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2.
BMC Infect Dis ; 20(1): 821, 2020 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-33172398

RESUMO

BACKGROUND: Although Moraxella catarrhalis (M. catarrhalis) is a common cause of community-acquired pneumonia (CAP), studies investigating clinical manifestations of CAP due to M. catarrhalis (MC-CAP) in adults are limited. Since S. pneumoniae is the leading cause of CAP globally, it is important to distinguish between MC-CAP and CAP due to S. pneumoniae (SP-CAP) in clinical practice. However, no past study compared clinical characteristics of MC-CAP and SP-CAP by statistical analysis. We aimed to clarify the clinical characteristics of MC-CAP by comparing those of SP-CAP, as well as the utility of sputum Gram staining. METHODS: This retrospective study screened CAP patients aged over 20 years visiting or admitted to Okinawa Miyako Hospital between May 2013 and April 2018. Among these, we included patients whom either M. catarrhalis alone or S. pneumoniae alone was isolated from their sputum by bacterial cultures. RESULTS: We identified 134 MC-CAP and 130 SP-CAP patients. Although seasonality was not observed in SP-CAP, almost half of MC-CAP patients were admitted in the winter. Compared to those with SP-CAP, MC-CAP patients were older (p < 0.01) and more likely to have underlying pulmonary diseases such as asthma and bronchiectasis (p < 0.01). Approximately half of asthmatic MC-CAP and SP-CAP patients had asthma attacks. Although winter is an influenza season in Japan, co-infection with influenza virus was less common in MC-CAP compared to SP-CAP patients (3% vs. 15%, p < 0.01). Bronchopneumonia patterns on X-ray, as well as bronchial wall thickening, bilateral distribution, and segmental pattern on CT were more common in MC-CAP patients than in SP-CAP patients (p < 0.01). Sputum Gram stain was highly useful method for the diagnosis in both MC-CAP and SP-CAP (78.4% vs. 89.2%), and penicillins were most frequently chosen as an initial treatment for both pneumonias. CONCLUSIONS: This is the first study to show that MC-CAP occurred in older people compared to SP-CAP, influenza virus co-infection was less common in MC-CAP than SP-CAP, and that MC-CAP frequently caused asthma attacks. Gram stain contributed for the appropriate treatment, resulting in conserving broad-spectrum antibiotics such as cephalosporins and fluoroquinolones in both MC-CAP and SP-CAP patients.


Assuntos
Antibacterianos/uso terapêutico , Infecções Comunitárias Adquiridas/diagnóstico , Infecções Comunitárias Adquiridas/tratamento farmacológico , Moraxella catarrhalis/isolamento & purificação , Pneumonia/diagnóstico , Pneumonia/tratamento farmacológico , Streptococcus pneumoniae/isolamento & purificação , Adulto , Idoso , Idoso de 80 Anos ou mais , Infecções Comunitárias Adquiridas/microbiologia , Feminino , Violeta Genciana , Hospitalização , Humanos , Japão , Masculino , Pessoa de Meia-Idade , Fenazinas , Pneumonia/microbiologia , Estudos Retrospectivos , Escarro/microbiologia , Coloração e Rotulagem , Resultado do Tratamento , Adulto Jovem
3.
Sci Rep ; 10(1): 17532, 2020 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-33067538

RESUMO

This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.


Assuntos
Infecções por Coronavirus/diagnóstico , Pneumonia Viral/diagnóstico , Pneumonia/diagnóstico , Tórax/diagnóstico por imagem , Adulto , Idoso , Automação , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/virologia , Bases de Dados Factuais , Aprendizado Profundo , Diagnóstico por Computador , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/classificação , Pneumonia Viral/virologia
4.
Aging (Albany NY) ; 12(20): 19938-19944, 2020 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-33085645

RESUMO

COVID-19 shared many symptoms with seasonal flu, and community-acquired pneumonia (CAP) Since the responses to COVID-19 are dramatically different, this multicenter study aimed to develop and validate a multivariate model to accurately discriminate COVID-19 from influenza and CAP. Three independent cohorts from two hospitals (50 in discovery and internal validation sets, and 55 in the external validation cohorts) were included, and 12 variables such as symptoms, blood tests, first reverse transcription-polymerase chain reaction (RT-PCR) results, and chest CT images were collected. An integrated multi-feature model (RT-PCR, CT features, and blood lymphocyte percentage) established with random forest algorism showed the diagnostic accuracy of 92.0% (95% CI: 73.9 - 99.1) in the training set, and 96. 6% (95% CI: 79.6 - 99.9) in the internal validation cohort. The model also performed well in the external validation cohort with an area under the receiver operating characteristic curve of 0.93 (95% CI: 0.79 - 1.00), an F1 score of 0.80, and a Matthews correlation coefficient (MCC) of 0.76. In conclusion, the developed multivariate model based on machine learning techniques could be an efficient tool for COVID-19 screening in nonendemic regions with a high rate of influenza and CAP in the post-COVID-19 era.


Assuntos
Infecções por Coronavirus/diagnóstico , Modelos Estatísticos , Pneumonia Viral/diagnóstico , Adulto , Algoritmos , Técnicas de Laboratório Clínico , Diagnóstico Diferencial , Feminino , Humanos , Influenza Humana/diagnóstico , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/diagnóstico , Adulto Jovem
6.
Eur J Med Res ; 25(1): 49, 2020 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-33046116

RESUMO

BACKGROUND: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. METHODS: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. RESULTS: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. CONCLUSIONS: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.


Assuntos
Algoritmos , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Pneumonia Viral/epidemiologia , Pneumonia/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Comorbidade , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/epidemiologia , Pneumonia Viral/diagnóstico , Reprodutibilidade dos Testes , Estudos Retrospectivos
7.
Interdiscip Sci ; 12(4): 555-565, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32959234

RESUMO

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.


Assuntos
Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico , Aprendizado Profundo , Pulmão/diagnóstico por imagem , Modelos Biológicos , Redes Neurais de Computação , Pneumonia Viral/diagnóstico , Raios X , Algoritmos , Betacoronavirus , Coronavirus , Infecções por Coronavirus/complicações , Infecções por Coronavirus/diagnóstico por imagem , Infecções por Coronavirus/virologia , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Humanos , Aprendizado de Máquina , Pandemias , Pneumonia/diagnóstico , Pneumonia/diagnóstico por imagem , Pneumonia/etiologia , Pneumonia/virologia , Pneumonia Viral/complicações , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/virologia , Radiografia/métodos , Valores de Referência , Tomografia Computadorizada por Raios X/métodos
8.
PLoS One ; 15(9): e0239590, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32991632

RESUMO

We investigated the association between the results of a simplified cough test and pneumonia onset in 226 patients with acute stroke admitted to Suiseikai Kajikawa Hospital from April to December, 2018. For the simplified cough test, performed on admission, patients orally inhaled a mist of 1% citric acid-physiological saline using a portable mesh nebulizer. When the first cough was evoked or if it remained absent for 30 seconds (indicating an abnormal result), the test was ended. Patients also completed the repetitive saliva swallowing test (RSST) and modified water swallowing test. We monitored patients for pneumonia signs for 30 days post-admission. Eighteen patients exhibited an abnormal simplified cough test result. On multivariate analysis, an abnormal RSST result was independently associated with an abnormal simplified cough test result. Seventeen patients developed pneumonia. The adjusted Cox proportional hazard model for pneumonia onset revealed that the simplified cough test had predictive power for pneumonia onset (hazard ratio, 10.52; 95% confidence interval, 3.72-29.72). The simplified cough test is a strong indicator for predicting the pneumonia development in patients with acute stroke; it should be added to existing bedside screening tests for predicting pneumonia risk, allowing appropriate and timely intervention.


Assuntos
Tosse/diagnóstico , Pneumonia/diagnóstico , Acidente Vascular Cerebral/fisiopatologia , Idoso , Deglutição/fisiologia , Feminino , Humanos , Masculino , Admissão do Paciente
9.
Oncology (Williston Park) ; 34(9): 370-376, 2020 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-32965669

RESUMO

In an asymptomatic 77-yearold woman, former 55 packyears smoker, a routine X-ray showed a 45-mm superior left lobe lesion. A chest CT scan confirmed a 36-mm superior left lobe lesion and an aortic-pulmonary lymph node enlargement measuring 42 mm, suspicious for neoplasia. A PET-CT scan showed an elevated uptake in the primary lesion, in the aortic-pulmonary lymph node, and in the left hilar lymph node with a standardized uptake value - 40 and 4.3, respectively. CT-guided lung biopsy showed a lung squamous cell carcinoma. An endobronchial ultrasound-guided transbronchial needle aspiration for lymph-node staging was negative for lymph node spread. Brain MRI was negative. Final staging was determined to be a IIIA (T2bN2) squamous cell carcinoma of the lung.


Assuntos
Anticorpos Monoclonais/uso terapêutico , Antineoplásicos Imunológicos/uso terapêutico , Carcinoma de Células Escamosas/terapia , Infecções por Coronavirus/diagnóstico , Neoplasias Pulmonares/terapia , Pneumonia Viral/diagnóstico , Pneumonia/diagnóstico , Idoso , Anticorpos Monoclonais/efeitos adversos , Antineoplásicos Imunológicos/efeitos adversos , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Betacoronavirus , Carboplatina/administração & dosagem , Carcinoma de Células Escamosas/diagnóstico por imagem , Quimiorradioterapia , Quimioterapia de Consolidação , Diagnóstico Diferencial , Feminino , Humanos , Pulmão/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico por imagem , Paclitaxel/administração & dosagem , Pandemias , Pneumonia/induzido quimicamente
11.
Wilderness Environ Med ; 31(3): 324-326, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32739040

RESUMO

Exposure to and consumption of brackish water are associated with an elevated risk of infection, hypernatremia, and hypothermia. Minimal data exist to support the diagnosis and treatment of patients with long-term brackish water exposure. We present a case of a patient who spent 5 to 10 d semisubmerged in the Elizabeth River in coastal Virginia. A 55-y-old male presented via ambulance after 5 to 10 d of being "stuck in the mud." He was hypernatremic, with a sodium of 176 mEq·L-1, hypothermic to 34.5°C (94.1°F), and hypotensive at 88/50 mm Hg, with a sodium concentration of 176 mEq·L-1 and an osmolality of 412 mosm·kg-1. He developed pneumonia, with respiratory cultures growing Vibrio parahemolyticus, Klebsiella oxytoca, and Shewanella algae. He had pustules, which grew Aeromonas hydrophilia and Aeromonas caviae. A nasogastric tube was placed. Using suction, 500 mL of coarse sand and gravel was removed from his stomach. Antibiotics and intravenous fluids were given. The patient fully recovered after 3 wk and was discharged to rehabilitation. Exposure to brackish water can present a unique set of infectious and metabolic complications. Initial care should include treatment of metabolic derangements, such as hypovolemia, hypernatremia, and hypothermia, and treatment of infections with antibiotics based on knowledge of the most likely causative organisms.


Assuntos
Furunculose/diagnóstico , Imersão/efeitos adversos , Intubação Gastrointestinal , Pneumonia/diagnóstico , Águas Salinas/efeitos adversos , Furunculose/microbiologia , Humanos , Hipernatremia/etiologia , Imersão/fisiopatologia , Masculino , Pessoa de Meia-Idade , Pneumonia/microbiologia , Areia , Resultado do Tratamento , Virginia
12.
Euro Surveill ; 25(30)2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32734857

RESUMO

We report a case of Legionella pneumonia in a dishwasher of a restaurant in Rome, Italy, just after the end of the lockdown that was in place to control the SARS-CoV-2 epidemic. The case highlights the importance of strict monitoring of water and air systems immediately before reopening business or public sector buildings, and the need to consider Legionella infections among the differential diagnosis of respiratory infections after lockdown due to the ongoing COVID-19 pandemic.


Assuntos
Antígenos de Bactérias/urina , Legionella pneumophila/isolamento & purificação , Legionella/isolamento & purificação , Doença dos Legionários/diagnóstico , Levofloxacino/uso terapêutico , Pneumonia/diagnóstico , Administração Intravenosa , Adulto , Anti-Infecciosos Urinários/uso terapêutico , Tosse/etiologia , Febre/etiologia , Cefaleia/etiologia , Humanos , Doença dos Legionários/tratamento farmacológico , Doença dos Legionários/urina , Masculino , Pneumonia/tratamento farmacológico , Pneumonia/urina , Resultado do Tratamento
13.
Pneumologie ; 74(8): 515-544, 2020 Aug.
Artigo em Alemão | MEDLINE | ID: mdl-32823360

RESUMO

The present guideline aims to improve the evidence-based management of children and adolescents with pediatric community-acquired pneumonia (pCAP). Despite a prevalence of approx. 300 cases per 100 000 children per year in Central Europe, mortality is very low. Prevention includes infection control measures and comprehensive immunization. The diagnosis can and should be established clinically by history, physical examination and pulse oximetry, with fever and tachypnea as cardinal features. Additional signs or symptoms such as severely compromised general condition, poor feeding, dehydration, altered consciousness or seizures discriminate subjects with severe pCAP from those with non-severe pCAP. Within an age-dependent spectrum of infectious agents, bacterial etiology cannot be reliably differentiated from viral or mixed infections by currently available biomarkers. Most children and adolescents with non-severe pCAP and oxygen saturation > 92 % can be managed as outpatients without laboratory/microbiology workup or imaging. Anti-infective agents are not generally indicated and can be safely withheld especially in children of young age, with wheeze or other indices suggesting a viral origin. For calculated antibiotic therapy, aminopenicillins are the preferred drug class with comparable efficacy of oral (amoxicillin) and intravenous administration (ampicillin). Follow-up evaluation after 48 - 72 hours is mandatory for the assessment of clinical course, treatment success and potential complications such as parapneumonic pleural effusion or empyema, which may necessitate alternative or add-on therapy.


Assuntos
Antibacterianos/uso terapêutico , Infecções Comunitárias Adquiridas/tratamento farmacológico , Pneumonia/tratamento farmacológico , Guias de Prática Clínica como Assunto , Pneumologia/normas , Adolescente , Antibacterianos/administração & dosagem , Criança , Infecções Comunitárias Adquiridas/diagnóstico , Infecções Comunitárias Adquiridas/virologia , Europa (Continente) , Alemanha , Humanos , Lactente , Pneumonia/diagnóstico , Pneumonia/virologia , Sociedades Médicas
14.
Eur Rev Med Pharmacol Sci ; 24(14): 7796-7800, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32744706

RESUMO

The 2019 Novel Coronavirus disease (COVID-19) broke out in Wuhan, China in December 2019 and spread throughout the world. Early screening and early diagnosis play key roles in prevention and management of the epidemic. Attention should also be paid to the infection of health workers and shortage of medical resources in high-risk areas. Here, we report two cases of patients diagnosed with COVID-19 and evaluated by robotic ultrasound based on 5G-powered technology 700 km east of Wuhan. We here show the advantages of this kind of remote ultrasound scan, which could become a method for the diagnosis and assessment of COVID-19.


Assuntos
Infecções por Coronavirus/patologia , Pneumonia Viral/patologia , Robótica , Ultrassonografia/métodos , Adulto , Betacoronavirus/genética , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/complicações , Infecções por Coronavirus/virologia , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia/diagnóstico , Pneumonia/etiologia , Pneumonia Viral/complicações , Pneumonia Viral/virologia , RNA Viral/metabolismo , Tecnologia de Sensoriamento Remoto
15.
Biomed Eng Online ; 19(1): 66, 2020 Aug 19.
Artigo em Inglês | MEDLINE | ID: mdl-32814568

RESUMO

BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. RESULTS AND CONCLUSIONS: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.


Assuntos
Infecções por Coronavirus/complicações , Processamento de Imagem Assistida por Computador , Aprendizado de Máquina , Pneumonia Viral/complicações , Pneumonia/complicações , Pneumonia/diagnóstico , Feminino , Humanos , Masculino , Pandemias , Tomografia Computadorizada por Raios X
16.
Am J Case Rep ; 21: e927586, 2020 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-32840240

RESUMO

BACKGROUND Rifampicin-induced pneumonitis is an infrequent occurrence, with only a few cases reported in the literature. Furthermore, this condition constitutes a diagnostic challenge, particularly in the era of COVID-19 infection. Here, we report a case of rifampicin-induced pneumonitis with clinical, imaging, and histological features of acute respiratory distress syndrome (ARDS), which required severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing to exclude a diagnosis of coronavirus disease 2019 (COVID-19) pneumonia. CASE REPORT A 43-year-old man on anti-TB treatment for TB meningitis developed new-onset fever, fatigue, hypoxemic respiratory failure, and bilateral pulmonary opacities. His clinical, chest X-ray, and CT thorax findings of ARDS were similar to both rifampicin-induced pneumonitis and severe COVID-19 pneumonia. However, reverse transcription polymerase chain reaction (RT-PCR) testing from a nasopharyngeal swab and bronchoalveolar lavage (BAL) via the GeneXpert system was negative for SARS-CoV-2. A detailed workup, including lung biopsy, revealed drug-induced pneumonitis as the cause of his presentation. His pneumonitis improved after discontinuation of rifampicin and recurred following the rifampicin challenge. CONCLUSIONS This case highlights the importance of early, rapid, and accurate testing for SARS-CoV-2 during the COVID-19 pandemic for patients presenting with acute respiratory symptoms, so that accurate diagnosis and early patient management are not delayed for patients with treatable causes of acute and severe lung diseases. Timely identification of rifampicin-induced pneumonitis via a high clinical suspicion, detailed workup, and histopathological analysis is required to avoid permanent damage to the lungs.


Assuntos
Infecções por Coronavirus/complicações , Pneumonia Viral/complicações , Pneumonia/induzido quimicamente , Rifampina/efeitos adversos , Tomografia Computadorizada por Raios X/métodos , Tuberculose Meníngea/tratamento farmacológico , Adulto , Antibióticos Antituberculose/efeitos adversos , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Humanos , Masculino , Pandemias , Pneumonia/diagnóstico , Pneumonia Viral/epidemiologia , Tuberculose Meníngea/complicações
17.
Eur Radiol ; 30(12): 6828-6837, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32683550

RESUMO

OBJECTIVE: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. METHODS: In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen's kappa, respectively. RESULTS: The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. CONCLUSIONS: A deep learning-based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. KEY POINTS: • A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).


Assuntos
Betacoronavirus , Infecções Comunitárias Adquiridas/diagnóstico , Infecções por Coronavirus/diagnóstico , Aprendizado Profundo , Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico , Pneumonia/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Inteligência Artificial , China/epidemiologia , Progressão da Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Curva ROC , Estudos Retrospectivos
18.
J Vet Diagn Invest ; 32(4): 621-625, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32687009

RESUMO

A 22-y-old American Quarter Horse gelding was presented with a history of chronic progressive respiratory problems and a diffuse pulmonary nodular pattern in thoracic radiographs. The horse was euthanized, and 4 formalin-fixed samples of lung were submitted for histopathology. There were multifocal areas of marked thickening of alveolar septa as a result of proliferation of myofibroblasts embedded in fibromyxoid matrix (interpreted as "Masson bodies"), focal areas of fibrosis, and numerous papillary projections of connective tissue into bronchioles. A diagnosis of organizing pneumonia was reached. No etiology was found for this lesion. It is important to consider causes of chronic interstitial pneumonia with fibrosis in horses other than equid herpesvirus 5, such as complicated viral or bacterial pneumonia or chronic toxicoses.


Assuntos
Doenças dos Cavalos/diagnóstico , Doenças Pulmonares Intersticiais/veterinária , Pneumonia/veterinária , Animais , Evolução Fatal , Doenças dos Cavalos/etiologia , Doenças dos Cavalos/patologia , Cavalos , Pulmão/patologia , Doenças Pulmonares Intersticiais/diagnóstico , Doenças Pulmonares Intersticiais/etiologia , Doenças Pulmonares Intersticiais/patologia , Masculino , Pneumonia/diagnóstico , Pneumonia/etiologia , Pneumonia/patologia
19.
PLoS One ; 15(7): e0235207, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32629459

RESUMO

BACKGROUND AND AIMS: The effects of physician specialty on the outcome of heart disease remains incompletely understood because of inconsistent findings from some previous studies. Our purpose is to compare the admission outcomes of heart disease in patients receiving care by cardiologists and noncardiologist (NC) physicians. METHODS: Using reimbursement claims data of Taiwan's National Health Insurance from 2008-2013, we conducted a matched study of 6264 patients aged ≥20 years who received a cardiologist's care during admission for heart disease. Using a propensity score matching procedure adjusted for sociodemographic characteristics, medical condition, and type of heart disease, 6264 controls who received an NC physician's care were selected. Logistic regressions were used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) for complications and mortality during admission for heart disease associated with a cardiologist's care. RESULTS: Patients who received a cardiologist's care had a lower risk of pneumonia (OR = 0.61; 95% CI, 0.53-0.70), septicemia (OR = 0.49; 95% CI, 0.39-0.61), urinary tract infection (OR = 0.76; 95% CI, 0.66-0.88), and in-hospital mortality (OR = 0.37; 95% CI, 0.29-0.47) than did patients who received an NC physician's care. The association between a cardiologist's care and reduced adverse events following admission was significant in both sexes and in patients aged ≥40 years. CONCLUSION: We raised the possibility that cardiologist care was associated with reduced infectious complications and mortality among patients who were admitted due to heart disease.


Assuntos
Cardiologistas , Clínicos Gerais , Cardiopatias/diagnóstico , Mortalidade Hospitalar/tendências , Pneumonia/diagnóstico , Sepse/diagnóstico , Infecções Urinárias/diagnóstico , Adulto , Idoso , Feminino , Cardiopatias/complicações , Cardiopatias/mortalidade , Cardiopatias/fisiopatologia , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Razão de Chances , Admissão do Paciente/estatística & dados numéricos , Pneumonia/complicações , Pneumonia/mortalidade , Pneumonia/fisiopatologia , Pontuação de Propensão , Fatores de Risco , Sepse/complicações , Sepse/mortalidade , Sepse/fisiopatologia , Taiwan/epidemiologia , Infecções Urinárias/complicações , Infecções Urinárias/mortalidade , Infecções Urinárias/fisiopatologia
20.
Zhonghua Jie He He Hu Xi Za Zhi ; 43(8): 639-647, 2020 Aug 12.
Artigo em Chinês | MEDLINE | ID: mdl-32727174

RESUMO

Patients with diabetes mellitus are prone to various infections owing to host immune impairment, especially pneumonia. In China, morbidity of pneumonia in patients with diabetes mellitus is high. And in case of pneumonia, patients with diabetes mellitus were of increased probability of opportunistic infections, low detection rate of pathogens, more severe pneumonia and high mortality, resulting in heavy medical and economic burdens. Therefore, it is urgent for multi-disciplinary experts to explore and formulate standards for diagnosis and treatment of pneumonia in patients with diabetes mellitus to improve the prognosis. Based on domestic and international literatures and clinical experiences of experts in respiratory and critical care medicine, endocrinology, microbiology and clinical pharmacy, a consensus was developed on the diagnosis and treatment of pneumonia in patients with diabetes mellitus.


Assuntos
Complicações do Diabetes , Diabetes Mellitus , Pneumonia/diagnóstico , Pneumonia/terapia , China , Consenso , Humanos , Infecções Oportunistas/microbiologia , Pneumonia/microbiologia , Prognóstico
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