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Updated: Jun 22, 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

Optimize individualized energy delivery for septic patients using predictive deep learning models.

Lu Wang^1,2, Li Chang³, Ruipeng Zhang^1,2

¹Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Asia Pacific Journal of Clinical Nutrition

|July 5, 2024

Summary

This summary is machine-generated.

Optimizing energy delivery for septic patients using deep learning models is crucial. Permissive underfeeding is recommended only in the early acute phase, with increased intake later to improve survival.

Keywords:

deep learning energy delivery machine learning nutrition support sepsis

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Area of Science:

Critical Care Medicine
Nutritional Support
Artificial Intelligence in Healthcare

Background:

Sepsis management requires precise nutritional support.
Individualized energy delivery is critical for septic patients in intensive care units (ICUs).
Current energy delivery strategies may not account for dynamic metabolic changes during sepsis.

Purpose of the Study:

To develop and validate deep learning models for optimizing individualized energy delivery in adult septic patients.
To identify optimal energy targets across different metabolic phases of sepsis.

Main Methods:

A retrospective study of 179 adult septic patients in the ICU, collecting 47 indicators over 14 days.
Data divided into three metabolic phases: acute early, acute late, and rehabilitation.
Deep learning models established for optimal energy targets in each phase, with external validation.

Main Results:

Optimal energy targets identified as 900 kcal/d (early acute), 2300 kcal/d (late acute), and 2000 kcal/d (rehabilitation).
Excessive energy in the early acute phase and insufficient energy in the late acute phase increased mortality.
Both excessive and insufficient energy delivery in the rehabilitation phase were associated with higher mortality risk.

Conclusions:

Time-series deep learning models can optimize energy delivery for septic patients in the ICU.
Permissive underfeeding is advised only in the early acute phase.
Increased energy intake in later phases may enhance survival and address energy deficits.