Potential Factors for Prediction of Disease Severity of COVID-19 Patients
zhang, h.; wang, x.; fu, z.; luo, m.; zhang, z.; zhang, k.; he, y.; wan, d.; zhang, l.; wang, j.; yan, x.; han, m.; chen, y.
Show abstract
ObjectiveCoronavirus disease 2019 (COVID-19) is an escalating global epidemic caused by SARS-CoV-2, with a high mortality in critical patients. Effective indicators for predicting disease severity in SARS-CoV-2 infected patients are urgently needed. MethodsIn this study, 43 COVID-19 patients admitted in Chongqing Public Health Medical Center were involved. Demographic data, clinical features, and laboratory examinations were obtained through electronic medical records. Peripheral blood specimens were collected from COVID-19 patients and examined for lymphocyte subsets and cytokine profiles by flow cytometry. Potential contributing factors for prediction of disease severity were further analyzed. ResultsA total of 43 COVID-19 patients were included in this study, including 29 mild patients and 14 sever patients. Severe patients were significantly older (61.9{+/-}9.4 vs 44.4{+/-}15.9) and had higher incidence in co-infection with bacteria compared to mild group (85.7%vs27.6%). Significantly more severe patients had the clinical symptoms of anhelation (78.6%) and asthma (71.4%). For laboratory examination, 57.1% severe cases showed significant reduction in lymphocyte count. The levels of Interluekin-6 (IL6), IL10, erythrocyte sedimentation rate (ESR) and D-Dimer (D-D) were significantly higher in severe patients than mild patients, while the level of albumin (ALB) was remarkably lower in severe patients. Further analysis demonstrated that ESR, D-D, age, ALB and IL6 were the major contributing factors for distinguishing severe patients from mild patients. Moreover, ESR was identified as the most powerful factor to predict disease progression of COVID-19 patients. ConclusionAge and the levels of ESR, D-D, ALB and IL6 are closely related to the disease severity of COVID-19 patients. ESR can be used as a valuable indicator for distinguishing severe COVID-19 patients in early stage, so as to increase the survival of severe patients.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SARS-CoV-2 infection induces mixed M1/M2 phenotype in circulating monocytes and alterations in both dendritic cell and monocyte subsets 96%
- COVID-19 Disease Severity and Determinants among Ethiopian Patients: A study of the Millennium COVID-19 Care Center 95%
- Characteristics and outcome profile of Hospitalized African COVID-19 patients: The Ethiopian Context 95%
Similar papers in this journal
- Relative expression of pro-inflammatory molecules in COVID-19 patients manifested disease severities 97%
- Evaluation of rapid antibody test and chest computed tomography results of COVID-19 patients: A retrospective study 95%
- Comprehensive genomic, immunological and clinical analysis of COVID-19 vaccine breakthrough infections: a prospective, comparative cohort study 95%
Similar papers in this journal
- Evaluation of the disease outcome in Covid-19 infected patients by disease symptoms: a retrospective cross-sectional study in Ilam Province, Iran 95%
- Determinants of Developing Symptomatic Disease in Ethiopian COVID-19 Patients 95%
- Quantitative investigation of factors relevant to the T cell spot test for tuberculosis infection in active tuberculosis 94%
Similar papers in this journal
- Is the infection of the SARS-CoV-2 Delta variant associated with the outcomes of COVID-19 patients? 95%
- Clinical findings in critical ill patients infected with SARS-Cov-2 in Guangdong Province, China: a multi-center, retrospective, observational study 95%
- Clinical Characteristics and Short-Term Outcomes of Severe Patients with COVID-19 in Wuhan, China 93%
Similar papers in this journal
- Circulatory Cytokines and Chemokines Profile in Human Coronaviruses: A systematic review and meta-analysis 95%
- Transcriptome analysis of PBMCs reveals distinct immune response in the asymptomatic and re-detectable positive COVID-19 patients 94%
- Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.