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1.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao ; 43(2): 216-221, 2021 Apr 28.
Artículo en Chino | MEDLINE | ID: covidwho-1225868

RESUMEN

Objective To analyze the CT characteristics of consolidation type of pulmonary cryptococcosis in immunocompetent patients,and thus improve the diagnosis of this disease. Methods A total of 20 cases with consolidation-type pulmonary cryptococcosis confirmed by pathological examinations were studied.Each patient underwent breath-hold multislice spiral CT,and 10 patients underwent contrast enhanced CT.The data including lesion number,lesion distribution,lesion density,performance of enhanced CT scan,accompanying signs,and prognosis were analyzed. Results The occurrence rates of single and multiple lesions were 80.0%(n=16)and 20.0%(n=4),respectively.In all the 16 multiple-lesion patients,the occurrence rate of unilateral lobar distribution was 56.0%(n=9).The 76 measurable lesions mainly presented subpleural distribution(71.1%,n=54)and lower pulmonary distribution(75.0%,n=57).A total of 39 lesions were detected in the 10 patients received contrast enhanced CT,in which 31 lesions(79.5%)showed homogeneous enhancement,34 lesions(87.2%)showed moderate enhancement,and all the lesions manifested angiogram sign.Consolidation lesions were accompanied by many CT signs,of which air bronchogram sign had the occurrence rate of 63.2%(n=48),including types Ⅲ(n =37)and Ⅳ(n=11).Other signs included halo signs(43/76,56.6%),vacuoles or cavities(9/76,11.8%),pleural thickening(14/20,70.0%),and pleural effusion(2/20,10.0%).After treatment,the lesions of 7 patients were basically absorbed and eventually existed in the form of fibrosis. Conclusions The lesions in the immunocompetent patients with consolidation type of pulmonary cryptococcosis usually occur in the lower lobe and close to the pleura,mainly presenting unilateral distribution.The CT angiogram signs,proximal air bronchogram signs,and halo signs are the main features of this disease,which contribute to the diagnosis.


Asunto(s)
Criptococosis , Enfermedades Pulmonares Fúngicas , Criptococosis/diagnóstico por imagen , Humanos , Pulmón , Enfermedades Pulmonares Fúngicas/diagnóstico por imagen , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
2.
Adv Exp Med Biol ; 1318: 413-434, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1222727

RESUMEN

The coronavirus disease 2019 (COVID-19) pandemic launched in the third decade of the twenty-first century and continued to present time to cause the worst challenges the modern medicine has ever encountered. Medical imaging is an essential part of the universal fight against this pandemic. In the absence of documented treatment and vaccination, early accurate diagnosis of infected patients is the backbone of this pandemic management. This chapter reviews different aspects of medical imaging in the context of COVID-19.


Asunto(s)
Humanos , Pandemias , Radiografía Torácica , Tomografía Computarizada por Rayos X
3.
JAMA ; 325(15): 1525-1534, 2021 04 20.
Artículo en Inglés | MEDLINE | ID: covidwho-1222575

RESUMEN

Importance: Little is known about long-term sequelae of COVID-19. Objective: To describe the consequences at 4 months in patients hospitalized for COVID-19. Design, Setting, and Participants: In a prospective uncontrolled cohort study, survivors of COVID-19 who had been hospitalized in a university hospital in France between March 1 and May 29, 2020, underwent a telephone assessment 4 months after discharge, between July 15 and September 18, 2020. Patients with relevant symptoms and all patients hospitalized in an intensive care unit (ICU) were invited for further assessment at an ambulatory care visit. Exposures: Survival of hospitalization for COVID-19. Main Outcomes and Measures: Respiratory, cognitive, and functional symptoms were assessed by telephone with the Q3PC cognitive screening questionnaire and a checklist of symptoms. At the ambulatory care visit, patients underwent pulmonary function tests, lung computed tomographic scan, psychometric and cognitive tests (including the 36-Item Short-Form Health Survey and 20-item Multidimensional Fatigue Inventory), and, for patients who had been hospitalized in the ICU or reported ongoing symptoms, echocardiography. Results: Among 834 eligible patients, 478 were evaluated by telephone (mean age, 61 years [SD, 16 years]; 201 men, 277 women). During the telephone interview, 244 patients (51%) declared at least 1 symptom that did not exist before COVID-19: fatigue in 31%, cognitive symptoms in 21%, and new-onset dyspnea in 16%. There was further evaluation in 177 patients (37%), including 97 of 142 former ICU patients. The median 20-item Multidimensional Fatigue Inventory score (n = 130) was 4.5 (interquartile range, 3.0-5.0) for reduced motivation and 3.7 (interquartile range, 3.0-4.5) for mental fatigue (possible range, 1 [best] to 5 [worst]). The median 36-Item Short-Form Health Survey score (n = 145) was 25 (interquartile range, 25.0-75.0) for the subscale "role limited owing to physical problems" (possible range, 0 [best] to 100 [worst]). Computed tomographic lung-scan abnormalities were found in 108 of 171 patients (63%), mainly subtle ground-glass opacities. Fibrotic lesions were observed in 33 of 171 patients (19%), involving less than 25% of parenchyma in all but 1 patient. Fibrotic lesions were observed in 19 of 49 survivors (39%) with acute respiratory distress syndrome. Among 94 former ICU patients, anxiety, depression, and posttraumatic symptoms were observed in 23%, 18%, and 7%, respectively. The left ventricular ejection fraction was less than 50% in 8 of 83 ICU patients (10%). New-onset chronic kidney disease was observed in 2 ICU patients. Serology was positive in 172 of 177 outpatients (97%). Conclusions and Relevance: Four months after hospitalization for COVID-19, a cohort of patients frequently reported symptoms not previously present, and lung-scan abnormalities were common among those who were tested. These findings are limited by the absence of a control group and of pre-COVID assessments in this cohort. Further research is needed to understand longer-term outcomes and whether these findings reflect associations with the disease.


Asunto(s)
/complicaciones , Hospitalización , Enfermedades Pulmonares/etiología , Pulmón/patología , Anciano , Ansiedad/etiología , Trastornos del Conocimiento/etiología , Estudios de Cohortes , Depresión/etiología , Disnea/etiología , Fatiga/etiología , Femenino , Estudios de Seguimiento , Humanos , Pulmón/diagnóstico por imagen , Enfermedades Pulmonares/diagnóstico por imagen , Enfermedades Pulmonares/patología , Masculino , Persona de Mediana Edad , Tomografía Computarizada por Rayos X
4.
Front Public Health ; 9: 648360, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1221994

RESUMEN

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.


Asunto(s)
Anciano , China/epidemiología , Humanos , Unidades de Cuidados Intensivos , Persona de Mediana Edad , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
5.
Ann Am Thorac Soc ; 18(5): 799-806, 2021 05.
Artículo en Inglés | MEDLINE | ID: covidwho-1219727

RESUMEN

Rationale: The natural history of recovery from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unknown. Because fibrosis with persistent physiological deficit is a previously described feature of patients recovering from similar coronaviruses, treatment represents an early opportunity to modify the disease course, potentially preventing irreversible impairment.Objectives: Determine the incidence of and describe the progression of persistent inflammatory interstitial lung disease (ILD) following SARS-CoV-2 when treated with prednisolone.Methods: A structured assessment protocol screened for sequelae of SARS-CoV-2 pneumonitis. Eight hundred thirty-seven patients were assessed by telephone 4 weeks after discharge. Those with ongoing symptoms had outpatient assessment at 6 weeks. Thirty patients diagnosed with persistent interstitial lung changes at a multidisciplinary team meeting were reviewed in the interstitial lung disease service and offered treatment. These patients had persistent, nonimproving symptoms.Results: At 4 weeks after discharge, 39% of patients reported ongoing symptoms (325/837) and were assessed. Interstitial lung disease, predominantly organizing pneumonia, with significant functional deficit was observed in 35/837 survivors (4.8%). Thirty of these patients received steroid treatment, resulting in a mean relative increase in transfer factor following treatment of 31.6% (standard deviation [SD] ± 27.6, P < 0.001), and forced vital capacity of 9.6% (SD ± 13.0, P = 0.014), with significant symptomatic and radiological improvement.Conclusions: Following SARS-CoV-2 pneumonitis, a cohort of patients are left with both radiological inflammatory lung disease and persistent physiological and functional deficit. Early treatment with corticosteroids was well tolerated and associated with rapid and significant improvement. These preliminary data should inform further study into the natural history and potential treatment for patients with persistent inflammatory ILD following SARS-CoV-2 infection.


Asunto(s)
Cuidados Posteriores/métodos , Glucocorticoides/uso terapéutico , Enfermedades Pulmonares Intersticiales , Pulmón , Pruebas de Función Respiratoria/métodos , /mortalidad , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Pulmón/diagnóstico por imagen , Pulmón/fisiopatología , Pulmón/virología , Enfermedades Pulmonares Intersticiales/diagnóstico , Enfermedades Pulmonares Intersticiales/etiología , Enfermedades Pulmonares Intersticiales/fisiopatología , Enfermedades Pulmonares Intersticiales/terapia , Masculino , Persona de Mediana Edad , Alta del Paciente/estadística & datos numéricos , Sobrevivientes/estadística & datos numéricos , Evaluación de Síntomas/métodos , Evaluación de Síntomas/estadística & datos numéricos , Tomografía Computarizada por Rayos X/métodos , Resultado del Tratamiento , Reino Unido/epidemiología
6.
J Med Internet Res ; 23(4): e27468, 2021 04 26.
Artículo en Inglés | MEDLINE | ID: covidwho-1219288

RESUMEN

BACKGROUND: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. OBJECTIVE: Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm. METHODS: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. RESULTS: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.


Asunto(s)
/diagnóstico por imagen , Aprendizaje Profundo , Diagnóstico por Computador , Pulmón/diagnóstico por imagen , Pulmón/virología , /aislamiento & purificación , Conjuntos de Datos como Asunto , Diagnóstico Precoz , Humanos , Pandemias , Tomografía Computarizada por Rayos X
7.
Dtsch Arztebl Int ; 118(5): 66, 2021 02 05.
Artículo en Inglés | MEDLINE | ID: covidwho-1215271
8.
J Healthc Eng ; 2021: 5528441, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1211612

RESUMEN

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.


Asunto(s)
/diagnóstico por imagen , Aprendizaje Profundo , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Tomografía Computarizada por Rayos X , Algoritmos , Bases de Datos Factuales , Humanos , Pandemias , Radiografía Torácica , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos X/clasificación , Tomografía Computarizada por Rayos X/métodos
9.
AJR Am J Roentgenol ; 215(1): 121-126, 2020 07.
Artículo en Inglés | MEDLINE | ID: covidwho-1211773

RESUMEN

OBJECTIVE. Confronting the new coronavirus infection known as coronavirus disease 2019 (COVID-19) is challenging and requires excluding patients with suspected COVID-19 who actually have other diseases. The purpose of this study was to assess the clinical features and CT manifestations of COVID-19 by comparing patients with COVID-19 pneumonia with patients with non-COVID-19 pneumonia who presented at a fever observation department in Shanghai, China. MATERIALS AND METHODS. Patients were retrospectively enrolled in the study from January 19 through February 6, 2020. All patients underwent real-time reverse transcription-polymerase chain reaction (RT-PCR) testing. RESULTS. Eleven patients had RT-PCR test results that were positive for severe acute respiratory syndrome coronavirus 2, whereas 22 patients had negative results. No statistical difference in clinical features was observed (p > 0.05), with the exception of leukocyte and platelet counts (p < 0.05). The mean (± SD) interval between onset of symptoms and admission to the fever observation department was 4.40 ± 2.00 and 5.52 ± 4.00 days for patients with positive and negative RT-PCR test results, respectively. The frequency of opacifications in patients with positive results and patients with negative results, respectively, was as follows: ground-glass opacities (GGOs), 100.0% versus 90.9%; mixed GGO, 63.6% versus 72.7%; and consolidation, 54.5% versus 77.3%. In patients with positive RT-PCR results, GGOs were the most commonly observed opacification (seen in 100.0% of patients) and were predominantly located in the peripheral zone (100.0% of patients), compared with patients with negative results (31.8%) (p = 0.05). The median number of affected lung lobes and segments was higher in patients with positive RT-PCR results than in those with negative RT-PCR results (five vs 3.5 affected lobes and 15 vs nine affected segments; p < 0.05). Although the air bronchogram reticular pattern was more frequently seen in patients with positive results, centrilobular nodules were less frequently seen in patients with positive results. CONCLUSION. At the point during the COVID-19 outbreak when this study was performed, imaging patterns of multifocal, peripheral, pure GGO, mixed GGO, or consolidation with slight predominance in the lower lung and findings of more extensive GGO than consolidation on chest CT scans obtained during the first week of illness were considered findings highly suspicious of COVID-19.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/complicaciones , Infecciones por Coronavirus/diagnóstico por imagen , Brotes de Enfermedades , Pulmón/diagnóstico por imagen , Neumonía Viral/complicaciones , Neumonía Viral/diagnóstico por imagen , Adulto , Anciano , China , Infecciones por Coronavirus/epidemiología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/epidemiología , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
11.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 38(2): 379-386, 2021 Apr 25.
Artículo en Chino | MEDLINE | ID: covidwho-1207916

RESUMEN

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.


Asunto(s)
Humanos , Pulmón/diagnóstico por imagen , Aprendizaje Automático , Tomografía Computarizada por Rayos X
12.
PLoS One ; 16(4): e0249607, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1207629

RESUMEN

INTRODUCTION: Acute presentations of COVID-19 infection vary, ranging from asymptomatic carriage through to severe clinical manifestations including acute respiratory distress syndrome (ARDS). Longer term sequelae of COVID-19 infection includes lung fibrosis in a proportion of patients. Krebs von den Lungen 6 (KL-6) is a mucin like glycoprotein that has been proposed as a marker of pulmonary epithelial cell injury. We sought to determine whether KL-6 was a marker of 1) the severity of acute COVID-19 infection, or 2) the persistence of symptoms/radiological abnormalities at medium term follow up. METHODS: Prospective single centre observational study. RESULTS: Convalescent KL-6 levels were available for 93 patients (male 63%, mean age 55.8 years) who attended an 12-week follow up appointment after being admitted to hospital with COVID-19. For 67 patients a baseline KL-6 result was available for comparison. There was no significant correlations between baseline KL-6 and the admission CXR severity score or clinical severity NEWS score. Furthermore, there was no significant difference in the baseline KL-6 level and an initial requirement for oxygen on admission or the severity of acute infection as measured at 28 days. There was no significant difference in the 12-week KL-6 level and the presence or absence of subjective breathlessness but patients with abnormal CT scans at 12 weeks had significantly higher convalescent KL-6 levels compared to the remainder of the cohort (median 1101 IU/ml vs 409 IU/ml). CONCLUSIONS: The association between high KL-6 levels at 12 weeks and persisting CT abnormalities (GGO/fibrosis), is a finding that requires further exploration. Whether KL-6 may help differentiate those patients with persisting dyspnoea due to complications rather than deconditioning or dysfunctional breathing alone, is an important future research question.


Asunto(s)
/sangre , Mucina-1/sangre , Adulto , Anciano , Biomarcadores/sangre , /patología , Progresión de la Enfermedad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Índice de Severidad de la Enfermedad , Tomografía Computarizada por Rayos X
13.
BMJ Case Rep ; 14(4)2021 Apr 27.
Artículo en Inglés | MEDLINE | ID: covidwho-1206018

RESUMEN

A middle-aged woman with diabetes presented with left-sided facial pain, complete ptosis and fever of short duration. On presentation, she had hyperglycaemia without ketosis. There was total ophthalmoplegia of the left eye with a visual acuity of 6/36. She incidentally tested positive for COVID-19. CT paranasal sinus and MRI brain revealed left-sided pansinusitis with acute infarct in the left parieto-occipital region without angioinvasion. An emergency functional endoscopic sinus procedure was done, which confirmed mucormycosis on histopathological examination. After 1 week of conventional amphotericin B and antibiotics, repeat CT brain showed improvement in mucosal thickening and sinusitis. This case is a rare presentation of mucormycosis associated with rapid progression to orbital apex syndrome with brain infarction in a patient with non-ketotic diabetes and COVID-19. Early diagnosis and treatment are essential to prevent further end-organ damage. It is also interesting that there was no angioinvasion and transient periarterial inflammation was attributed to brain infarction.


Asunto(s)
Blefaroptosis/complicaciones , Complicaciones de la Diabetes , Mucormicosis/diagnóstico , Oftalmoplejía/complicaciones , Enfermedades Orbitales/complicaciones , Enfermedades de los Senos Paranasales/complicaciones , Anfotericina B/uso terapéutico , Antifúngicos/uso terapéutico , Encéfalo/diagnóstico por imagen , Femenino , Humanos , Imagen por Resonancia Magnética , Persona de Mediana Edad , Mucormicosis/tratamiento farmacológico , Enfermedades Orbitales/diagnóstico por imagen , Enfermedades Orbitales/etiología , Enfermedades de los Senos Paranasales/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Resultado del Tratamiento
14.
BMC Pulm Med ; 21(1): 136, 2021 Apr 26.
Artículo en Inglés | MEDLINE | ID: covidwho-1204068

RESUMEN

BACKGROUND: All over the world, SARS-CoV-2 pneumonia is causing a significant short-term morbidity and mortality, but the medium-term impact on lung function and quality of life of affected patients are still unknown. METHODS: In this prospective observational study, 39 patients with SARS-CoV-2 pneumonia were recruited from a single COVID-19 hospital in Southern Switzerland. At three months patients underwent radiological and functional follow-up through CT scan, lung function tests, and 6 min walking test. Furthermore, quality of life was assessed through self-reported questionnaires. RESULTS: Among 39 patients with SARS-CoV-2 pneumonia, 32 (82% of all participants) presented abnormalities in CT scan and 25 (64.1%) had lung function tests impairment at three months. Moreover, 31 patients (79.5%) reported a perception of poor health due to respiratory symptoms and all 39 patients showed an overall decreased quality of life. CONCLUSIONS: Medium-term follow up at three months of patients diagnosed with SARS-CoV-2 pneumonia shows the persistence of abnormalities in CT scans, a significant functional impairment assessed by lung function tests and a decreased quality of life in affected patients. Further studies evaluating the long-term impact are warranted to guarantee an appropriate follow-up to patients recovering from SARS-CoV-2 pneumonia.


Asunto(s)
/fisiopatología , Pulmón/fisiopatología , Calidad de Vida , Anciano , Convalecencia , Femenino , Volumen Espiratorio Forzado , Estado de Salud , Humanos , Tiempo de Internación , Pulmón/diagnóstico por imagen , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Capacidad de Difusión Pulmonar , Recuperación de la Función , Pruebas de Función Respiratoria , Suiza , Tomografía Computarizada por Rayos X , Capacidad Vital , Prueba de Paso
16.
Sci Rep ; 11(1): 8731, 2021 04 22.
Artículo en Inglés | MEDLINE | ID: covidwho-1199315

RESUMEN

Coronavirus disease 2019 (COVID-19) can present with a variety of symptoms. Severity of the disease may be associated with several factors. Here, we review clinical features of COVID-19 inpatients with different severities. This cross-sectional study was performed in Imam Reza hospital, Mashhad, Iran, during February-April 2020. COVID-19 patients with typical computed tomography (CT) patterns and/or positive reverse-transcriptase polymerase chain reaction (RT-PCR) were included. The patients were classified into three groups of moderate, severe, and critical based on disease severity. Demographic, clinical, laboratory, and radiologic findings were collected and compared. P < 0.05 was considered statistically significant. Overall, 200 patients with mean age of 69.75 ± 6.39 years, of whom 82 (41%) were female were studied. Disease was severe/critical in the majority of patients (167, 83.5%). Disease severity was significantly associated with age, malignant comorbidities, dyspnea, nausea/vomiting, confusion, respiratory rate, pulse rate, O2 saturation, extent of CT involvement, serum C-reactive protein (CRP), pH, pO2, and aspartate transaminase (P < 0.05). Moreover, complications including shock, coagulopathy, acidosis, sepsis, acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission, and intubation were significantly higher in patients with higher severities (P < 0.05). O2 saturation, nausea/vomiting, and extent of lung CT involvement were independent predictors of severe/critical COVID-19 (OR 0.342, 45.93, and 25.48, respectively; P < 0.05). Our results indicate O2 saturation, nausea/vomiting, and extent of lung CT involvement as independent predictors of severe COVID-19 conditions. Serum CRP levels and pO2 were also considerably higher patients with higher severity and can be used along with other factors to predict severe disease in COVID-19 patients.


Asunto(s)
Proteína C-Reactiva/metabolismo , /epidemiología , Factores de Edad , Anciano , Comorbilidad , Estudios Transversales , Femenino , Humanos , Pacientes Internos , Irán , Masculino , Persona de Mediana Edad , Índice de Severidad de la Enfermedad , Tomografía Computarizada por Rayos X
18.
Ther Adv Respir Dis ; 15: 17534666211009410, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1195908

RESUMEN

AIMS: A novel coronavirus SARS-CoV-2 has resulted in an ongoing global pandemic of Coronavirus disease 2019 (COVID-19). However, the outcomes of recovered patients have not been well defined. METHODS: This is a prospective observational follow-up study of survivors with COVID-19 from a designated tertiary center in Hefei, China. We examined chest computed tomography (CT) scanning, pulmonary function, 6-min walk distance (6MWD), and 36 item Short Form General Health Survey (SF-36). RESULTS: Among 81 enrolled patients, 62 (77%) patients and 61 (75%) patients, respectively, completed 1-month and 3-month follow-ups. Abnormal CT findings were still present in 73% of patients at 1 month and 54% at 3 months, whereas chest CT scan scores improved progressively at 1-month (5.0 ± 5.1) and 3-month follow up (3.0 ± 4.5) compared with that during hospitalization (11 ± 6.8). Mild restrictive pulmonary impairment was detected in 11% and 10% of patients at 1-month and 3-month follow up, respectively. The 6MWD was 523 ± 77 m in male patients and 484 ± 58 m in female patients, which was significantly lower than in healthy controls (606 ± 68 m, 568 ± 78 m, p < 0.001). SF-36 scores were significantly impaired in the domains of role physical (RP), role emotional (RE), and social functioning (SF) compared with the normal age-matched population. RP was improved at 3-month compared with 1-month follow up in the 41-64 years group (p < 0.01). Multivariable analysis showed that older age (over 40 years) and steroid administration during hospitalization were independently associated with worse chest CT scores at 3-month follow up. CONCLUSIONS: At 3 months, chest CT abnormalities were present in one half of COVID-19 survivors and worse chest CT scores were independently associated with older age and steroid administration during hospitalization. Residual pulmonary function impairments were modest, whereas exercise capacity and SF-36 scores were significantly lower than the general population. Support program and further follow-up evaluations may be needed.The reviews of this paper are available via the supplemental material section.


Asunto(s)
/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Factores de Edad , Femenino , Humanos , Pulmón/fisiopatología , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Radiografía Torácica , Factores de Tiempo , Velocidad al Caminar
19.
J Coll Physicians Surg Pak ; 30(4): 388-392, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: covidwho-1192065

RESUMEN

OBJECTIVE:   To determine a cut-off value of Chest CT severity score (CT-SS) in order to discriminate between the clinical types of COVID-19 pneumonia. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Radiology, Shifa International Hospital, from 1st March to June 30th, 2020. METHODOLOGY: One hundred and three consecutive patients' RT PCR positive for COVID-19 were included. Two consultant radiologists, with experience of 7 to 10 years in body imaging, evaluated their HRCT studies in consensus and calculated the CT severity score. A scoring of all 20 individual regions in each lung were assigned by the radiologists attributing a score of 0, 1 or 2 to each region, if parenchymal opacification was none, less than 50%, or 50% or more, respectively. The CT severity score was a summation of scores of all 20 regions of both lungs combined with a range of 0 to 40 points. The scores were compared for clinically mild and severe disease. RESULTS: Significant differences were noted regarding the scoring of lung opacity in mild and severe groups in each lung segment, p <0.05. The most significantly involved segments were right lower lobe's medial and lateral basal segment, left upper lobe's superior lingular segment and left lower lobe's medial basal and lateral basal segments. To discriminate mild versus severe disease, CT-SS threshold value turned out to be 19.5 Conclusion: CTSS may be of value for a prompt and objective means of assessing the degree of severity and disease burden in lungs. Key Words: COVID-19, COVID-19 diagnosis, Pneumonia, Novel coronavirus, CT severity score, Respiratory tract infection, Triage, Pandemic, RT-PCR, SARS-COV 2, Outbreak.


Asunto(s)
/diagnóstico por imagen , Índice de Severidad de la Enfermedad , Tomografía Computarizada por Rayos X , Humanos , Pulmón/diagnóstico por imagen , Pandemias , Estudios Retrospectivos
20.
IEEE Trans Neural Netw Learn Syst ; 32(5): 1810-1820, 2021 05.
Artículo en Inglés | MEDLINE | ID: covidwho-1191869

RESUMEN

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.


Asunto(s)
/diagnóstico por imagen , Aprendizaje Profundo , Redes Neurales de la Computación , Rayos X , /clasificación , Aprendizaje Profundo/clasificación , Diagnóstico Diferencial , Humanos , Neumonía Bacteriana/clasificación , Neumonía Bacteriana/diagnóstico por imagen , Neumonía Viral/clasificación , Neumonía Viral/diagnóstico por imagen , Tomografía Computarizada por Rayos X/clasificación
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