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Use of Machine Learning Models to Predict Microaspiration Measured by Tracheal Pepsin A.

Annette Bourgault¹, Ilana Logvinov², Chang Liu³

¹Annette Bourgault is an associate professor in Nursing, University of Central Florida College of Nursing, Orlando.

American Journal of Critical Care : an Official Publication, American Association of Critical-Care Nurses

|December 31, 2024

Summary

Tracheal pepsin A indicates microaspiration and may predict enteral feeding intolerance. Machine learning identified key predictors, aiding early intervention for at-risk patients.

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Area of Science:

Critical care medicine
Gastroenterology
Biomarker discovery

Background:

Enteral feeding intolerance is common and linked to higher mortality.
Microaspiration, indicated by tracheal pepsin A, occurs frequently in patients on mechanical ventilation.
Tracheal pepsin A is a potential biomarker for enteral feeding intolerance.

Purpose of the Study:

To identify predictors of microaspiration using tracheal or oral pepsin A.
To determine if predictors of tracheal pepsin A overlap with those of enteral feeding intolerance.

Main Methods:

Machine learning models (random forest, XGBoost, SVM) were applied to data from 283 ventilated adults.
Tracheal and oral aspirates were analyzed for pepsin A levels over 14 days.
Predictors were identified using 5-fold cross-validation.

Main Results:

The random forest model demonstrated high performance for predicting tracheal pepsin A (AUC 0.844).
The top 20 predictors of tracheal pepsin A were successfully identified.
Four predictors of tracheal pepsin A were also linked to enteral feeding intolerance.

Conclusions:

Tracheal pepsin A shows promise as a biomarker for enteral feeding intolerance.
Machine learning effectively identified predictors of microaspiration.
Identifying at-risk patients can facilitate timely treatment for enteral feeding intolerance.