A Data-Driven Approach to Quantifying Immune States in Sepsis.

Abstract

In sepsis, understanding the interplay among white blood cells, lymphocytes, and neutrophils is crucial for assessing the immune condition and optimizing treatment strategies. Blood samples were collected from 512 patients diagnosed with sepsis and 205 healthy controls, totaling 717 samples. Data visualization analysis and three-dimensional numerical fitting were performed to establish a mathematical model describing the relationships among white blood cells, lymphocytes, and neutrophils. Self-organizing feature map (SOFM) was employed to automatically cluster the sepsis sample data in the three-dimensional space represented by the model, yielding different immune states. Analysis revealed that white blood cell, lymphocyte, and neutrophil counts are constrained within a three-dimensional plane, as described by the equation: WBC = 1.098 × Neutrophils + 1.046 × Lymphocytes + 0.1645, yielding a prediction error (RMSE) of 1%. This equation is universally applicable to all samples despite differences in their spatial distributions. SOFM clustering identified nine distinct immune states within the sepsis patient population, representing different levels of immune status, oscillation periods, and recovery stages. The proposed mathematical model, represented by the equation above, reveals a basic constraint boundary on the immune cell populations in both sepsis patients and healthy controls. Furthermore, the SOFM clustering approach provides a comprehensive overview of the distinct immune states observed within this constraint boundary in sepsis patients. This study lays the foundation for future work on quantifying and categorizing the immune condition in sepsis, which may ultimately contribute to the development of more objective diagnostic and treatment strategies.