Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

Front Cell Infect Microbiol

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Published: April 2021

Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.

Methods: This study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.

Findings: The model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94-94.31%), sensitivity 97.17% (95% CI, 94.97-98.46%), and specificity 82.05% (95% CI, 77.24-86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91-99.04%), 82.22% sensitivity (95% CI, 67.41-91.49%), and 84.00% specificity (95% CI, 63.08-94.75%).

Interpretation: We found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient's prognosis and to reduce mortality.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7966961PMC
http://dx.doi.org/10.3389/fcimb.2020.586054DOI Listing

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