  COVID-19 pandemic has created an extreme pressure on the global healthcare
services. Fast, reliable and early clinical assessment of the severity of the
disease can help in allocating and prioritizing resources to reduce mortality.
In order to study the important blood biomarkers for predicting disease
mortality, a retrospective study was conducted on 375 COVID-19 positive
patients admitted to Tongji Hospital (China) from January 10 to February 18,
2020. Demographic and clinical characteristics, and patient outcomes were
investigated using machine learning tools to identify key biomarkers to predict
the mortality of individual patient. A nomogram was developed for predicting
the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils
(%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at
hospital admission were identified as key predictors of death by multi-tree
XGBoost model. The area under curve (AUC) of the nomogram for the derivation
and validation cohort were 0.961 and 0.991, respectively. An integrated score
(LNLCA) was calculated with the corresponding death probability. COVID-19
patients were divided into three subgroups: low-, moderate- and high-risk
groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability
less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model,
nomogram and LNLCA score can help in early detection of high mortality risk of
COVID-19 patients, which will help doctors to improve the management of patient
stratification.
