Patterns (Sep 2020)
A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
Abstract
Summary: The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics. The Bigger Picture: The COVID-19 pandemic is threatening millions of lives and putting medical systems under stress worldwide. Although the infection growth in some areas has ceased, there is a risk of a second wave. Therefore, a sustainable strategy to defend against a pandemic using the current limited but effective healthcare resources is in high demand. Our study aims to find a solution that triages patients to hospitalization by identifying their severity progression. In this study, a model that used four easily accessible biomarkers to assess the risk of severe COVID-19 was successfully developed. This model is easy to use, and it eliminates the dependence on expensive equipment to make a decision. It was found to be effective in identifying the risk of severe COVID-19. Thus, it is practically applicable for general practitioners to effectively assess the infection and allocate inpatient care to the cases who need it most. Our study is expected to have a prolonged social impact under the current circumstances.
