Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study.

PURPOSE: Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS: One hundred fifty COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS: A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control.

Diagnostic Value of Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio in Patients with Severe COVID-19 - 7 PLADs, China, January 21-February 10, 2020.

What is already known about this topic?: Coronavirus disease 2019 (COVID-19) causes symptoms ranging from mild to severe. Indicators for identifying severe COVID-19 infection have not been well identified, especially for young patients. What is added by this report?: Both neutrophil-lymphocyte ratio (NLR) [area under curve (AUC): 0.80; the odds ratios (OR) and 95% confidence intervals (95% CI): 1.30 (1.13-1.50)] and platelet-lymphocyte ratio (PLR) [AUC: 0.87; OR (95% CI): 1.05 (1.01-1.09)] were determined to be indicators for recognition of patients with severe COVID-19 in young patients less than age 40. What are the implications for public health practice?: NLR and PLR are useful indicators for identifying patients with severe COVID-19, especially in young patients less than age 40.

Clinical and antibody characteristics reveal diverse signatures of severe and non-severe SARS-CoV-2 patients.

BACKGROUND: COVID-19 pandemic continues, clarifying signatures in clinical characters and antibody responses between severe and non-severe COVID-19 cases would benefit the prognosis and treatment. METHODS: In this study, 119 serum samples from 37 severe or non-severe COVID-19 patients from the First People's Hospital of Yueyang were collected between January 25 and February 18 2020. The clinical features, antibody responses targeting SARS-CoV-2 spike protein (S) and its different domains, SARS-CoV-2-specific Ig isotypes, IgG subclasses, ACE2 competitive antibodies, binding titers with FcγIIa and FcγIIb receptors, and 14 cytokines were comprehensively investigated. The differences between severe and non-severe groups were analyzed using Mann-Whitney U test or Fisher's exact test. RESULTS: Severe group including 9 patients represented lower lymphocyte count, higher neutrophil count, higher level of LDH, total bile acid (TBA) (P < 1 × 10-4), r-glutaminase (P = 0.011), adenosine deaminase (P < 1 × 10-4), procalcitonin (P = 0.004), C-reactive protein (P < 1 × 10-4) and D-dimer (P = 0.049) compared to non-severe group (28 patients). Significantly, higher-level Igs targeting S, different S domains (RBD, RBM, NTD, and CTD), FcγRIIa and FcγRIIb binding capability were observed in a severe group than that of a non-severe group, of which IgG1 and IgG3 were the main IgG subclasses. RBD-IgG were strongly correlated with S-IgG both in severe and non-severe group. Additionally, CTD-IgG was strongly correlated with S-IgG in a non-severe group. Positive RBD-ACE2 binding inhibition was strongly associated with high titers of antibody (S-IgG1, S-IgG3, NTD-IgG, RBD-IgA, NTD-IgA, and CTD-IgA) especially RBD-IgG and CTD-IgG in the severe group, while in the non-severe group, S-IgG3, RBD-IgG, NTD-IgG, and NTD-IgM were correlated with ACE2 blocking rate. S-IgG1, NTD-IgM and S-IgM were negatively associated with illness day in a severe group, while S-IgG3, RBD-IgA, CTD-IgA in the severe group (r = 0.363, P = 0.011) and S-IgG1, NTD-IgA, CTD-IgA in the non-severe group were positively associated with illness day. Moreover, GRO-α, IL-6, IL-8, IP-10, MCP-1, MCP-3, MIG, and BAFF were also significantly elevated in the severe group. CONCLUSION: Antibody detection provides important clinical information in the COVID-19 process. The different signatures in Ig isotypes, IgG subclasses, antibody specificity between the COVID-19 severe and non-severe group will contribute to future therapeutic and preventive measures development.

Early predictors and screening tool developing for severe patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. METHODS: This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. RESULTS: Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than < 0.8 × 109 per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747-0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. CONCLUSIONS: This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service.

Development of portable vital signs monitoring system for field severe patient / 医疗卫生装备

Objective To design a portable vital signs monitoring system to monitor and analyze severe patient vital signs in field conditions.Methods The communication protocols of all medical devices were analyzed.The information on the vital signs were acquired periodically from medical devices with the signal transducer based on embedded technique,and then sent to the computer for analysis.The abnormalities were displayed on the portable terminal.Results The system met the desired requirements,and facilitated the medical personnel dedicated to treatment.Conclusion The system contributes to information sharing for the changes of vital signs in field conditions,so that the doctor can take measures in time to enhance the efficiency and reliability of the treatment.

Clinical value of CereTom mobile CT scanner for patients in neurological ICU / 医疗卫生装备

Objective To explore the clinical value of cranial CT for the patients in neurological ICU by analyzing the application of mobile CT scanner CereTom in some hospital.Methods Retrospective analysis was carried out for the patients being hospitalized and undergoing cranial CT examination in some hospital from March 2012 to August 2014.Results Totally 261 patients and 325 times of examination were involved in, and two ones failed in the examination, with the success rate of 99.23%. There were 218 patients (83.52%) had the examination completed in one time and 43 ones (16.48%) in several times. It's proved that bedside CT could be applied clinically with high successful rate. The mean time of bedside CT examination was (18.3±3.8)min, significantly less that then of common examination.Conclusion Mobile CT may decrease moving-related risk of the patient and the time, manpower consumed for examination, and thus is worth popularizing clinically.