Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
1.
Int J Infect Dis ; 122: 622-627, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1926531

ABSTRACT

OBJECTIVES: Here, we retrospectively described the diagnosis and treatment of 32 cases diagnosed with Chlamydia psittaci pneumonia during the COVID-19 pandemic. METHODS: Clinical information was collected from all the patients. Reverse transcription-PCR and ELISAs were conducted for the detection of COVID-19 using nasal swabs and bronchoalveolar lavage fluid (BALF) samples. Metagenomic next-generation sequencing (mNGS) was performed for the identification of causative pathogens using BALF, peripheral blood and sputum samples. End-point PCR was performed to confirm the mNGS results. RESULTS: All 32 patients showed atypical pneumonia and had infection-like symptoms that were similar to COVID-19. Results of reverse transcription-PCR and ELISAs ruled out COVID-19 infection. mNGS identified C. psittaci as the suspected pathogen in these patients within 48 hours, which was validated by PCR, except for three blood samples. The sequence reads that covered fragments of C. psittaci genome were detected more often in BALF than in sputum or blood samples. All patients received doxycycline-based treatment regimens and showed favorable outcomes. CONCLUSION: This retrospective study, with the highest number of C. psittaci pneumonia enrolled cases in China so far, suggests that human psittacosis may be underdiagnosed and misdiagnosed clinically, especially in the midst of the COVID-19 pandemic.


Subject(s)
COVID-19 , Chlamydophila psittaci , Influenza, Human , Mycoses , Pneumonia, Mycoplasma , Pneumonia , Psittacosis , COVID-19/diagnosis , Chlamydophila psittaci/genetics , Humans , Pandemics , Psittacosis/diagnosis , Psittacosis/drug therapy , Psittacosis/epidemiology , Retrospective Studies
2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309031

ABSTRACT

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from Jan 17 to Feb 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920 (95% confidence interval : 0.902-0.938;AUROC=0.915, and its standard deviation of 0.028, as evaluated in 5-fold cross-validation). At a value of whether the predicted score >4.0, the model could detect NCP with a specificity of 98.3%;at a cut-off value of < -0.5, the model could rule out NCP with a sensitivity of 97.9%. The study demonstrated that the rapid screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

3.
Medicine (Baltimore) ; 100(24): e26279, 2021 Jun 18.
Article in English | MEDLINE | ID: covidwho-1269620

ABSTRACT

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/diagnosis , Mass Screening/methods , SARS-CoV-2/isolation & purification , Adult , China , Female , Humans , Male , Middle Aged , Prospective Studies , ROC Curve , Sensitivity and Specificity
4.
J Digit Imaging ; 34(2): 231-241, 2021 04.
Article in English | MEDLINE | ID: covidwho-1103473

ABSTRACT

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.


Subject(s)
COVID-19 , Deep Learning , Pneumonia, Viral , Humans , Retrospective Studies , SARS-CoV-2
5.
Sci Rep ; 11(1): 3863, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087494

ABSTRACT

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Mass Screening , Models, Biological , Pneumonia/diagnosis , SARS-CoV-2/physiology , Adult , China/epidemiology , Female , Humans , Male , Middle Aged , ROC Curve
6.
J Magn Reson Imaging ; 54(2): 421-428, 2021 08.
Article in English | MEDLINE | ID: covidwho-1085671

ABSTRACT

BACKGROUND: Myocardial injury has been found using magnetic resonance imaging in recovered coronavirus disease 2019 (COVID-19) patients unselected or with ongoing cardiac symptoms. PURPOSE: To evaluate for the presence of myocardial involvement in recovered COVID-19 patients without cardiovascular symptoms and abnormal serologic markers during hospitalization. STUDY TYPE: Prospective. POPULATION: Twenty-one recovered COVID-19 patients and 20 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T, cine, T2-weighted imaging, T1 mapping, and T2 mapping. ASSESSMENT: Cardiac ventricular function includes end-diastolic volume, end-systolic volume, stroke volume, cardiac output, left ventricle (LV) mass, and ejection fraction (EF) of LV and right ventricle (RV), and segmental myocardial T1 and T2 values were measured. STATISTICAL TESTS: Student's t-test, univariate general linear model test, and chi-square test were used for analyses between two groups. Ordinary one-way analyses of variance or Kruskal-Wallis H test were used for analyses between three groups, followed by post-hoc analyses. RESULTS: Fifteen (71.43%) COVID-19 patients had abnormal magnetic resonance findings, including raised myocardial native T1 (5, 23.81%) and T2 values (10, 47.62%), decreased LVEF (1, 4.76%), and RVEF (2, 9.52%). The segmental myocardial T2 value of COVID-19 patients (49.20 [46.1, 54.6] msec) was significantly higher than HC (48.3 [45.2, 51.7] msec) (P < 0.001), while the myocardial native T1 value showed no significant difference between COVID-19 patients and HC. The myocardial T2 value of serious COVID-19 patients (52.5 [48.1, 57.1] msec) was significantly higher than unserious COVID-19 patients (48.8 [45.9, 53.8] msec) and HC (48.3 [45.2, 51.7]) (P < 0.001). COVID-19 patients with abnormally elevated D-dimer, C-reactive protein, or lymphopenia showed higher myocardial T2 values than without (all P < 0.05). DATA CONCLUSION: Cardiac involvement was observed in recovered COVID-19 patients with no preexisting cardiovascular disease, no cardiovascular symptoms, and elevated serologic markers of myocardial injury during the whole course of COVID-19. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.


Subject(s)
COVID-19 , Heart , Humans , Magnetic Resonance Imaging, Cine , Myocardium , Predictive Value of Tests , Prospective Studies , SARS-CoV-2 , Stroke Volume , Ventricular Function, Left
SELECTION OF CITATIONS
SEARCH DETAIL