Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 8 de 8
Filter
2.
Chinese Journal of Integrated Traditional and Western Medicine ; 40(12):1454-1457, 2020.
Article in Chinese | CAB Abstracts | ID: covidwho-1342702

ABSTRACT

Objective: To observe distribution of traditional Chinese medicine(TCM) patterns and changes of peripheral blood cell count and chest imaging in mild and moderate COVID-19 patients.

3.
European Journal of Inflammation (Sage Publications, Ltd.) ; : 1-6, 2021.
Article in English | Academic Search Complete | ID: covidwho-1298037

ABSTRACT

Most of the critically ill novel coronavirus 2019 pneumonia (NCP) patients progress promptly, and soon match the ARDS diagnostic criteria. When mechanical ventilation and prone position cannot reverse the fatal hypoxia—extra-corporeal-membrane-oxygenation (ECMO) will be applied as a salvage treatment if available. Here, we report a novel coronavirus 2019 pneumonia (NCP) patient, a male, 67 years old, who was treated with ECMO for 30 days. In the midst, bronchoscopy was utilized to comprehend the airway lesions and clear secretions. And computed tomography (CT) scans were performed before and after the treatment of ECMO. In the recovering phase of his disease, the patient experienced multiple times pneumothorax on both sides. Some newly developed lung bullae in the subpleural area and modest bronchiectasis were found by the CT scan. The newly developed lung bullae was the probable cause of pneumothorax. Notably, in the whole process of his illness, the serum IL-6 only had a slight elevation in the early period, there is no typical cytokine storm as that was seen in non-COVID-19 ARDS. After 3-months meticulous treatment, the patient made a full recovery and now is discharged from our hospital. Though COVID-19 may not cause typical cytokine storm, the inflammation in lung may inflict severe damage to lung. Severe NCP may cause lung bullae and bronchiectasis, making the patients hard to be weaned from mechanical ventilation or ECMO. [ABSTRACT FROM AUTHOR] Copyright of European Journal of Inflammation (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

5.
Cultures of Science ; : 2096608320985112, 2021.
Article in English | Sage | ID: covidwho-1085172

ABSTRACT

Medical education is an important part of health care. Many medical educators have thought deeply about it since the outbreak of COVID-19. Based on my own experience and thinking during the pandemic, I now believe more strongly in the importance of the cultures of medicine and science, which reflect the views of life amongst medical and scientific professionals. The two cultures derive from the lives of medical workers and scientists and manifest in their attitudes towards clinical practice, scientific research and teaching. They also embody the spirit, essence and philosophy of the medical and scientific professions ? the common belief in seeking the truth and the common will to pursue kindness. Education today is not simply a matter of passing on knowledge. More importantly, the next generation shall inherit the scientific culture of seeking the truth, pursuing kindness and exploring beauty and humanity. This is crucial to medical education and clinical practice.

6.
Nat Commun ; 11(1): 3543, 2020 07 15.
Article in English | MEDLINE | ID: covidwho-974925

ABSTRACT

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.


Subject(s)
Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Deep Learning/statistics & numerical data , Pneumonia, Viral/diagnosis , Pneumonia, Viral/pathology , Triage/methods , Betacoronavirus , COVID-19 , Critical Illness , Hospitalization , Humans , Middle Aged , Models, Theoretical , Pandemics , Prognosis , Risk , SARS-CoV-2 , Survival Analysis
7.
Emerg Microbes Infect ; 9(1): 1974-1983, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-714084

ABSTRACT

Dynamic changes of RNA and antibodies in SARS-CoV-2 infected patients remain largely unknown, and influence factors for antibody production have not been fully clarified. In this study, consecutive throat swabs specimens (n = 1875) from 187 patients were collected to analyse the dynamic changes of RNA. Moreover, 162 serial serum samples from 31 patients were tested for seroconversion of IgM and IgG. Meanwhile, IgM and IgG were also detected in 409 COVID-19 patients and 389 controls. Additionally, the logistic regression analysis was executed to identify the possible influence factors for antibody production. The median positive conversion time for RNA was day 7 (IQR, 3-11), and the positive rate was highest in day 1-5 (74.59 %) and then gradually decreased. The median time of seroconversion for IgM and IgG were both day 12 (IQR, 10-15). The sensitivity and specificity for IgM (or IgG) was 87.04% and 96.92%, respectively. Multivariate logistic regression indicated that reduced lymphocytes and short positive conversion time for SARS-CoV-2 RNA were independent factors for negative results of IgM and IgG. In conclusion, RNA and antibodies should be combined for COVID-19 diagnosis, and delayed seroconversion was influenced by the decreased lymphocytes and short positive conversion time for RNA.


Subject(s)
Antibodies, Viral/blood , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , RNA, Viral/isolation & purification , Aged , Betacoronavirus/genetics , Betacoronavirus/immunology , COVID-19 , COVID-19 Testing , Female , Humans , Immunoglobulin G/blood , Immunoglobulin M/blood , Male , Middle Aged , Pandemics , Pharynx/virology , RNA, Viral/genetics , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity , Seroconversion
8.
JAMA Intern Med ; 180(8): 1081-1089, 2020 08 01.
Article in English | MEDLINE | ID: covidwho-245503

ABSTRACT

Importance: Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/). Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/standards , Coronavirus Infections/physiopathology , Critical Care/organization & administration , Critical Illness/therapy , Pneumonia, Viral/physiopathology , Adult , Aged , COVID-19 , COVID-19 Testing , China , Cohort Studies , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Risk Assessment/standards , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL
...