Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.

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Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.

Travel Med Infect Dis. 2020 Jun 08;:101782

Authors: Wang F, Hou H, Wang T, Luo Y, Tang G, Wu S, Zhou H, Sun Z

Abstract
INTRODUCTION: There are currently no satisfactory methods for predicting the outcome of Coronavirus Disease-2019 (COVID-19). The aim of this study is to establish a model for predicting the prognosis of the disease.
METHODS: The laboratory results were collected from 54 deceased COVID-19 patients on admission and before death. Another 54 recovered COVID-19 patients were enrolled as control cases.
RESULTS: Many laboratory indicators, such as neutrophils, AST, γ-GT, ALP, LDH, NT-proBNP, Hs-cTnT, PT, APTT, D-dimer, IL-2R, IL-6, IL-8, IL-10, TNF-α, CRP, ferritin and procalcitonin, were all significantly increased in deceased patients compared with recovered patients on admission. In contrast, other indicators such as lymphocytes, platelets, total protein and albumin were significantly decreased in deceased patients on admission. Some indicators such as neutrophils and procalcitonin, others such as lymphocytes and platelets, continuously increased or decreased from admission to death in deceased patients respectively. Using these indicators alone had moderate performance in differentiating between recovered and deceased COVID-19 patients. A model based on combination of four indicators (P = 1/[1 + e-(-2.658+0.587×neutrophils - 2.087×lymphocytes - 0.01×platelets+0.004×IL-2R)]) showed good performance in predicting the death of COVID-19 patients. When cutoff value of 0.572 was used, the sensitivity and specificity of the prediction model were 90.74% and 94.44%, respectively.
CONCLUSIONS: Using the current indicators alone is of modest value in differentiating between recovered and deceased COVID-19 patients. A prediction model based on combination of neutrophils, lymphocytes, platelets and IL-2R shows good performance in predicting the outcome of COVID-19.

PMID: 32526372 [PubMed - as supplied by publisher]

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