharge. CONCLUSION: Typical airway changes in adult COVID-19 pneumonia include bronchial wall thickening without significant stenosis of the airway lumen and the absence of bronchial mucus plugs. Moreover, bronchi located in unaffected lung regions have a normal appearance. These characteristics have potential value in differential diagnosis. Key Words: Coronavirus disease, Airway, Computed tomography, Chest.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Betacoronavirus , Clinical Laboratory Techniques , Coronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Radiography, Thoracic , Retrospective Studies , Thorax
2.
Korean J Radiol ; 21(10): 1150-1160, 2020 10.
Article in English | MEDLINE | ID: covidwho-742717

ABSTRACT

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Deep Learning , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Adult , Aged , Female , Humans , Male , Middle Aged , Pandemics , Radiography, Thoracic/methods , Retrospective Studies , Tomography, X-Ray Computed/methods
4.
PLoS One ; 15(8): e0237302, 2020.
Article in English | MEDLINE | ID: covidwho-729562

ABSTRACT

BACKGROUND: As the current outbreak of COVID-2019 disease has spread to the other more than 150 countries besides China around the world and the death number constantly increased, the clinical data and radiological findings of death cases need to be explored so that more physicians, radiologists and researchers can gain important information to save more lives. METHODS: 73 patients who died from COVID-19 were retrospectively included. The clinical and laboratory data of the patients were extracted from electronic medical records. The clinical data, inflammation-related laboratory results, and CT imaging features were summarized. The laboratory results and dynamic changes of imaging features and severity scores of lung involvement based on chest CT were analyzed. RESULTS: The mean age was 67±12 years. The typical clinical symptoms included fever (88%), cough (62%) and dyspnea (23%). 65% patients had at least one underlying disease. GGO with consolidation was the most common feature for the five lung lobes (47%-53% among the various lobes), with total severity score of 12.97±5.87 for the both lungs. The proportion of GGO with consolidation is markedly increased on follow-up chest CT compared with initial CT scans, as well as the averaging total CT scores (14.53±5.76 vs. 6.60±5.65; P<0.001). The severity score was rated as severe (white lung) in 13% patients on initial CT scans, and in 60% on follow-up CT scans. Moderate positive correlations were found between CT scores and leucocytes, neutrophils and IL-2R (r = 0.447-0581, P<0.001). CONCLUSION: Chest CT findings and laboratory test results were worsening in patients who died of COVID-19, with moderate positive correlations between CT severity scores and inflammation-related factors of leucocytes, neutrophils, and IL-2R. Chest CT imaging may play an more important role in monitoring disease progression and predicting prognosis.


Subject(s)
Coronavirus Infections/diagnostic imaging , Coronavirus Infections/mortality , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/mortality , Tomography, X-Ray Computed , Adult , Aged , Aged, 80 and over , Betacoronavirus , China , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Pandemics , Radiography, Thoracic , Retrospective Studies
5.
Radiology ; 296(3): E156-E165, 2020 09.
Article in English | MEDLINE | ID: covidwho-729427

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiologists , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus , Child , Child, Preschool , China , Diagnosis, Differential , Female , Humans , Infant , Infant, Newborn , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Philadelphia , Pneumonia/diagnostic imaging , Radiography, Thoracic , Radiologists/standards , Radiologists/statistics & numerical data , Retrospective Studies , Rhode Island , Sensitivity and Specificity , Young Adult
6.
Radiology ; 296(3): E166-E172, 2020 09.
Article in English | MEDLINE | ID: covidwho-722986

ABSTRACT

Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic/methods , Aged , Aged, 80 and over , Betacoronavirus , Databases, Factual , Female , Humans , Male , Middle Aged , Pandemics , ROC Curve , Tomography, X-Ray Computed
7.
J Infect Dev Ctries ; 14(7): 750-757, 2020 Jul 31.
Article in English | MEDLINE | ID: covidwho-721544

ABSTRACT

INTRODUCTION: The numbers of people infected with SARS-CoV-2 in Indonesia especially in Jakarta as the epicenter continue to rise. Limited published clinical data, scarcity and long turn over time of diagnostic testing put clinician in dilemma to make diagnosis. METHODOLOGY: This is an observational case series study from confirmed COVID-19 patient in our hospital from first case admission on 17 March 30 April, 2020. We collected patient's demography, symptoms, comorbidities, therapy, laboratory, chest x-ray and ECG consecutively. RESULTS: Between 17 March 2020 and 30 April 2020, there were 30 confirmed COVID-19 cases, 16 (53.3%) were male. Clinical symptoms were dyspnea in 22 (73.3%) and dry cough 16 (53.3%). Comorbidities were diabetes in 14 (46.6%), hypertension 10 (33.3%) and Coronary Artery Disease (CAD) in 10 (33.3%) patients respectively. Laboratory findings showed lymphopenia in 21 (70%) patients, increased inflammation marker in Erythrocyte Sedimentation Rate (ESR), C-Reactive Protein (CRP) and Lactate Dehydrogenase (LDH) 21 (70%), 23 (76.6%) and 12 (40%) patients respectively. Twenty-seven (90%) cases had abnormal Chest X-Ray (CXR) and mostly severe 18 (60%). Descriptive finding for images included consolidation 16 (53.3%) and Ground Glass Opacities (GGO) in 10 (33.3%) patients. CONCLUSIONS: Based on our findings, most cases of COVID-19 admitted in secondary referral hospital were already in moderate to severe stages. This is most likely due to late referral from primary care and unspecific clinical features resemblance of other infectious diseases. Inflammation marker and CXR are cost effective findings and can be used as marker to determine further referral.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/etiology , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/etiology , Adult , Aged , Clinical Laboratory Techniques , Comorbidity , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Diabetes Mellitus/epidemiology , Electrocardiography , Female , Humans , Hypertension/epidemiology , Indonesia/epidemiology , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Radiography, Thoracic , Secondary Care Centers/statistics & numerical data , X-Rays
8.
Br J Radiol ; 93(1113): 20200643, 2020 Sep 01.
Article in English | MEDLINE | ID: covidwho-721360

ABSTRACT

OBJECTIVE: To investigate the diagnostic performance of chest CT in screening patients suspected of Coronavirus disease 2019 (COVID-19) in a Western population. METHODS: Consecutive patients who underwent chest CT because of clinical suspicion of COVID-19 were included. CT scans were prospectively evaluated by frontline general radiologists who were on duty at the time when the CT scan was performed and retrospectively assessed by a chest radiologist in an independent and blinded manner. Real-time reverse transcriptase-polymerase chain reaction was used as reference standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Sensitivity and specificity of the frontline general radiologists were compared to those of the chest radiologist using the McNemar test. RESULTS: 56 patients were included. Sensitivity, specificity, PPV, and NPV for the frontline general radiologists were 89.3% [95% confidence interval (CI): 71.8%, 97.7%], 32.1% (95% CI: 15.9%, 52.4%), 56.8% (95% CI: 41.0%, 71.7%), and 75.0% (95% CI: 42.8%, 94.5%), respectively. Sensitivity, specificity, PPV, and NPV for the chest radiologist were 89.3% (95% CI: 71.8%, 97.7%), 75.0% (95% CI: 55.1%, 89.3%), 78.1% (95% CI: 60.0%, 90.7%), and 87.5% (95% CI: 67.6%, 97.3%), respectively. Sensitivity was not significantly different (p = 1.000), but specificity was significantly higher for the chest radiologist (p = 0.001). CONCLUSION: Chest CT interpreted by frontline general radiologists achieves insufficient screening performance. Although specificity of a chest radiologist appears to be significantly higher, sensitivity did not improve. A negative chest CT result does not exclude COVID-19. ADVANCES IN KNOWLEDGE: Our study shows that chest CT interpreted by frontline general radiologists achieves insufficient diagnostic performance to use it as an independent screening tool for COVID-19. Although specificity of a chest radiologist appears to be significantly higher, sensitivity is still insufficiently high.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , Aged, 80 and over , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Prospective Studies , Radiography, Thoracic/methods , Reproducibility of Results , Retrospective Studies , Sensitivity and Specificity
10.
BMJ Case Rep ; 13(8)2020 Aug 11.
Article in English | MEDLINE | ID: covidwho-713629

ABSTRACT

The COVID-19 pandemic has had a significant impact on the structure and operation of healthcare services worldwide. We highlight a case of a 64-year-old man who presented to the emergency department with acute dyspnoea on a background of a 2-week history of fever, dry cough and shortness of breath. On initial assessment the patient was hypoxic (arterial oxygen saturation (SaO2) of 86% on room air), requiring 10 L/min of oxygen to maintain 98% SaO2 Examination demonstrated left-sided tracheal deviation and absent breath sounds in the right lung field on auscultation. A chest radiograph revealed a large right-sided tension pneumothorax which was treated with needle thoracocentesis and a definitive chest drain. A CT pulmonary angiogram demonstrated segmental left lower lobe acute pulmonary emboli, significant generalised COVID-19 parenchymal features, surgical emphysema and an iatrogenic pneumatocoele. This case emphasises the importance of considering coexisting alternative diagnoses in patients who present with suspected COVID-19.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/complications , Pneumonia, Viral/complications , Pneumothorax/complications , Pulmonary Embolism/complications , Anti-Bacterial Agents/therapeutic use , Anticoagulants/therapeutic use , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Drainage , Humans , Lung/diagnostic imaging , Male , Middle Aged , Oxygen Inhalation Therapy , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Pneumothorax/diagnostic imaging , Pneumothorax/therapy , Pulmonary Embolism/diagnostic imaging , Pulmonary Embolism/drug therapy , Radiography, Thoracic , Tinzaparin/therapeutic use , Tomography, X-Ray Computed
11.
BMJ Case Rep ; 13(8)2020 Aug 11.
Article in English | MEDLINE | ID: covidwho-713318

ABSTRACT

A 33-year-old pregnant woman was hospitalised with fever, cough, myalgia and dyspnoea at 23.5 weeks of gestation (WG). Development of acute respiratory distress syndrome (ARDS) mandated invasive mechanical ventilation. A nasopharyngeal swab proved positive for severe acute respiratory syndrome coronavirus 2 by reverse transcription-PCR. The patient developed hypertension and biological disorders suggesting pre-eclampsia and HELLP (haemolysis, elevated liver enzyme levels and low platelet levels) syndrome. Pre-eclampsia was subsequently ruled out by a low ratio of serum soluble fms-like tyrosine kinase-1 to placental growth factor. Given the severity of ARDS, delivery by caesarean section was contemplated. Because the ratio was normal and the patient's respiratory condition stabilised, delivery was postponed. She recovered after 10 days of mechanical ventilation. She spontaneously delivered a healthy boy at 33.4 WG. Clinical and laboratory manifestations of COVID-19 infection can mimic HELLP syndrome. Fetal extraction should not be systematic in the absence of fetal distress or intractable maternal disease. Successful evolution was the result of a multidisciplinary teamwork.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/complications , Live Birth , Pneumonia, Viral/complications , Pregnancy Complications, Infectious/etiology , Respiratory Distress Syndrome, Adult/etiology , Adult , Coronavirus Infections/diagnosis , Female , Humans , Pandemics , Pneumonia, Viral/diagnosis , Pregnancy , Pregnancy Complications, Infectious/diagnostic imaging , Pregnancy Complications, Infectious/therapy , Radiography, Thoracic , Respiration, Artificial , Respiratory Distress Syndrome, Adult/diagnostic imaging , Respiratory Distress Syndrome, Adult/therapy
12.
Sci Rep ; 10(1): 13590, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-713031

ABSTRACT

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Deep Learning , Diagnosis, Computer-Assisted/methods , Neural Networks, Computer , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic/classification , Thorax/diagnostic imaging , Coronavirus Infections/virology , Humans , Pandemics , Pneumonia, Viral/virology , ROC Curve , Sensitivity and Specificity
14.
Medicine (Baltimore) ; 99(32): e21570, 2020 Aug 07.
Article in English | MEDLINE | ID: covidwho-706114

ABSTRACT

RATIONALE: Macrophage activation syndrome (MAS) is a rare life-threatening condition characterized by cytokine-mediated tissue injury and multiorgan dysfunction. PATIENT CONCERNS: We describe the unique case of young man who developed MAS as the sole manifestation of an otherwise paucisymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DIAGNOSES: Clinical and biological criteria led to the diagnosis of MAS; cytokine profile was highly suggestive reverse transcription polymerase chain reaction for SARS-CoV-2 in nasopharyngeal swabs was negative, but serum anti-SARS-CoV-2 immunoglobulin A and immunoglobulin G resulted positive leading to the diagnosis of SARS-CoV-2 infection. INTERVENTIONS: The patient was treated with empiric antibiotic and hydroxychloroquine. OUTCOMES: Clinical improvement ensued. At follow-up, the patient is well. LESSON: SARS-CoV-2 infection may trigger develop life-threatening complications, like MAS. This can be independent from coronavirus disease 2019 gravity.


Subject(s)
Ceftriaxone/administration & dosage , Coronavirus Infections/diagnosis , Hospitalization , Hydroxychloroquine/administration & dosage , Macrophage Activation Syndrome/diagnosis , Pneumonia, Viral/diagnosis , Adolescent , Blood Chemical Analysis , China , Clinical Laboratory Techniques/methods , Coronavirus Infections/drug therapy , DNA, Viral/analysis , Diagnosis, Differential , Disease Progression , Drug Therapy, Combination , Electrocardiography/methods , Follow-Up Studies , Humans , Macrophage Activation Syndrome/therapy , Male , Pandemics , Patient Discharge , Pneumonia, Viral/drug therapy , Radiography, Thoracic/methods , Real-Time Polymerase Chain Reaction/methods , Risk Assessment , Severity of Illness Index , Tomography, X-Ray Computed/methods , Treatment Outcome
15.
Chest ; 158(1): 406-415, 2020 07.
Article in English | MEDLINE | ID: covidwho-700492

ABSTRACT

BACKGROUND: The risks from potential exposure to coronavirus disease 2019 (COVID-19), and resource reallocation that has occurred to combat the pandemic, have altered the balance of benefits and harms that informed current (pre-COVID-19) guideline recommendations for lung cancer screening and lung nodule evaluation. Consensus statements were developed to guide clinicians managing lung cancer screening programs and patients with lung nodules during the COVID-19 pandemic. METHODS: An expert panel of 24 members, including pulmonologists (n = 17), thoracic radiologists (n = 5), and thoracic surgeons (n = 2), was formed. The panel was provided with an overview of current evidence, summarized by recent guidelines related to lung cancer screening and lung nodule evaluation. The panel was convened by video teleconference to discuss and then vote on statements related to 12 common clinical scenarios. A predefined threshold of 70% of panel members voting agree or strongly agree was used to determine if there was a consensus for each statement. Items that may influence decisions were listed as notes to be considered for each scenario. RESULTS: Twelve statements related to baseline and annual lung cancer screening (n = 2), surveillance of a previously detected lung nodule (n = 5), evaluation of intermediate and high-risk lung nodules (n = 4), and management of clinical stage I non-small cell lung cancer (n = 1) were developed and modified. All 12 statements were confirmed as consensus statements according to the voting results. The consensus statements provide guidance about situations in which it was believed to be appropriate to delay screening, defer surveillance imaging of lung nodules, and minimize nonurgent interventions during the evaluation of lung nodules and stage I non-small cell lung cancer. CONCLUSIONS: There was consensus that during the COVID-19 pandemic, it is appropriate to defer enrollment in lung cancer screening and modify the evaluation of lung nodules due to the added risks from potential exposure and the need for resource reallocation. There are multiple local, regional, and patient-related factors that should be considered when applying these statements to individual patient care.


Subject(s)
Carcinoma, Non-Small-Cell Lung/diagnosis , Coronavirus Infections , Lung Neoplasms , Multiple Pulmonary Nodules/diagnosis , Pandemics , Pneumonia, Viral , Radiography, Thoracic/methods , Betacoronavirus/isolation & purification , Consensus , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Early Detection of Cancer/methods , Early Detection of Cancer/standards , Humans , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/pathology , Neoplasm Staging , Pandemics/prevention & control , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Resource Allocation , Risk Assessment/methods
16.
Eur J Cardiothorac Surg ; 58(3): 646-647, 2020 09 01.
Article in English | MEDLINE | ID: covidwho-694782

ABSTRACT

Pneumomediastinum is a rare clinical finding, but one which can be the source of significant concern for clinicians. By presenting 3 such cases, we highlight that pneumomediastinum can complicate the course of a severe coronavirus disease 2019 infection but emphasize that conservative management is the first-line method of treatment, with gradual resorption of the air from the tissues. It is important to be alert to the development of pneumothorax, which will require drainage.


Subject(s)
Conservative Treatment , Coronavirus Infections/complications , Disease Progression , Mediastinal Emphysema/etiology , Mediastinal Emphysema/therapy , Pneumonia, Viral/complications , Aged , Anti-Bacterial Agents/therapeutic use , Blood Gas Analysis , Clinical Laboratory Techniques/methods , Continuous Positive Airway Pressure/methods , Coronavirus Infections/diagnosis , Follow-Up Studies , Humans , Male , Mediastinal Emphysema/diagnostic imaging , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Radiography, Thoracic/methods , Rare Diseases , Risk Assessment , Sampling Studies , Time Factors , Tomography, X-Ray Computed/methods , Treatment Outcome
17.
Eur Rev Med Pharmacol Sci ; 24(14): 7801-7803, 2020 07.
Article in English | MEDLINE | ID: covidwho-693476

ABSTRACT

SARS-CoV-2 infection in children is uncommon compared to adult population. However, some children required hospital and/or PICU admission. The aim of this short communication is to share our experience with Point-of-Care Ultrasound (POCUS) when managing these patients. Remarkably, all cases presented pleural and pericardial effusions, detected by POCUS, despite showing an adequate urinary output and prior to receiving any kind of fluid resuscitation. Effusions have been described as rare among SARS-CoV-2 infection in adult population. By performing portable chest X-Ray they would have gone unnoticed in our patients. Other POCUS findings consisted of all types of consolidations and coalescent B-line patterns. POCUS was also performed in order to optimize PEEP, checking adequate endotracheal intubation positioning (avoiding the risk of contagiousness related to auscultation in this framework), and to assess volemia status, cardiac performance, and brain neuro-monitoring. There was not cross-infection. In pediatric SARS-CoV-19 effusions are frequent but easily unnoticed unless lung and echo POCUS are performed.


Subject(s)
Coronavirus Infections/diagnostic imaging , Pericardial Effusion/diagnostic imaging , Pleural Effusion/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Ultrasonography , Betacoronavirus , Child , Humans , Pandemics , Pericardial Effusion/virology , Pleural Effusion/virology , Radiography, Thoracic
20.
Cleve Clin J Med ; 87(8): 469-476, 2020 07 31.
Article in English | MEDLINE | ID: covidwho-691294

ABSTRACT

The typical findings of COVID-19 on chest radiography and computed tomography (CT) include bilateral, multifocal parenchymal opacities (ground-glass opacities with or without consolidation, and "crazy paving"). In most cases, the opacities are predominantly in the peripheral and lower lung zones, and several have rounded morphology. However, these imaging findings are not pathognomonic for COVID-19 pneumonia and can be seen in other viral and bacterial infections, as well as with noninfectious causes such as drug toxicity and connective tissue disease. Most radiology professional organizations and societies recommend against routine screening CT to diagnose or exclude COVID-19.


Subject(s)
Coronavirus Infections , Lung/diagnostic imaging , Pandemics , Pneumonia, Viral , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Coronavirus Infections/physiopathology , Diagnosis, Differential , Humans , Pneumonia, Viral/diagnosis , Pneumonia, Viral/etiology , Pneumonia, Viral/physiopathology
SELECTION OF CITATIONS
SEARCH DETAIL