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

Abstract

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.

Neutrophil Extracellular Trap Formation Potential Correlates with Lung Disease Severity in COVID-19 Patients

“…COVID-19 patients and healthy donors did not differ in regard to age, sex, race, or ethnicity. Peak WBC count was higher in patients who developed moderate to severe lung disease (14.36 ± 1.61 × 10 9 /L) versus patients who developed no/minimal lung disease (8.80 ± 0.86 × 10 9 /L, p < 0.01) consistent with a recently published prospective study in Wuhan that reported significantly higher WBC count in COVID-19 patients with severe-critically severe versus mild-moderate disease [ 37 ]. The reference range for (WBC) count is 4.5–11.0 × 10 9 /L.…”

“…The baseline characteristics of each cohort are displayed in Table 1. [37]. The reference range for (WBC) count is 4.5-11.0 × 10 9 /L.…”

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

“…Huyut et al [6], in a study of patients who died from COVID-19, showed that the ESR, INR, PT, CRP, D-dimer, and ferritin biomarkers are the most important biomarkers to detect the mortality of the disease. Luo et al [56] proposed a multi-criteria decision making (MCDM) algorithm combining ideal the solution similarity sequencing technique (TOPSIS) and naive Bayes (NB) as a feature selection procedure to predict the severity of COVID-19 from initial RBV values. With the MCDM model, the WBC, LYM, NEU values, and age were the most effective features in determining the severity of the disease with 82% accuracy obtained by ROC analysis [56].…”

Predictors Of Successful Outcome Of Self-Proning And High Flow Nasal Oxygen Therapy In Patients With COVID-19 Pneumonia: A Retrospective Analysis

“…Multiple blood tests were shown to be associated with independent risk for poor ICU outcomes in COVID-19 patients. Elevated white blood cell count and neutrophils and low lymphocyte count are directly proportional to the severity of COVID-19 [17]. Moreover, elevated biomarkers of in ammation in COVID-19 namely: D-dimer, CRP, LDH, and ferritin, are independently associated with higher ICU admission, invasive ventilatory support, and death [18].…”