A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
Yichao Zheng,
Yinheng Zhu,
Mengqi Ji,
Rongpin Wang,
Xinfeng Liu,
Mudan Zhang,
Jun Liu,
Xiaochun Zhang,
Choo Hui Qin,
Lu Fang,
Shaohua Ma
Affiliations
Yichao Zheng
Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
Yinheng Zhu
Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
Mengqi Ji
Department of Automation, Tsinghua University, Beijing 100084, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
Rongpin Wang
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
Xinfeng Liu
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
Mudan Zhang
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China
Jun Liu
Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha 410011, China; Department of Radiology Quality Control Center, Changsha 410011, China
Xiaochun Zhang
Department of Radiology, Zhongnan Hospital, Wuhan University, Wuhan 43000, China
Choo Hui Qin
Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China
Lu Fang
Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China; Corresponding author
Shaohua Ma
Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China; Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen 518055, China; Corresponding author
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.
