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1.
Medicine (Baltimore) ; 100(7): e24668, 2021 Feb 19.
Artículo en Inglés | MEDLINE | ID: covidwho-1091183

RESUMEN

ABSTRACT: We aimed to retrospectively analyze the clinical and computed tomography (CT) characteristics of young adults with Coronavirus Disease 2019 (COVID-19) pneumonia who were critically ill and to identify the features associated with non-survival.Thirty-eight COVID-19 patients (20-45 years old, 28 men) who had been admitted in the intensive care unit were included, including 18 non-survivors (group 1) and 20 survivors (group 2). Their clinical characteristics and initial and follow-up CT were compared between groups.In group 1, the days from illness onset to death were 21.1 ±â€Š10.3 days; 7 patients had underlying comorbidities. At admission, group 1 exhibited higher serum ferritin and interleukin-6 (IL-6) levels (1142.6 ±â€Š242.4 mg/L and 33.8 ±â€Š18.6 mmol/L) compared with group 2 (728.3 ±â€Š150.9 mg/L and 15.2 ±â€Š6.9 mmol/L, P < .01). Group 1 exhibited more rapidly progressive opacities and consolidation in follow-up CT (16.7 ±â€Š3.1 scores, 15.7 ±â€Š3.1 segments) than group 2 (11.4 ±â€Š4.0 scores, 10.3 ±â€Š4.6 segments, P < .01). The oxygenation index was lower (87.6 ±â€Š19.2 vs 99.1 ±â€Š20.4 mm Hg) and the mechanical ventilation duration was longer (14.7 ±â€Š6.9 vs 9.7 ±â€Š3.7 days) in group 1 compare with group 2 (P < .01).Compared with the survivors, the non-survivors showed higher serum ferritin and IL-6 levels, more rapidly progressive opacities in CT, lower oxygenation index, and longer mechanical ventilation durations. Special attention to ferritin/IL-6 levels and oxygenation index as well as early CT application and timely reexaminations are important to identify the individuals who may be at risk of becoming critically ill.


Asunto(s)
/diagnóstico , Neumonía Viral/diagnóstico , Neumonía Viral/inmunología , Neumonía Viral/mortalidad , Tomografía Computarizada por Rayos X/métodos , Adulto , Enfermedad Crítica , Progresión de la Enfermedad , Femenino , Humanos , Masculino , Neumonía Viral/virología , Estudios Retrospectivos , Análisis de Supervivencia
2.
Medicine (Baltimore) ; 100(5): e23991, 2021 Feb 05.
Artículo en Inglés | MEDLINE | ID: covidwho-1087853

RESUMEN

ABSTRACT: Since the first infected case of Coronavirus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019, the virus has spread swiftly, inflicting upon millions of people around the globe. The objective of the study is to investigate and analyze the clinical characteristics and outcomes of patients infected with COVID-19 in Wuxi, China.Cross-sectional study.The Fifth People's Hospital of Wuxi, China.A total of 48 COVID-19 patients were enrolled in the study from 23 January 2020 to 8 March 2020, and the clinical data of these subjects were collected.Epidemiological, clinical, laboratory, and radiologic characteristics, as well as treatment and outcome data, were collected and analyzed.Of these 48 patients with confirmed COVID-19, 3 were mild cases (6.3%), 44 were moderate cases (91.7%), 1 was severe case (2.1%). The median age of the subjects was 45 years (interquartile range [IQR], 24-59; range, 5-75 years). Twenty-five of the patients (52.1%) were male and 23 (47.9%) were female. Twenty-eight cases (58.3%) returned to Wuxi, Jiangsu Province. Thirty-four (70.8%) cases were infected due to clustering epidemic and 29 cases (85.3%) were attributable to family-clustering epidemic. No obvious clinical symptoms were observed in the cohort of patients, except for 3 mild cases. The most common symptoms include fever (41 [85.4%]), cough (28 [58.3%]), asthenia (13 [27.1%]), expectoration (11 [22.9%]), diarrhea (10 [20.8%]), and dyspnea (5 [10.4%]). Seventeen (35.4%) patients had lower lymphocyte values than baseline, 31 patients (64.6%) had higher d-dimers to exceed the normal range. The distribution of high-resolution computed tomography (HRCT)-positive lesions were as follows: left lung in 5 cases (10.4%), right lung in 9 cases (18.8%), and bilateral lungs in 31 cases (64.6%). In terms of density of lesions: 28 cases (58.3%) showed ground glass shadows in the lung, 7 cases (14.6%) showed solid density shadows, and 10 cases (20.8%) showed mixed density shadows. Extrapulmonary manifestations found that mediastinal lymph nodes were enlarged in 2 cases (4.2%) and that pleural effusion was present in 1 case (2.1%). All patients underwent treatment in quarantine. Forty-five (93.8%) patients received antiviral treatments, 22 (45.8%) patients received antibacterial treatments, 6 (12.5%) patients received glucocorticoid treatments, 2 (4.2%) patients received high flow oxygen inhalation treatments, and 6 (12.5%) patients received noninvasive ventilation treatments. As of 8 March 2020, all 48 patients included in this study were cured. The average time of hospitalization of the 48 patients was 18 ±â€Š6 (mean ±â€ŠSD) days, the average time of the lesion resorption was 11 ±â€Š4 days, and the average time taken to achieve negativity in the result of nucleic acid examination was (10 ±â€Š4) days.The epidemiological characteristics of 48 COVID-19 patients in Wuxi were mainly imported cases and clustered cases. The clinical manifestations of these patients were mainly fever and cough. Laboratory results showed that the lymphocytopenia and increased d-dimer are positively correlated with disease severity. Pulmonary imaging showed unilateral or bilateral ground glass infiltration. Most of the patients entered clinical recovery stage within 15 days after hospitalization.


Asunto(s)
Tos , Fiebre , Hospitalización/estadística & datos numéricos , Atención al Paciente , Evaluación de Síntomas/estadística & datos numéricos , /sangre , /fisiopatología , China/epidemiología , Análisis por Conglomerados , Tos/diagnóstico , Tos/etiología , Salud de la Familia/estadística & datos numéricos , Femenino , Fiebre/diagnóstico , Fiebre/etiología , Productos de Degradación de Fibrina-Fibrinógeno/análisis , Humanos , Linfopenia/diagnóstico , Linfopenia/etiología , Masculino , Persona de Mediana Edad , Atención al Paciente/métodos , Atención al Paciente/estadística & datos numéricos , Radiografía Torácica/estadística & datos numéricos , Tomografía Computarizada por Rayos X/métodos
3.
Eur Rev Med Pharmacol Sci ; 25(2): 1135-1145, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1082411

RESUMEN

OBJECTIVE: To explore the different clinical and CT features distinguishing COVID-19 from H1N1 influenza pneumonia. PATIENTS AND METHODS: We compared two independent cohorts of COVID-19 pneumonia (n=405) and H1N1 influenza pneumonia (n=78), retrospectively. All patients were confirmed by RT-PCR. Four hundred and five cases of COVID-19 pneumonia were confirmed in nine hospitals of Zhejiang province, China from January 21 to February 20, 2020. Seventy-eight cases of H1N1 influenza pneumonia were confirmed in our hospital from January 1, 2017 to February 29, 2020. Their clinical manifestations, laboratory test results, and CT imaging characteristics were compared. RESULTS: COVID-19 pneumonia patients showed less proportions of underlying diseases, fever and respiratory symptoms than those of H1N1 pneumonia patients (p<0.01). White blood cell count, neutrophilic granulocyte percentage, C-reactive protein, procalcitonin, D-Dimer, and lactate dehydrogenase in H1N1 pneumonia patients were higher than those of COVID-19 pneumonia patients (p<0.05). H1N1 pneumonia was often symmetrically located in the dorsal part of inferior lung lobes, while COVID-19 pneumonia was unusually showed as a peripheral but non-specific lobe distribution. Ground glass opacity was more common in COVID-19 pneumonia and consolidation lesions were more common in H1N1 pneumonia (p<0.01). COVID-19 pneumonia lesions showed a relatively clear margin compared with H1N1 pneumonia. Crazy-paving pattern, thickening vessels, reversed halo sign and early fibrotic lesions were more common in COVID-19 pneumonia than H1N1 pneumonia (p<0.05). Pleural effusion in COVID-19 pneumonia was significantly less common than H1N1 pneumonia (p<0.01). CONCLUSIONS: Compared with H1N1 pneumonia in Zhejiang, China, the clinical manifestations of COVID-19 pneumonia were more concealed with less underlying diseases and slighter respiratory symptoms. The more common CT manifestations of COVID-19 pneumonia included ground-glass opacity with a relatively clear margin, crazy-paving pattern, thickening vessels, reversed halo sign, and early fibrotic lesions, while the less common CT manifestations of COVID-19 pneumonia included consolidation and pleural effusion.


Asunto(s)
/diagnóstico por imagen , Subtipo H1N1 del Virus de la Influenza A/aislamiento & purificación , Gripe Humana/diagnóstico por imagen , Gripe Humana/epidemiología , Tomografía Computarizada por Rayos X/métodos , Adulto , Anciano , Estudios de Casos y Controles , China/epidemiología , Estudios de Cohortes , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos
4.
Eur Rev Med Pharmacol Sci ; 25(2): 1080-1086, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1081037

RESUMEN

OBJECTIVE: This study aimed to explore the atypical imaging findings of the novel coronavirus pneumonia (COVID-19) and its evolution. MATERIALS AND METHODS: The atypical imaging data of ten patients in our hospital who tested positive for COVID-19 were analyzed retrospectively, and the distribution, morphology, and image evolution of the lesions were analyzed. High-resolution computed tomography (HRCT) was performed in all cases, and the imaging features were analyzed and summarized by two senior radiologists. RESULTS: Of these ten patients, three were male, and seven were female. The age of these patients ranged from 21-53 years, with an average age of 36.3 ± 3.6. The first symptom was fever in nine cases and dry cough in one case. A total of 17 lesions were detected in these ten patients. Five patients had a single lesion, and five patients had multiple lesions, for a total of 12 lesions. Ten lesions (58.82%) were located in the inferior lobe of the right lung, four lesions (23.53%) in the left inferior lobe, two lesions (11.76%) in the left upper lobe, and one lesion (5.88%) in the right middle lobe. Among the five single lesions, two were solid lesions, two were mixed ground-glass lesions, and one was a pure ground-glass lesion. Among the 12 multiple lesions, eight were solid lesions, two were mixed ground-glass lesions, and two were pure ground-glass lesions. Atypical manifestations in image signs: five lesions (29.41%) had single solid and sub-solid nodules, and four lesions (23.53%) had cavitary nodules. Typical manifestation (the presence of "white lung"): three lesions (17.65%) had an air bronchogram, two lesions (11.76%) had crazy-paving signs, two lesions (11.76%) had vascular thickening, and one lesion (5.88%) had halo signs. At reexamination 2-6 days later, 15 lesions (88.24%) had enlarged or increased, and two lesions (11.76%) had decreased or absorbed. CONCLUSIONS: Patients with COVID-19 may have atypical imaging findings. Radiologists should improve their understanding of the novel coronavirus pneumonia to avoid any missed diagnoses.


Asunto(s)
/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Femenino , Humanos , Pulmón/fisiopatología , Masculino , Persona de Mediana Edad , Tomografía Computarizada por Rayos X/tendencias , Adulto Joven
5.
J Coll Physicians Surg Pak ; 31(1): 14-20, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1068267

RESUMEN

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.


Asunto(s)
Pulmón/diagnóstico por imagen , Neumonía Viral/diagnóstico por imagen , Tórax/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Pruebas Diagnósticas de Rutina , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Pakistán , Radiografía Torácica/métodos , Atención Terciaria de Salud , Adulto Joven
6.
J Vis Exp ; (166)2020 12 19.
Artículo en Inglés | MEDLINE | ID: covidwho-1067800

RESUMEN

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.


Asunto(s)
/diagnóstico por imagen , Imagenología Tridimensional/normas , Pulmón/diagnóstico por imagen , Programas Informáticos/normas , Tomografía Computarizada por Rayos X/normas , /epidemiología , Humanos , Imagenología Tridimensional/métodos , Neumonía/diagnóstico por imagen , Neumonía/epidemiología , Tomografía Computarizada por Rayos X/métodos
7.
PLoS One ; 16(1): e0245518, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1067418

RESUMEN

OBJECTIVES: High-risk CXR features in COVID-19 are not clearly defined. We aimed to identify CXR features that correlate with severe COVID-19. METHODS: All confirmed COVID-19 patients admitted within the study period were screened. Those with suboptimal baseline CXR were excluded. CXRs were reviewed by three independent radiologists and opacities recorded according to zones and laterality. The primary endpoint was defined as hypoxia requiring supplemental oxygen, and CXR features were assessed for association with this endpoint to identify high-risk features. These features were then used to define criteria for a high-risk CXR, and clinical features and outcomes of patients with and without baseline high-risk CXR were compared using logistic regression analysis. RESULTS: 109 patients were included. In the initial analysis of 40 patients (36.7%) with abnormal baseline CXR, presence of bilateral opacities, multifocal opacities, or any upper or middle zone opacity were associated with supplemental oxygen requirement. Of the entire cohort, 29 patients (26.6%) had a baseline CXR with at least one of these features. Having a high-risk baseline CXR was significantly associated with requiring supplemental oxygen in univariate (odds ratio 14.0, 95% confidence interval 3.90-55.60) and multivariate (adjusted odds ratio 8.38, 95% CI 2.43-28.97, P = 0.001) analyses. CONCLUSION: We identified several high-risk CXR features that are significantly associated with severe illness. The association of upper or middle zone opacities with severe illness has not been previously emphasized. Recognition of these specific high-risk CXR features is important to prioritize limited healthcare resources for sicker patients.


Asunto(s)
/diagnóstico por imagen , Adulto , /virología , Estudios de Cohortes , Servicio de Urgencia en Hospital , Femenino , Hospitalización , Humanos , Pulmón/diagnóstico por imagen , Masculino , Persona de Mediana Edad , Radiografía Torácica/métodos , Estudios Retrospectivos , Factores de Riesgo , Índice de Severidad de la Enfermedad , Tórax/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos
8.
BMJ Case Rep ; 14(2)2021 Feb 04.
Artículo en Inglés | MEDLINE | ID: covidwho-1066842

RESUMEN

A previously healthy 40-year-old man was referred to our emergency department with pruritic skin lesions and dyspnoea. Laboratory investigation revealed hypereosinophilia. Further diagnostic work-up confirmed the diagnosis of idiopathic hypereosinophilic syndrome (iHES), a rare myeloproliferative disease with a heterogeneous clinical presentation. We describe a unique case with cardiac, pulmonary, hepatic and cutaneous involvement at time of presentation. This case accentuates the importance of an extensive multidisciplinary diagnostic work-up, since iHES is a condition with potential rapid progressive multiorgan failure which requires prompt analysis and treatment. In addition, this case emphasises the importance of being aware of tunnel vision, especially during the COVID-19 pandemic, which might give rise to an increased risk of missing rare diagnoses. Our patient was treated with prednisolone, after which both his clinical condition and eosinophil concentrations markedly improved.


Asunto(s)
Síndrome Hipereosinofílico/diagnóstico , Síndrome Hipereosinofílico/patología , Adulto , Antiinflamatorios/uso terapéutico , Biopsia/métodos , Diagnóstico Diferencial , Disnea/complicaciones , Eosinófilos/patología , Humanos , Síndrome Hipereosinofílico/complicaciones , Síndrome Hipereosinofílico/tratamiento farmacológico , Masculino , Prednisolona/uso terapéutico , Enfermedades de la Piel/complicaciones , Enfermedades de la Piel/diagnóstico , Tomografía Computarizada por Rayos X/métodos , Resultado del Tratamiento
9.
Commun Biol ; 4(1): 35, 2021 01 04.
Artículo en Inglés | MEDLINE | ID: covidwho-1065967

RESUMEN

Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.


Asunto(s)
/diagnóstico , Aprendizaje Profundo , Redes Neurales de la Computación , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Tomografía Computarizada por Rayos X/métodos , Algoritmos , /virología , Humanos , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , /fisiología , Sensibilidad y Especificidad
10.
Ann Palliat Med ; 10(1): 560-571, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1063565

RESUMEN

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.


Asunto(s)
/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Neumonía Viral/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Infecciones por Citomegalovirus/diagnóstico por imagen , Femenino , Humanos , Virus de la Influenza A , Virus de la Influenza B , Gripe Humana/diagnóstico por imagen , Pulmón/virología , Masculino , Persona de Mediana Edad , Neumonía Viral/virología , Estudios Retrospectivos
11.
Eur J Radiol ; 134: 109442, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1060223

RESUMEN

PURPOSE: The vascular enlargement (VE) pattern differs from previously described imaging patterns for pneumonia. This study aimed to investigate the incidence, computed tomography (CT) characteristics, and diagnostic value of the VE pattern in coronavirus disease 2019 (COVID-19). METHOD: The CT data of 106 patients with COVID-19 from January 19 to February 29, 2020, and 52 patients with influenza virus pneumonia (IVP) from January 2018 to February 2020 were retrospectively collected. The incidences of the VE pattern between the two groups were compared. The CT manifestations of COVID-19 were analyzed with a particular focus on the VE pattern's specific CT signs, dynamic changes, and relationships with lesion size and disease severity. RESULTS: Peripheral and multilobar ground-glass opacities (GGOs) or mixed GGOs with various sizes and morphologies were typical features of COVID-19 on initial CT. The VE pattern was more common in COVID-19 (88/106, 83.02 %) than in IVP (10/52, 19.23 %) on initial CT (P < 0.001). Three special VE-pattern-specific CT signs, including central vascular sign, ginkgo leaf sign, and comb sign, were identified. Four types of dynamic changes in the VE pattern were observed on initial and follow-up CT, which were closely associated with the evolution of lesions and the time interval from the onset of symptoms to initial CT scan. The VE pattern in COVID-19 was more commonly seen in larger lesions and patients with severe-critical type (all P < 0.001). CONCLUSIONS: The VE pattern is a valuable CT sign for differentiating COVID-19 from IVP, which correlates with more extensive or serious disease. A good understanding of the CT characteristics of the VE pattern may contribute to the early and accurate diagnosis of COVID-19 and prediction of the evolution of lesions.


Asunto(s)
/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Neumonía/diagnóstico por imagen , Arteria Pulmonar/patología , Venas Pulmonares/diagnóstico por imagen , Venas Pulmonares/patología , Tomografía Computarizada por Rayos X/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Diagnóstico Diferencial , Femenino , Humanos , Gripe Humana/diagnóstico por imagen , Gripe Humana/patología , Pulmón/irrigación sanguínea , Pulmón/patología , Masculino , Persona de Mediana Edad , Neumonía/patología , Arteria Pulmonar/diagnóstico por imagen , Estudios Retrospectivos , Adulto Joven
12.
J Transl Med ; 19(1): 29, 2021 01 07.
Artículo en Inglés | MEDLINE | ID: covidwho-1059725

RESUMEN

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.


Asunto(s)
/métodos , /diagnóstico , Neumonía Viral/diagnóstico por imagen , Neumonía Viral/diagnóstico , Tomografía Computarizada por Rayos X/métodos , Adulto , Anciano , /estadística & datos numéricos , China/epidemiología , Femenino , Ensayos Analíticos de Alto Rendimiento/métodos , Ensayos Analíticos de Alto Rendimiento/estadística & datos numéricos , Humanos , Aprendizaje Automático , Masculino , Persona de Mediana Edad , Modelos Estadísticos , Nomogramas , Pandemias , Neumonía Viral/epidemiología , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/estadística & datos numéricos , Estudios Retrospectivos , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos X/estadística & datos numéricos , Investigación en Medicina Traslacional
14.
BMJ Case Rep ; 14(1)2021 Jan 28.
Artículo en Inglés | MEDLINE | ID: covidwho-1054633

RESUMEN

We present a case of a 38-year-old man with a history of chronic thromboembolic pulmonary hypertension on therapeutic anticoagulation and recent hospitalisation for COVID-19 disease who was hospitalised for recurrent acute pulmonary embolism despite therapeutic anticoagulation with warfarin (International Normalized Ratio (INR) of 3.0). Our case highlights the hypercoagulable state associated with COVID-19 disease and the absence of standardised approaches to anticoagulation treatment for this population.


Asunto(s)
Anticoagulantes/uso terapéutico , Embolia Pulmonar/complicaciones , Embolia Pulmonar/tratamiento farmacológico , Warfarina/uso terapéutico , Adulto , Enfermedad Crónica , Diagnóstico Diferencial , Humanos , Masculino , Arteria Pulmonar/diagnóstico por imagen , Embolia Pulmonar/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos
15.
Monaldi Arch Chest Dis ; 91(1)2021 Jan 25.
Artículo en Inglés | MEDLINE | ID: covidwho-1050664

RESUMEN

Dear Editor, The Corona virus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the Wuhan province of china in December 2019. COVID-19 spread to the world in a short time and was declared as public health emergency of international concern by World Health Organization...


Asunto(s)
Diabetes Mellitus Tipo 2 , Hemoglobina A Glucada/análisis , Pulmón , Terapia por Inhalación de Oxígeno/métodos , Anciano , /diagnóstico , Diabetes Mellitus Tipo 2/sangre , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Resultado Fatal , Fibrosis/diagnóstico , Fibrosis/etiología , Fibrosis/terapia , Servicios de Atención de Salud a Domicilio , Humanos , Pulmón/diagnóstico por imagen , Pulmón/patología , Masculino , Factores de Riesgo , Tomografía Computarizada por Rayos X/métodos , Resultado del Tratamiento
16.
Nat Commun ; 12(1): 634, 2021 01 27.
Artículo en Inglés | MEDLINE | ID: covidwho-1049964

RESUMEN

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.


Asunto(s)
/diagnóstico , Aprendizaje Profundo , Redes Neurales de la Computación , Tomografía Computarizada por Rayos X/métodos , Inteligencia Artificial , Humanos , Modelos Biológicos , Análisis Multivariante , Pronóstico , Radiólogos , Índice de Severidad de la Enfermedad
17.
J Med Syst ; 45(3): 28, 2021 Jan 26.
Artículo en Inglés | MEDLINE | ID: covidwho-1047302

RESUMEN

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.


Asunto(s)
Inteligencia Artificial/normas , Pulmón/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Anciano , Anciano de 80 o más Años , Aprendizaje Profundo/normas , Femenino , Humanos , Italia , Masculino , Persona de Mediana Edad , Reproducibilidad de los Resultados , Índice de Severidad de la Enfermedad
18.
Monaldi Arch Chest Dis ; 90(4)2020 Dec 23.
Artículo en Inglés | MEDLINE | ID: covidwho-1042105

RESUMEN

We report the case of a man affected by cystic fibrosis who developed a severe SARS-CoV-2 related pneumonia in March 2020. In addition to lopinavir/ritonavir and hydroxychloroquine, he was treated with two doses of tocilizumab, displaying a significant clinical improvement. This is the first case described in the literature of an adult patient affected by cystic fibrosis who received tocilizumab for COVID-19, with documented total recovery, also assessed by a spirometry.


Asunto(s)
Anticuerpos Monoclonales Humanizados/administración & dosificación , Fibrosis Quística , Hidroxicloroquina/administración & dosificación , Lopinavir/administración & dosificación , Neumonía Viral , Infecciones del Sistema Respiratorio/microbiología , Ritonavir/administración & dosificación , /aislamiento & purificación , Adulto , Antivirales/administración & dosificación , /diagnóstico , /fisiopatología , Fibrosis Quística/complicaciones , Fibrosis Quística/inmunología , Fibrosis Quística/fisiopatología , Combinación de Medicamentos , Humanos , Pulmón/diagnóstico por imagen , Pulmón/fisiopatología , Masculino , Terapia por Inhalación de Oxígeno/métodos , Neumonía Viral/diagnóstico , Neumonía Viral/tratamiento farmacológico , Neumonía Viral/etiología , Receptores de Interleucina-6/antagonistas & inhibidores , Pruebas de Función Respiratoria/métodos , Infecciones del Sistema Respiratorio/tratamiento farmacológico , Infecciones del Sistema Respiratorio/etiología , Tomografía Computarizada por Rayos X/métodos , Resultado del Tratamiento
19.
Respir Res ; 22(1): 13, 2021 Jan 12.
Artículo en Inglés | MEDLINE | ID: covidwho-1024368

RESUMEN

BACKGROUND: It is essential to avoid admission of patients with undetected corona virus disease 2019 (COVID-19) to hospitals' general wards. Even repeated negative reverse transcription polymerase chain reaction (RT-PCR) results do not rule-out COVID-19 with certainty. The study aimed to evaluate a rule-out strategy for COVID-19 using chest computed tomography (CT) in adults being admitted to the emergency department and suspected of COVID-19. METHODS: In this prospective, single centre, diagnostic accuracy cohort study, consecutive adults (≥ 18 years) presenting with symptoms consistent with COVID-19 or previous contact to infected individuals, admitted to the emergency department and supposed to be referred to general ward were included in March and April 2020. All participants underwent low-dose chest CT. RT-PCR- and specific antibody tests were used as reference standard. Main outcome measures were sensitivity and specificity of chest CT. Predictive values were calculated based on the theorem of Bayes using Fagan's nomogram. RESULTS: Of 165 participants (56.4% male, 71 ± 16 years) included in the study, the diagnosis of COVID-19 was confirmed with RT-PCR and AB tests in 13 participants (prevalence 7.9%). Sensitivity and specificity of chest CT were 84.6% (95% confidence interval [CI], 54.6-98.1) and 94.7% (95% CI, 89.9-97.7), respectively. Positive and negative likelihood ratio of chest CT were 16.1 (95% CI, 7.9-32.8) and 0.16 (95% CI, 0.05-0.58) and positive and negative predictive value were 57.9% (95% CI, 40.3-73.7) and 98.6% (95% CI, 95.3-99.6), respectively. CONCLUSION: At a low prevalence of COVID-19, chest CT could be used as a complement to repeated RT-PCR testing for early COVID-19 exclusion in adults with suspected infection before referral to hospital's general wards. Trial registration ClinicalTrials.gov: NCT04357938 April 22, 2020.


Asunto(s)
/diagnóstico por imagen , Servicio de Urgencia en Hospital/tendencias , Admisión del Paciente/tendencias , Cuarentena/tendencias , Tomografía Computarizada por Rayos X/tendencias , Anciano , Anciano de 80 o más Años , Estudios de Cohortes , Femenino , Alemania/epidemiología , Humanos , Masculino , Persona de Mediana Edad , Prevalencia , Estudios Prospectivos , Cuarentena/métodos , Tomografía Computarizada por Rayos X/métodos
20.
Sci Rep ; 11(1): 417, 2021 01 11.
Artículo en Inglés | MEDLINE | ID: covidwho-1019886

RESUMEN

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.


Asunto(s)
/diagnóstico por imagen , Aprendizaje Profundo , Pulmón/diagnóstico por imagen , Tórax/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodos
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