Machine Learning Algorithms to Detect Patient-Ventilator Asynchrony. A Feasibility Study.

Abstract Background: Adequate ventilatory support requires frequent assessment of patient-ventilator interactions. It is desirable, therefore, to develop a reliable, automated method for this task. This study evaluates the feasibility of developing machine-learning algorithms to emulate how experienced clinicians evaluate normal and abnormal breathing patterns, including patient-ventilator asynchrony. Methods: We enrolled 44 adult patients within 24 hours of initiating invasive mechanical ventilation. Airway flow and pressure signals were acquired directly from the ventilator and stored as sequential 2.2-minute epochs for waveform classification. Experienced clinicians visually classified 50,712 epochs, encompassing approximately 2.6 million breathing cycles. Nineteen clinical variables were used to train four Random Forest algorithms to: 1) detect asynchronous breathing, 2) identify asynchrony type, 3) grade signal disruption, and 4) identify dynamic hyperinflation. Algorithm accuracy was assessed by the percentage of correctly identified epochs, while clinical reliability was evaluated by comparing the algorithms’ predictions to those of clinicians with varying experience in asynchrony classification. Results: The algorithm detected asynchronous breathing with 91% accuracy. Accuracies for asynchrony classification, severity grading, and dynamic hyperinflation were 82%, 87%, and 93%, respectively. Algorithm classifications aligned more closely with expert clinicians (kappa = 0.46, and 0.59) than non-experts (kappa = 0.25, and 0.38; p < 0.05). Greater time asynchronous was associated with increased 28-day mortality (p = 0.015). Conclusions: Machine-learning algorithms may be trained to emulate experienced clinicians in evaluating breathing during mechanical ventilation. Larger databases and advancements in artificial intelligence may lead to powerful algorithms capable of establishing associations between airway signals and successful ventilatory support.