A Data-Driven Approach to Quantifying Immune States in Sepsis

Summary

This study investigates the immune condition in sepsis by analyzing the quantitative relationships among white blood cells, lymphocytes, and neutrophils in sepsis patients and healthy controls using data visualization analysis and three-dimensional numerical fitting to establish a mathematical model.

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.

Introduction

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, remains a significant challenge in critical care medicine¹. Despite advances in understanding the pathophysiology of sepsis, the complex interplay between the immune system and pathogens continues to pose difficulties in diagnosing and treating this condition effectively². Current clinical approaches often focus on monitoring infection indicators, organ function, cytokines, microbial detection, and gut microbiome³. However, there is a growing recognition of the crucial role played by immune cells, par....

Protocol

This study explores the immune condition in sepsis patients by investigating the relationships among white blood cells, lymphocytes, and neutrophils. The patients were enrolled in the intensive care unit (ICU) of Dongzhimen Hospital in Beijing, China, and underwent standard blood tests after providing informed consent. The study was conducted as per the guidelines of the institutional human research ethics committee. Data grouping and detailed data content can be found in Supplementary Table 1. The software tools utilized in this study are enumerated in the Table of Materials.

Discussion

This study presents an approach to understanding the immune condition in sepsis by leveraging advanced data visualization and machine learning techniques. By uncovering the mathematical relationship among key immune cell populations and identifying distinct immune states, the study provides a new perspective on the complex immune dynamics in sepsis and contributes to the development of more effective diagnostic and therapeutic strategies^11,12. The key findings in.......

Materials

Name	Company	Catalog Number	Comments
MATLAB	MathWorks	2022B	Computing and visualization
Probabilistic Scatter Plots for Immune States	Intelligent  Entropy	Immune States V1.0	Beijing Intelligent Entropy Science & Technology Co Ltd. Modeling for CT/MRI fusion