Patient-ventilator asynchrony unraveled: optimization of data acquisition and analysis

Introduction: Nocturnal noninvasive mechanical ventilation has been proven beneficial for patients with end-stage chronic obstructive pulmonary disease. Asynchronies between the patient's respiratory effort and the ventilator's assistance may interfere with treatment efficiency. It is still unclear which technique among the existing modalities is most suitable for monitoring and quantifying patient-ventilator asynchronies (PVA) in a clinical setting.

Aims and objectives: The main goal of this study is to develop an algorithm to automatically detect PVA using synchronous data and compare methods to detect PVA using 1) only ventilator data, 2) ventilator data with surface electromyography (sEMG) of the diaphragm, and 3) ventilator data with the patient’s effort from respiratory inductance plethysmography (RIP).

Methods: The preliminary data of the first 3 patients admitted to the respiratory care unit of the UMC Groningen for the initiation of noninvasive ventilation are presented. The measurements include continuous data of the ventilator, RIP of the thorax and abdomen, and sEMG of the diaphragm. All nocturnal data are synchronously acquired using a polysomnography device.

Results: The preliminary data demonstrate that synchronous data acquisition from all modalities is possible through one synchronous interface. PVA are detected based on the analysis of waveforms and reveal different results when comparing monitoring modalities.