Value of clinical laboratory test for early prediction of mortality in patients with COVID-19: the BGM score

Abstract

Background: COVID-19 causes high mortality and long hospitalization periods. The aim of this study was to search for new early prognostic strategies accessible to most health care centers. Methods: Laboratory results, demographic and clinical data from 500 patients with positive SARS-CoV-2 infection were included in our study. The data set was split into training and test set prior to generating different multivariate models considering the occurrence of death as the response variable. A final computational method called the BGM score was obtained by combining the previous models and is available as an interactive web application. Results: The logistic regression model comprising age, creatinine (CREA), D-dimer (DD), C-reactive protein (CRP), platelet count (PLT), and troponin I (TNI) showed a sensitivity of 47.3%, a specificity of 98.7%, a kappa of 0.56, and a balanced accuracy of 0.73. The CART classification tree yielded TNI, age, DD, and CRP as the most potent early predictors of mortality (sensitivity = 68.4%, specificity = 92.5%, kappa = 0.61, and balanced accuracy = 0.80). The artificial neural network including age, CREA, DD, CRP, PLT, and TNI yielded a sensitivity of 66.7%, a specificity of 92.3%, a kappa of 0.54, and a balanced accuracy of 0.79. Finally, the BGM score surpassed the prediction accuracy performance of the independent multivariate models, yielding a sensitivity of 73.7%, a specificity of 96.5%, a kappa of 0.74, and a balanced accuracy of 0.85. Conclusions: The BGM score may support clinicians in managing COVID-19 patients and providing focused interventions to those with an increased risk of mortality.