Predicting ventilator-associated pneumonia with machine learning.

Area of Science:

• Critical care medicine
• Medical informatics
• Machine learning applications

Background:

• Ventilator-associated pneumonia (VAP) is a leading cause of mortality in intensive care units (ICUs).
• Current diagnostic methods for VAP often lack accuracy and can delay crucial antimicrobial therapy.
• Machine learning (ML) approaches using electronic health record (EHR) data for VAP diagnosis remain underexplored.

Purpose of the Study:

• To evaluate the performance of various ML models in predicting VAP diagnosis.
• To identify the potential of ML in improving VAP detection within the ICU setting.

Main Methods:

• A retrospective study analyzed EHR data from 6126 adult ICU patients on mechanical ventilation for at least 48 hours.
• Five distinct ML models were developed to predict VAP diagnosis 48 hours post-ventilation initiation.
• Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve.

Main Results:

• The top-performing ML model achieved an AUROC of 0.854.
• Key predictive features included duration of mechanical ventilation, antibiotic administration, sputum test frequency, and Glasgow Coma Scale scores.
• Feature importance was quantified using Shapley values.

Conclusions:

• Supervised ML utilizing EHR data presents a promising avenue for VAP diagnosis.
• Further validation of these ML models is warranted.
• This technology has the potential to facilitate earlier and more accurate VAP diagnosis, improving patient outcomes.