Prediction of Sepsis in COVID-19 Using Laboratory Indicators.
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.
['Tang|Guoxing|G|', 'Luo|Ying|Y|', 'Lu|Feng|F|', 'Li|Wei|W|', 'Liu|Xiongcheng|X|', 'Nan|Yucen|Y|', 'Ren|Yufei|Y|', 'Liao|Xiaofei|X|', 'Wu|Song|S|', 'Jin|Hai|H|', 'Zomaya|Albert Y|AY|', 'Sun|Ziyong|Z|']
D000328:Adult D000368:Aged D000086382:COVID-19 D000086742:COVID-19 Testing D002681:China D005260:Female D006801:Humans D016015:Logistic Models D000069550:Machine Learning D008297:Male D008875:Middle Aged D011379:Prognosis D012372:ROC Curve D012189:Retrospective Studies D012307:Risk Factors D000086402:SARS-CoV-2 D018805:Sepsis
sepsis COVID-19 coagulation function inflammatory factor artificial intelligence