ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) has become a global health problem. We aim to investigate the changes in the results of viral nucleic acid tests on pharyngeal swabs and feces of patients with COVID-19 and CT imaging of lungs as the disease progresses. Methods: Seven patients with COVID-19 in the third affiliated hospital of Sun Yat-sen University Yuedong Hospital were retrospectively enrolled with clinical features, including imaging staging, and performance characteristics of viral nucleic acid test results of pharyngeal swabs and feces. The dynamic changes of these features were observed during hospitalization, and therapeutic effect and prognosis of patients were evaluated. Results: The results of seven confirmed cases were positive for viral nucleic acid tests on pharyngeal swabs early after the onset of symptoms, and then turned negative; while the results of viral nucleic acid tests on feces were persistently positive in the mid-term clinical treatment and recovery period. And the viral nucleic acid test results were capricious in three cases. Pulmonary CT imaging showed characteristic changes in early, advanced and recovery phases. Conclusion: The application of viral nucleic acid detection and pulmonary CT imaging can be used for screeningof suspected cases, and early diagnosis and dynamic condition assessment of patients.
Subject(s)
COVID-19ABSTRACT
Background Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) that began in Wuhan and rapidly spread globally. The speed and scope of spread of COVID-19 makes urgent of the defining clinical characteristics, serological and radiological changes of the affected patients.Method 7 patients with laboratory-confirmed COVID-19 who admitted to the Third affiliated hospital of Sun Yat-sen university Yuedong hospital from January 2020 to March 2020 were retrospectively enrolled and their clinical features, serological and radiological longitudinal changes were analyzed.Results Among the 7 patients, all (100%) had a clear epidemiological history. The most common symptoms were respiratory symptoms 6 (85.7%), and only 2 (28.6%) of the patients had fever at their first visit. The cohort included 4 (57.1%) common types and 3 (42.9%) severe types. Two (28.6%) common types patients developed to severe type in a short time. All of the 7 patients (100%) had abnormal liver function, normal renal function and normal procalcitonin. The detection time of specific antibody in 7 patients was 5~13d after symptoms. Before the specific antibody could be detected, the absolute value of lymphocytes decreased in 2 (28.6%) common type cases transferred to severe type cases accompanied with obvious progress in pulmonary imaging, and the phenomenon of decreased albumin and elevated globulin occurred in 6 patients (85.7%). The predominant pattern of lung lesions observed was bilateral (71.4%) and mainly near the pleura at the first diagnosis. Bilateral pulmonary involvement occurred in 6 cases (85.7%) during the course of disease. In 4 cases (57.1%) with obvious pulmonary lesions, the absolute value of lymphocytes decreased, albumin decreased and globulin increased during the course of the disease. Conclusion Serum specific antibodies can be detected within 2 weeks of onset. Close observation of the dynamic changes of absolute value of blood lymphocytes, serum albumin and globulin which were related to pulmonary imaging changes in patients will contribute to assessment of COVID-19.
Subject(s)
COVID-19 , Fever , Lung DiseasesABSTRACT
Background Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) that began in Wuhan and rapidly spread globally. The speed and scope of spread of COVID-19 makes urgent of the defining clinical characteristics, serological and radiological changes of the affected patients. Method 7 patients with laboratory-confirmed COVID-19 who admitted to the Third affiliated hospital of Sun Yat-sen university Yuedong hospital from January 2020 to March 2020 were retrospectively enrolled and their clinical features, serological and radiological longitudinal changes were analyzed. Results Among the 7 patients, all (100%) had a clear epidemiological history. The most common symptoms were respiratory symptoms 6 (85.7%), and only 2 (28.6%) of the patients had fever at their first visit. The cohort included 4 (57.1%) common types and 3 (42.9%) severe types. Two (28.6%) common types patients developed to severe type in a short time. All of the 7 patients (100%) had abnormal liver function, normal renal function and normal procalcitonin. The detection time of specific antibody in 7 patients was 5~13d after symptoms. Before the specific antibody could be detected, the absolute value of lymphocytes decreased in 2 (28.6%) common type cases transferred to severe type cases accompanied with obvious progress in pulmonary imaging, and the phenomenon of decreased albumin and elevated globulin occurred in 6 patients (85.7%). The predominant pattern of lung lesions observed was bilateral (71.4%) and mainly near the pleura at the first diagnosis. Bilateral pulmonary involvement occurred in 6 cases (85.7%) during the course of disease. In 4 cases (57.1%) with obvious pulmonary lesions, the absolute value of lymphocytes decreased, albumin decreased and globulin increased during the course of the disease. Conclusion Serum specific antibodies can be detected within 2 weeks of onset. Close observation of the dynamic changes of absolute value of blood lymphocytes, serum albumin and globulin which were related to pulmonary imaging changes in patients will contribute to assessment of COVID-19.
Subject(s)
COVID-19ABSTRACT
The novel coronavirus pneumonia (NCP) has spread from Wuhan to all parts of China since December 2019, and the prevention and control of NCP is a top priority for medical staff. Now report three cases of NCP patients, whose viral nucleic acids still positive in stool after throat swab detection turned negative. In view of the highly homologous and similar clinical manifestations between the 2019 novel coronavirus (2019-nCoV) and the severe acute respiratory syndrome(SARS) related coronaviruses, it is recommended to attach great importance to the detection of the viral nucleic acids in stool, with the reference of SARS prevention and control experience. In order to minimize the risks of gastrointestinal spread, the detection of 2019-nCoV nucleic acids in stool may be recommended as the reference standard of disisolation and discharge.
ABSTRACT
Background Severe cases of coronavirus disease 2019 (COVID-19) rapidly develop acute respiratory distress leading to respiratory failure, with high short-term mortality rates. At present, there is no reliable risk stratification tool for non-severe COVID-19 patients at admission. We aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. Methods SARS-CoV-2 infected patients from one center in Wuhan city and two centers in Guangzhou city, China were included retrospectively. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. We compared the demographic, clinical, and laboratory data between severe and non-severe group. Based on baseline data, least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression model were used to construct a nomogram for risk prediction in the train cohort. The predictive accuracy and discriminative ability of nomogram were evaluated by area under the curve (AUC) and calibration curve. Decision curve analysis (DCA) and clinical impact curve analysis (CICA) were conducted to evaluate the clinical applicability of our nomogram. Findings The train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19 and 107 (28.76%) patients had one of the following basic disease, including hypertension, diabetes, coronary heart disease, chronic respiratory disease, tuberculosis disease. We found one demographic and six serological indicators (age, serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width (RDW), blood urea nitrogen, albumin, direct bilirubin) are associated with severe COVID-19. Based on these features, we generated the nomogram, which has remarkably high diagnostic accuracy in distinguishing individuals who exacerbated to severe COVID-19 from non-severe COVID-19 (AUC 0.912 [95% CI 0.846-0.978]) in the train cohort with a sensitivity of 85.71 % and specificity of 87.58% ; 0.853 [0.790-0.916] in validation cohort with a sensitivity of 77.5 % and specificity of 78.4%. The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. DCA and CICA further indicated that our nomogram conferred significantly high clinical net benefit. Interpretation Our nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19. And this risk stratification tool will enable better centralized management and early treatment of severe patients, and optimal use of medical resources via patient prioritization and thus significantly reduce mortality rates. The RDW plays an important role in predicting severe COVID-19, implying that the role of RBC in severe disease is underestimated.
Subject(s)
Respiratory Distress Syndrome , Severe Acute Respiratory Syndrome , Diabetes Mellitus , Coronary Disease , Chronic Disease , Hypertension , Tuberculosis , COVID-19 , Respiratory InsufficiencyABSTRACT
BackgroundA novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and MethodsWe collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.