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Propofol-associated Hypertriglyceridemia: Development and Multicenter Validation of a Machine-Learning-Based

Jiawen Deng¹, Kiyan Heybati², Keshav Poudel²

¹Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.

Journal of Intensive Care Medicine

|May 15, 2025

Summary

This summary is machine-generated.

An explainable machine learning tool predicts propofol-associated hypertriglyceridemia risk in critically ill patients. This aids clinicians in monitoring triglycerides and optimizing sedation, improving patient care.

Keywords:

critical care hypertriglyceridemia machine-learning mechanical ventilation propofol sedation

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Area of Science:

Critical Care Medicine
Machine Learning in Healthcare
Pharmacology

Background:

Propofol sedation is common in intensive care units (ICUs).
Propofol-associated hypertriglyceridemia is a potential complication in critically ill patients.
Predictive tools are needed to identify patients at risk.

Purpose of the Study:

To develop and validate an explainable machine learning (ML) tool.
To predict the risk of propofol-associated hypertriglyceridemia.
To aid clinicians in risk assessment and management.

Main Methods:

Retrospective analysis of 3922 ICU patients from Mayo Clinic hospitals.
Development of COVID-inclusive and COVID-independent ML models.
External validation using nested leave-one-site-out cross-validation (LOSO-CV).
Model explainability assessed using permutation importance and SHAP values.

Main Results:

19.6% of patients developed hypertriglyceridemia within 10 days of propofol initiation.
COVID-inclusive model achieved an AUC-ROC of 0.71; COVID-independent model achieved 0.69.
Key predictors included age, initial propofol dose, and BMI.

Conclusions:

An explainable ML tool was developed with acceptable predictive performance.
The tool can assist clinicians in identifying high-risk patients for propofol-associated hypertriglyceridemia.
This facilitates targeted triglyceride monitoring and optimized sedative selection.