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Updated: Jun 18, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Development and validation of a machine learning-based interpretable model for predicting sepsis by complete blood

Tiancong Zhang^1,2,3, Shuang Wang^1,2,3, Qiang Meng^1,2,3

¹Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.

|July 31, 2024

Summary

Early sepsis detection is crucial. Machine learning models using complete blood cell (CBC) parameters effectively predict sepsis, with the LASSO model showing high accuracy in identifying at-risk patients.

Keywords:

Cell population data Complete blood cell parameters Machine learning Prediction model Sepsis

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Area of Science:

Medical Informatics
Clinical Pathology
Machine Learning in Healthcare

Background:

Sepsis is a life-threatening infectious disease with high mortality rates.
Early detection and prompt intervention are critical for improving patient outcomes.
Complete blood cell (CBC) parameters offer a readily available data source for sepsis assessment.

Purpose of the Study:

To develop and validate a clinical prediction model for early sepsis detection.
To leverage machine learning algorithms for analyzing CBC parameters.
To identify key CBC parameters that predict sepsis onset.

Main Methods:

Utilized data from 572 patients (215 sepsis, 357 local infection) at West China Hospital.
Analyzed 57 CBC parameters using LASSO, RF, SVM, and XGBoost for feature selection.
Developed a Logistic Regression model and evaluated performance using AUC, calibration, and decision curves on discovery and validation cohorts.
Employed SHAP and BD profiles for model interpretability.

Main Results:

The LASSO-based model achieved the highest diagnostic performance.
Achieved an AUC of 0.9446 in the discovery cohort and 0.9001 in the validation cohort (P < 0.001).
LY-Z, MO-Z, and PLT-I were identified as the most impactful CBC parameters for sepsis prediction.

Conclusions:

A predictive model utilizing CBC parameters is effective for early sepsis detection.
Machine learning enhances the utility of routine blood tests for critical care diagnostics.
This approach offers a promising tool for timely sepsis identification and management.