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1.
J Am Coll Radiol ; 17(7): e29-e36, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-1796598

ABSTRACT

OBJETIVO: Describir las estrategias, manejo de emergencias y los procedimientos de control de infecciones de nuestro departamento durante el brote de la enfermedad por coronavirus 2019 (COVID-19). MéTODOS: Creamos un equipo de manejo de emergencias. El equipo estableció varias medidas: Reconfiguración del flujo de trabajo en el departamento de radiología, distribución de material de protección personal y adiestramiento del personal, procedimientos para la obtención de imágenes en pacientes sospechosos o confirmados con COVID-19, así como para pacientes sin historial de exposición o síntomas. Aquellos con sospecha o confirmación de COVID-19 fueron escaneados en una unidad dedicada para ello. RESULTADOS: Del 21 de enero del 2020 hasta el 9 de marzo del 2020, 3,083 personas con sospecha o confirmación de COVID-19 recibieron CT de torax. Incluyendo los exámenes iniciales y repetidos, el número total de CT fue 3,340. Como resultado de nuestras medidas de precaución, ninguno de los miembros del personal del departamento de radiología fue infectado con COVID-19. CONCLUSIóN: Las estrategias de planificación y las protecciones adecuadas pueden ayudar a proteger a los pacientes y al personal contra una enfermedad altamente infecciosa. Y a la misma vez ayudar a mantener la capacidad de atender un volumen alto de pacientes.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323626

ABSTRACT

Background: Coronavirus disease 2019(COVID-19) is a worldwide pandemic.In this study, we aimed to evaluate the risk factors of death from severe and critical COVID-19 patients. Method: A retrospective study of patients diagnosed with severe and critical COVID-19 from four hospitals in Wuhan, China, describing the clinical characteristics and laboratory results, and using Cox regression to study the risk factors was conducted. Results: Four hundred and forty-six patients with COVID-19 showed a high case fatality rate(CFR)(20.2%). All patients required oxygen therapy, and 52(12%) patients required invasive mechanical ventilation,of which 50(96%) patients died.The univariate Cox proportional hazard model showed a white blood cell count of more than 10 × 10⁹/L(HR3.903,95%CI 2.413 to 6.313),patients’ risk of death significantly increased.The multivariate Cox proportional hazard model demonstrated that older age (HR 1.074, 95% CI 1.050 to 1.098) was an independent risk factor and high white blood cell count(HR 1.119, 95% CI 1.056 to 1.186)was a predictive factor for COVID-19 on admission. Conclusions: COVID-19 is a new disease entity that carries significant risk of morbidity and CFR.Older age was an independent risk factor and high white blood cell was a predictive factor for COVID-19.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-320695

ABSTRACT

Background: Since 2020 COVID-19 pandemic became an emergent public sanitary incident. The epidemiology data and the impact on prognosis of secondary infection in severe and critical COVID-19 patients in China remained largely unclear. Methods: . We retrospectively reviewed medical records of all adult patients with laboratory-confirmed COVID-19 who were admitted to ICUs from January 18 th 2020 to April 26 th 2020 at two hospitals in Wuhan, China and one hospital in Guangzhou, China. We measured the frequency of bacteria and fungi cultured from respiratory tract, blood and other body fluid specimens. The risk factors for and impact of secondary infection on clinical outcomes were also assessed. Results: . Secondary infections were very common (86.6%) when patients were admitted to ICU for >72 hours. The majority of infections were respiratory, with the most common organisms being Klebsiella pneumoniae (24.5%), Acinetobacter baumannii (21.8%), Stenotrophomonas maltophilia (9.9%), Candida albicans (6.8%), and Pseudomonas spp. (4.8%). Furthermore, the proportions of multidrug resistant (MDR) bacteria and carbapenem resistant Enterobacteriaceae (CRE) were high. We also found that age ≥60 years and mechanical ventilation ≥13days independently increased the likelihood of secondary infection. Finally, patients with positive cultures had reduced ventilator free days in 28 days and patients with CRE and/or MDR bacteria positivity showed lower 28 day survival rate. Conclusions: . In a retrospective cohort of severe and critical COVID-19 patients admitted to ICUs in China, the prevalence of secondary infection was high, especially with CRE and MDR bacteria, resulting in poor clinical outcomes.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-318469

ABSTRACT

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315786

ABSTRACT

Objectives: Since December 2019, a outbreak of Corona Virus Disease-2019(COVID-19) started in Wuhan, China. Now we comprehended much more about the troublesome disease from studies than the beginning. But more details between admission laboratory test and prognosis of COVID-19 were still confusing. So we focused on the admission biochemical test, and tried to verify their influence to the prognosis of COVID-19. Method: 522 patients from 4 hospitals were enrolled in this retrospective cohort study. We collected demographic information, comorbidities and laboratory biochemical indicators, then compared them between survivors’ and nonsurvivors’ group. Logistic regression methods were used to explore the risk factors associated with in-hospital death. Linear regression and receiver operating characteristic curve(ROC-curve) was applied to assess the efficiency of risk factors and regression model. Results: Age of nonsurvivors’ group(68.9) was older than survivors group(50.0). Diabetes(68.7%) was the most common comorbidity in the nonsurvivors’ group. In univariate regression analysis, most biochemical tests were related to the mortality except lipid metabolic results. Age, fasting blood glucose and blood urea nitrogen(BUN) were with a p-value less than 0.001 in multivariate regression model. Conclusion: Age, BUN and fasting blood glucose were risk factors associated with the prognosis of COVID-19 related pneumonia.Authors Qi Long, Chen-liang Zhou, Ye-ming Wang, Bin Song, Xiao-bin Cheng, Qiu-fen Dong, and Liu-lin Wang contributed equally to this work.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311589

ABSTRACT

Background: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. Methods: : A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on registering CT images within the lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. As a result, we compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across different course of the disease. Results: : For the performance of infection segmentation, comparing the segmentation results with manually annotated ground truth, the average Dice is 91.6%±10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1%±3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four distinct patterns (progression, absorption, enlargement, and further absorption) with remarkable concurrent HU patterns for GGO and consolidations. Conclusions: : By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four distinct disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325431

ABSTRACT

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

8.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325247

ABSTRACT

Objectives: To investigate computed tomography (CT) and clinical features could help differentiate coronavirus disease 2019 (COVID-19) from seasonal influenza pneumonia. Methods: : We retrospectively evaluated the clinical features and chest CT findings of Chinese patients with COVID-19 and seasonal influenza pneumonia treated during the same period. Results: : The 24 patients with COVID-19 (mean age, 41 years;13 men) and 79 patients with seasonal influenza pneumonia (mean age, 41 years;50 men) differed significantly in mean temperature, respiratory rate, and systolic blood pressure;in central-peripheral, superior-inferior, and anterior-posterior distribution but not lateral distribution of pulmonary lesions;and patchy ground-glass opacity (GGO), GGO nodules, vascular enlargement in GGO, air bronchogram, bronchiolectasis in GGO or consolidation, interlobular septal thickening, and crazy-paving pattern. Separate regression models were developed with clinical features, CT features (including anatomical distributions), and a combined model informed by the first two. The combined model had the best diagnostic performance for identifying COVID-19: a cut-off value of 0.38 was 74% sensitive and 100% specific and had an area under the receiver operating characteristics curve of 0.94. This model was based on sputum production, vascular enlargement in GGO, and central-peripheral distribution (random vs subpleural). Conclusions: : The combination of sputum production, vascular enlargement in GGO, and central-peripheral distribution should be extremely helpful in the differential diagnosis of COVID-19.

9.
Science ; 375(6584): 1048-1053, 2022 03 04.
Article in English | MEDLINE | ID: covidwho-1673339

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has become the dominant infective strain. We report the structures of the Omicron spike trimer on its own and in complex with angiotensin-converting enzyme 2 (ACE2) or an anti-Omicron antibody. Most Omicron mutations are located on the surface of the spike protein and change binding epitopes to many current antibodies. In the ACE2-binding site, compensating mutations strengthen receptor binding domain (RBD) binding to ACE2. Both the RBD and the apo form of the Omicron spike trimer are thermodynamically unstable. An unusual RBD-RBD interaction in the ACE2-spike complex supports the open conformation and further reinforces ACE2 binding to the spike trimer. A broad-spectrum therapeutic antibody, JMB2002, which has completed a phase 1 clinical trial, maintains neutralizing activity against Omicron. JMB2002 binds to RBD differently from other characterized antibodies and inhibits ACE2 binding.


Subject(s)
Angiotensin-Converting Enzyme 2/chemistry , Antibodies, Neutralizing/chemistry , Antibodies, Viral/chemistry , SARS-CoV-2/chemistry , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Antibodies, Neutralizing/immunology , Antibodies, Neutralizing/metabolism , Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/immunology , Antibodies, Viral/metabolism , Binding Sites , Cryoelectron Microscopy , Epitopes , Humans , Immunoglobulin Fab Fragments/chemistry , Immunoglobulin Fab Fragments/immunology , Immunoglobulin Fab Fragments/metabolism , Models, Molecular , Mutation , Protein Binding , Protein Conformation , Protein Domains , Protein Interaction Domains and Motifs , Protein Multimerization , Protein Subunits/chemistry , SARS-CoV-2/genetics , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/immunology , Spike Glycoprotein, Coronavirus/metabolism , Thermodynamics
10.
Sci Rep ; 11(1): 17791, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1397897

ABSTRACT

The purpose of this study is to explore whether uric acid (UA) can independently act as a prognostic factor and critical marker of the 2019 novel corona virus disease (COVID-19). A multicenter, retrospective, and observational study including 540 patients with confirmed COVID-19 was carried out at four designated hospitals in Wuhan. Demographic, clinical, laboratory data were collected and analyzed. The primary end point was in-hospital death of patients with COVID-19. The concentration of admission UA (adUA) and the lowest concentration of uric acid during hospitalization (lowUA) in the dead patients were significantly lower than those in the survivors. Multivariate logistic regression analysis showed the concentration of lowUA (OR 0.986, 95% CI 0.980-0.992, p < 0.001) was able to independently predict the risk of in-hospital death. The mean survival time in the low-level group of lowUA was significantly lower than other groups. When lowUA was ≤ 166 µmol/L, the sensitivity and specificity in predicting hospital short-term mortality were 76.9%, (95% CI 68.5-85.1%) and 74.9% (95% CI 70.3-78.9%). This retrospective study determined that the lowest concentration of UA during hospitalization can be used as a prognostic indicator and a marker of disease severity in severe patients with COVID-19.


Subject(s)
COVID-19/mortality , Uric Acid/blood , Adult , Aged , Biomarkers/blood , COVID-19/blood , COVID-19/diagnosis , China/epidemiology , Feasibility Studies , Female , Hospital Mortality , Humans , Kaplan-Meier Estimate , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment/methods , Risk Factors , Sensitivity and Specificity , Severity of Illness Index
11.
Front Med (Lausanne) ; 8: 663646, 2021.
Article in English | MEDLINE | ID: covidwho-1394776

ABSTRACT

Background: Pancreatic enzyme elevation has been reported in patients with COVID-19 during the pandemic. However, with the shortage of medical resources and information, several challenges are faced in the examination and treatment of this condition in COVID-19 patients. There is little information on whether such condition is caused by pancreatic injury, and if this is a warning sign of life threatening complications like multiple organ failure in patients. The objective of this study is to explore the relationship between elevated pancreatic enzymes and the underlying risk factors during the management of COVID-19 patients. Method: A total of 55 COVID-19 patients admitted to the intensive care unit (ICU) of Wuhan Jinyintan hospital from January 1 to March 30, 2020 were enrolled in this study. All participants underwent transabdominal ultrasound imaging to assess their pancreas. Results: Out of the 55 patients, three patients had pancreatitis, 29 (52.7%) with elevated pancreatic enzymes, and 23 (41.8%) without. The most common symptoms of patients with COVID-19 were fever and cough. There was no statistical difference in most baseline characteristics except myalgia on admission. Compared with those having normal enzyme levels, patients with elevated pancreatic enzymes had higher rates of mortality (79.3 vs. 52.2%; P = 0.038), and lower rates of discharge (20.7 vs. 47.8%; P = 0.038). Patients with elevated enzymes had higher incidence of mechanical ventilation (P = 0.004) and kidney injury (P = 0.042) than patients without elevated pancreatic enzymes. The results of multivariable logistic analysis showed that the odds ratio were 10.202 (P = 0.002) for mechanical ventilation and 7.673 (P = 0.014) for kidney injury with the elevated enzymes vs. the normal conditions. Conclusions: The findings show that the incidences of pancreatic enzymes elevation are not low in critical COVID-19 patients and only a few of them progressed to acute pancreatitis (AP). Increased pancreatic enzymes levels is associated with poor prognosis in COVID-19 patients. In addition, the kidney injury and oxygenation degradation are associated with the pancreatic enzymes elevation in COVID-19 patients.

12.
Ann Palliat Med ; 10(8): 8557-8570, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1353025

ABSTRACT

BACKGROUND: Since 2020 COVID-19 pandemic became an emergent public sanitary incident. The epidemiology data and the impact on prognosis of secondary infection in severe and critical COVID-19 patients in China remained largely unclear. METHODS: We retrospectively reviewed medical records of all adult patients with laboratory-confirmed COVID-19 who were admitted to ICUs from January 18th 2020 to April 26th 2020 at two hospitals in Wuhan, China and one hospital in Guangzhou, China. We measured the frequency of bacteria and fungi cultured from respiratory tract, blood and other body fluid specimens. The risk factors for and impact of secondary infection on clinical outcomes were also assessed. RESULTS: Secondary infections were very common (86.6%) when patients were admitted to ICU for >72 hours. The majority of infections were respiratory, with the most common organisms being Klebsiella pneumoniae (24.5%), Acinetobacter baumannii (21.8%), Stenotrophomonas maltophilia (9.9%), Candida albicans (6.8%), and Pseudomonas spp. (4.8%). Furthermore, the proportions of multidrug resistant (MDR) bacteria and carbapenem resistant Enterobacteriaceae (CRE) were high. We also found that age ≥60 years and mechanical ventilation ≥13 days independently increased the likelihood of secondary infection. Finally, patients with positive cultures had reduced ventilator free days in 28 days and patients with CRE and/or MDR bacteria positivity showed lower 28-day survival rate. CONCLUSIONS: In a retrospective cohort of severe and critical COVID-19 patients admitted to ICUs in China, the prevalence of secondary infection was high, especially with CRE and MDR bacteria, resulting in poor clinical outcomes.


Subject(s)
COVID-19 , Coinfection , Cross Infection , Adult , Anti-Bacterial Agents/therapeutic use , Coinfection/drug therapy , Cross Infection/drug therapy , Cross Infection/epidemiology , Humans , Middle Aged , Pandemics , Retrospective Studies , SARS-CoV-2
13.
BMC Infect Dis ; 21(1): 371, 2021 Apr 20.
Article in English | MEDLINE | ID: covidwho-1195913

ABSTRACT

BACKGROUND: The current coronavirus disease 2019 (COVID-19) is a public health emergency. In this study, we aimed to evaluate the risk factors for mortality in severe and critical COVID-19 patients. METHODS: We performed a retrospective study of patients diagnosed with severe and critical COVID-19 from four hospitals in Wuhan, China, by evaluating the clinical characteristics and laboratory results, and using Cox proportional hazards model to assess the risk factors involved in disease progression. RESULTS: In total, 446 patients with COVID-19 were enrolled. The study indicated a high mortality rate (20.2%) in severe and critical COVID-19 patients. At the time of admission, all patients required oxygen therapy, and 52 (12%) required invasive mechanical ventilation, of which 50 (96%) died. The univariate Cox proportional hazards model showed a white blood cell count of more than 10 × 109/L (HR 3.993,95%CI 2.469 to 6.459) that correlated with an increased mortality rate. The multivariable Cox proportional hazards model demonstrated that older age (HR 1.066, 95% CI 1.043 to 1.089) and higher white blood cell count (HR 1.135, 95% CI 1.080 to 1.192) were independent risk factors for determining COVID-19 associated mortality. CONCLUSIONS: COVID-19 is associated with a significant risk of morbidity and mortality in the population. Older age and higher white blood cell count were found to be independent risk factors for mortality.


Subject(s)
Age Factors , COVID-19/diagnosis , Leukocyte Count , Adult , Aged , COVID-19/physiopathology , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Proportional Hazards Models , Respiration, Artificial , Retrospective Studies , Risk Factors
14.
Aging (Albany NY) ; 13(7): 9243-9252, 2021 04 03.
Article in English | MEDLINE | ID: covidwho-1168300

ABSTRACT

BACKGROUND: Coronavirus disease (COVID-19) has spread rapidly since 2019. Approximately 15% of the patients will develop severe complications such as multiple organ disease syndrome related to cytokine release syndrome (CRS). Continuous renal replacement therapy (CRRT) can remove inflammatory cytokines through filtration or adsorption. We evaluated the effectiveness of CRRT in COVID-19 patients with CRS. METHODS: This retrospective, multicenter, descriptive study included 83 patients with CRS from three hospitals in Wuhan. RESULTS: In COVID-19 patients with CRS, the fatality rate was even higher in CRRT group (P=0.005). However, inflammatory markers such as C-reactive protein, neutrophil counts, and D-dimer decreased after CRRT (P<0.05). Results of Lasso model showed that tracheotomy (ß -1.31) and convalescent plasma (ß -1.41) were the protective factors. In contrast, CRRT (ß 1.07), respiratory failure (ß 1.61), consolidation on lung CT (ß 0.48), acute kidney injury (AKI) (ß 0.47), and elevated neutrophil count (ß 0.02) were the risk factors for death. CONCLUSIONS: Our results showed that although CRRT significantly reduced the inflammation, it did not decrease the fatality rate of patients with CRS. Therefore, the choice of CRRT indication, dialysis time and dialysis mode should be more careful and accurate in COVID-19 patients with CRS.


Subject(s)
COVID-19/therapy , Continuous Renal Replacement Therapy , Critical Illness/therapy , Cytokine Release Syndrome/therapy , Aged , C-Reactive Protein/analysis , COVID-19/blood , COVID-19/complications , China , Cytokine Release Syndrome/blood , Cytokine Release Syndrome/etiology , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Inflammation/blood , Inflammation/etiology , Inflammation/therapy , Male , Middle Aged , SARS-CoV-2/isolation & purification , Treatment Outcome
15.
BMC Med Imaging ; 21(1): 57, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1148211

ABSTRACT

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.


Subject(s)
COVID-19/diagnostic imaging , Community-Acquired Infections/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Disease Progression , Humans , Pneumonia/diagnostic imaging , Tomography, X-Ray Computed/methods
16.
Sci Rep ; 11(1): 6422, 2021 03 19.
Article in English | MEDLINE | ID: covidwho-1142463

ABSTRACT

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.


Subject(s)
COVID-19/diagnosis , Influenza, Human/diagnosis , Pneumonia, Viral/diagnosis , Adult , COVID-19/diagnostic imaging , Diagnosis, Differential , Female , Humans , Influenza, Human/diagnostic imaging , Male , Middle Aged , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Young Adult
17.
Phys Med Biol ; 2021 Feb 19.
Article in English | MEDLINE | ID: covidwho-1137923

ABSTRACT

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 CAP patients underwent thin-section CT. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features. An infection Size Aware Random Forest method (iSARF) was used for classification. Experimental results show that the proposed method yielded best performance when using the handcrafted features with sensitivity of 91.6%, specificity of 86.8%, and accuracy of 89.8% over state-of-the-art classifiers. Additional test on 734 subjects with thick slice images demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Furthermore, the data of extracted features will be made available after the review process.

18.
Diagn Interv Radiol ; 27(3): 350-353, 2021 May.
Article in English | MEDLINE | ID: covidwho-1112835

ABSTRACT

During the coronavirus disease 2019 (COVID-19) pandemic period, container computed tomography (CT) scanners were developed and used for the first time in China to perform CT examinations for patients with clinically mild to moderate COVID-19 who did not need to be hospitalized for comprehensive treatment, but needed to be isolated in Fangcang shelter hospitals (also known as makeshift hospitals) to receive some supportive treatment. The container CT is a multidetector CT scanner installed within a radiation-protected stand-alone container (a detachable lead shielding room) that is deployed outside the makeshift hospital buildings. The container CT approach provided various medical institutions with the solution not only for rapid CT installation and high adaptability to site environments, but also for significantly minimizing the risk of cross-infection between radiological personnel and patients during CT examination in the pandemic. In this article, we described the typical setup of a container CT and how it worked for chest CT examinations in Wuhan city, the epicenter of COVID-19 outbreak.


Subject(s)
COVID-19/diagnostic imaging , Emergency Service, Hospital , Lung/diagnostic imaging , Multidetector Computed Tomography/instrumentation , Multidetector Computed Tomography/methods , Tomography Scanners, X-Ray Computed , China , Humans , Pandemics , SARS-CoV-2
19.
Aging (Albany NY) ; 13(4): 4794-4810, 2021 02 11.
Article in English | MEDLINE | ID: covidwho-1084990

ABSTRACT

Coronavirus disease 2019 (COVID-19)-associated coagulation dysfunction is gaining attention. In particular, dynamic changes in the D-dimer level may be related to disease progression. Here, we explored whether elevated D-dimer level was related to multiple organ failure and a higher risk of death. This study included 158 patients with COVID-19 who were admitted to the intensive care unit (ICU) at Jinyintan Hospital in Wuhan, China between January 20, 2020 and February 26, 2020. Clinical and laboratory data were collected. The relationship between D-dimer elevation and organ dysfunction was analyzed, as were dynamic changes in inflammation and lipid metabolism. Approximately 63.9% of patients with COVID-19 had an elevated D-dimer level on ICU admission. The 14 day ICU mortality rate was significantly higher in patients with a high D-dimer level than in those with a normal D-dimer level. Patients with a D-dimer level of 10-40µg/mL had similar organ function on ICU admission to those with a D-dimer level of 1.5-10µg/mL. However, patients with higher levels of D-dimer developed organ injuries within 7 days. Furthermore, significant differences in inflammation and lipid metabolism markers were observed between the two groups. In conclusion, the D-dimer level is closely related to COVID-19 severity and might influence the likelihood of rapid onset of organ injury after admission.


Subject(s)
COVID-19/blood , Fibrin Fibrinogen Degradation Products/analysis , Inflammation/blood , Multiple Organ Failure/blood , Aged , Biomarkers/blood , COVID-19/complications , COVID-19/metabolism , China/epidemiology , Disease Progression , Female , Humans , Inflammation/etiology , Inflammation/metabolism , Intensive Care Units , Lipid Metabolism , Male , Middle Aged , Multiple Organ Failure/etiology , Multiple Organ Failure/metabolism , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification
20.
Exp Hematol Oncol ; 10(1): 6, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-1058277

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with coagulation abnormalities which are indicators of higher mortality especially in severe cases. METHODS: We studied patients with proven COVID-19 disease in the intensive care unit of Jinyintan Hospital, Wuhan, China from 30 to 2019 to 31 March 2020. RESULTS: Of 180 patients, 89 (49.44 %) had died, 85 (47.22 %) had been discharged alive, and 6 (3.33 %) were still hospitalised by the end of data collection. A D-dimer concentration of > 0.5 mg/L on admission was significantly associated with 30 day mortality, and a D-dimer concentration of > 5 mg/L was found in a much higher proportion of non-survivors than survivors. Sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC) scoring systems were dichotomised as < 4 or ≥ 4 and < 5 or ≥ 5, respectively, and the mortality rate was significantly different between the two stratifications in both scoring systems. Enoxaparin was administered to 68 (37.78 %) patients for thromboembolic prophylaxis, and stratification by the D-dimer concentration and DIC score confirmed lower mortality in patients who received enoxaparin when the D-dimer concentration was > 2 than < 2 mg/L or DIC score was ≥ 5 than < 5. A low platelet count and low serum calcium concentration were also related to mortality. CONCLUSIONS: A D-dimer concentration of > 0.5 mg/L on admission is a risk factor for severe disease. A SIC score of > 4 and DIC score of > 5 may be used to predict mortality. Thromboembolic prophylaxis can reduce mortality only in patients with a D-dimer concentration of > 2 mg/L or DIC score of ≥ 5.

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