The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.

The Selection of Indicators from Initial Blood Routine Test Results to Improve the Accuracy of Early Prediction of COVID-19 Severity

Early prediction of disease severity is important for effective treatment of COVID-19. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets {Age, WBC, LYMC, NEUT}, {Age, WBC, LMYC} and {Age, NEUT, LYMC}. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. This result indicated that using age and the indicators selected by the MCDM algorithm from blood routine test results can effectively predict the severity of COVID-19 at an early stage.

Anticoagulant in Patients with Pulmonary Embolism and Fatty Liver: A Case Report

AIMS: Patients with liver disease are more prone to thrombosis and bleeding events than healthy people. The decision to use anticoagulation in patients with pulmonary embolism and liver disease requires a cautious evaluation of the risks of bleeding and the benefits of anticoagulation. In order to make a recommendation for patient with fatty liver that developed pulmonary embolism, we reported this case. METHODS: In this paper, we reported a case of a patient with fatty liver that developed pulmonary embolism. The low molecular weight heparin sodium injection and different anticoagulants were evaluated for this patient.RESULTS: After evaluating the efficacy and safety of different anticoagulants in patients with pulmonary embolism and liver dysfunction, rivaroxaban is more suitable for this patient to be treated by sequential therapy. After three weeks of treatment with rivaroxaban, the patient’s blood routine test results were normal, and no side effect was found. CONCLUSIONS: For patients with pulmonary embolism and fatty liver, maybe NOAC is more suitable.

Blood routine test in mild and common 2019 coronavirus (COVID-19) patients

The new 2019 coronavirus disease (COVID-19), according to the World Health Organization (WHO), has been characterized as a pandemic. As more is being discovered about this virus, we aim to report findings of the complete blood count (CBC) of COVID-19 patients. This would serve in providing physicians with important knowledge on the changes that can be expected from the CBC of mild and normal COVID-19 patients. A total of 208 mild and common patients were admitted at the Dongnan Hospital located in the city of Xiaogan, Hubei, China. The CBCs of these patients, following a confirmed diagnosis of COVID-19, were retrospectively analyzed and a significant P<0.05 was found after a full statistical analysis was conducted using the Statistical Package for the Social Sciences (IBM SPSS). CBC analysis revealed changes in the levels of red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and C-reactive protein (CRP). Clinicians should expect similar findings when dealing with the new COVID-19.