Automated Detection of Patient Ventilator Asynchrony Using Pes Signal A Feasibility Study Towards a Detection Algorithm

1. INTRODUCTION AND RATIONALE Mechanical ventilation should unload the respiratory muscles, provide adequate gas exchange and should be safe, i.e., harm due to mechanical ventilation should be reduced to a minimum. To achieve this the interaction between the ventilator and the patient is preferentially synchronous. Ventilator settings not being synchronized with patient respiratory drive or activity is a phenomenon known as patient-ventilator asynchrony (PVA). PVA may induce several deleterious effects.1 Studies have shown asynchronies to be associated with patient discomfort, increased work of breathing, prolonged weaning, and in one study, even increased mortality.

Monitoring PVA however is difficult. Clinicians often have to rely on physical examination of the patient as well as visual inspection of pressure, flow and volume waveforms to identify an asynchrony.6 The sensitivity and positive predictive value of analyzing breath-to-breath waveforms are very low (22% and 32%, respectively).1 Artifacts such as cardiac oscillation may mimic asynchronies, and there are times when clinicians standing at the bedside are unable to distinguish between asynchronies and artifacts with certainty.6 Furthermore, detection of PVA is dependent of bedside examination. This challenge leads to the desire of developing effective automated PVA recognition algorithms.7 Various automated algorithms have been developed, however with a variable performance.1 For a correct analysis of asynchronies, the use of an esophageal balloon catheter, which measures the esophageal pressure (Pes), or a catheter which measures the electrical activity of the diaphragm, is necessary.1 Since the use of Pes catheters, it is possible to describe other forms of PVA, such as reverse triggering, which is an asynchrony in which the ventilator triggers the patient.8 Until recently, however, the esophageal catheter has not been routinely used in daily practice but more as a research tool. Since the introduction of personalized medicine, clinicians have gained interest in esophageal manometry to better titrate care to the unique physiology of a patient.9 There are in the current literature no reports of PAV detection algorithms that use the Pes signal for detection.

In the LUMC the investigators use the esophageal catheter in all patients admitted with acute respiratory failure and in patients ventilated for more than 48 hours per protocol. The esophageal signal gives the opportunity to detect asynchronies more easily than without. The investigators therefore hypothesize that an algorithm based on the esophageal signal will perform better than an algorithm that only uses other ventilator waveforms. 2. OBJECTIVES 2.1 Primary Objective The primary objective of this study is to develop an asynchrony detection algorithm based on pressure, flow and Pes signals of patient data using ML.

2.2 Secondary Objectives Secondary objectives are to validate the detection algorithm by comparing its performance with the assessment of the expert panel and to compare its performance with the performance of a second algorithm which is based on pressure- and flow signals only. 3. STUDY DESIGN This study will take place at the Intensive Care Unit of the LUMC. First, internal data of adult ICU patients on mechanical ventilation because of acute respiratory failure or with a ventilation duration of at least 24 hours and that are equipped with an esophageal balloon catheter will be collected from the ventilator. The data of interest include pressure, flow and Pes signals of the ventilation. It is necessary to collect as much data as possible as this is required for the development of the algorithm. A minimum of 50 patients will be included with from each a ventilation recording between 4 and 8 hours, which amounts to 200 - 400 hours of mechanical ventilation recording.

The following labels will be assigned to the data: