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Abstract
The early detection and timely treatment are the most important factors for improving the outcome of patients with sepsis. Sepsis-related clinical score, such as SIRS, SOFA and LODS, were defined to identify patients with suspected infection and to predict severity and mortality. A few hematological parameters associated with organ dysfunction and infection were included in the score although various clinical pathology parameters (hematology, serum chemistry and plasma coagulation) in blood sample have been found to be associated with outcome in patients with sepsis. The investigation of the parameters facilitates the implementation of a complementary model for screening sepsis to existing sepsis clinical criteria and other laboratory signs. In this study, statistical analysis on the multiple clinical pathology parameters obtained from two groups, patients with sepsis and patients with fever, was performed and the complementary model was elaborated by stepwise parameter selection and machine learning. The complementary model showed statistically better performance (AUC 0.86 vs. 0.74–0.51) than models built up with specific hematology parameters involved in each existing sepsis-related clinical score. Our study presents the complementary model based on the optimal combination of hematological parameters for sepsis screening in patients with fever.
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1 Hanyang University, Center for Next Generation Cytometry, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317) ; Hanyang University, Department of Chemistry, College of Natural Sciences, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317) ; Hanayng University, Institute of Next Generation Material Design, Seoul, Republic of Korea (GRID:grid.49606.3d)
2 Hanyang University, Center for Next Generation Cytometry, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317) ; Hanyang University, Department of Chemistry, College of Natural Sciences, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317)
3 Yonsei University, Department of Laboratory Medicine, College of Medicine, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
4 Hanyang University, Department of Laboratory Medicine, College of Medicine, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317)
5 Hanyang University, Department of Mathematics, College of Natural Sciences, Seoul, Republic of Korea (GRID:grid.49606.3d) (ISNI:0000 0001 1364 9317)
6 Kangwon National University, Division of Electronics, Information and Communication Engineering, Kangwon-Do, Republic of Korea (GRID:grid.412010.6) (ISNI:0000 0001 0707 9039)