Rationale: Patient-ventilator asynchrony (PVA) in mechanical ventilation is associated with adverse patient outcome such as a prolonged stay in the ICU and even mortality. The prevalence of asynchronies is, however, difficult to quantify. It is common to use only the pressure and flow signal of the ventilator to detect asynchronies. The detection method is often based on definitions. The investigators will use new techniques (esophageal pressure signal and machine learning (ML)) to improve detection and quantification of patient-ventilator asynchronies. The hypothesis is that an algorithm which uses the Pes signal and ML to detect and quantify asynchronies is superior to previous techniques. Objective: 1. To develop an asynchrony detection algorithm based on pressure, flow and Pes signal using ML. 2. To develop a second algorithm with the same ML technique based on pressure an flow signal only. 3. To compare the performance of these models in comparison with an expert team and with each other. Study design: The investigators will collect internal data from the ventilator connected to patients on mechanical ventilation (population described below). First, the investigators will, with a dedicated expert team, identify and annotate the asynchronies based on visual inspection of the pressure, flow and Pes signal. Second, the investigators will develop an ML algorithm which will be trained with the annotated data from the visual inspection. Third, the performance of the AI algorithm will be compared with the performance of the expert panel using newly obtained data. Fourth, the performance of the AI algorithm will be compared with the second algorithm which uses the pressure and flow signal only. Study population: All patients admitted to the adult ICU of the LUMC on mechanical ventilation who are ventilated \> 24 hours and are equipped with an esophageal balloon catheter. Intervention (if applicable): None. Main study parameters/endpoints: The performance of the detection algorithm.
Age range
18 Years – 100 Years
Sex
ALL
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Performance of detection algorithm
Timeframe: 8 hours