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, 10 (1), 273

Implementation of Complementary Model Using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients With Fever

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Implementation of Complementary Model Using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients With Fever

Jang-Sik Choi et al. Sci Rep.

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.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Overall workflow of this study.
Figure 2
Figure 2
Proportion of top 20 diseases ranked based on frequency in control group.
Figure 3
Figure 3
Model performance in each step of stepwise forward selection.
Figure 4
Figure 4
2D t-SNE plot for the optimal combination and sets of specific hematology parameters in each score.
Figure 5
Figure 5
ROC curve for the optimal combination and sets of specific hematology parameters in each score.
Figure 6
Figure 6
Radar pattern (a) and box plot (b) of clinical values of the optimal combination in the outcomes of the complementary model for the validation dataset.

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