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Session: Respiration & Pregnancy
Session starts: Thursday 26 January, 10:30
Presentation starts: 10:30
Room: Room 531

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Tom Bakkes (Eindhoven University of Technology)
Anouk van Diepen (Eindhoven University of Technology)
Ashley de Bie (Catharina Ziekenhuis Eindhoven)
Massimo Mischi (Eindhoven University of Technology)
Pierre Woerlee (Eindhoven University of Technology)
Simona Turco (Eindhoven University of Technology)

Abstract:
Patient-ventilator asynchrony (PVA) is a hard-to-catch adverse event in mechanically ventilated critically ill patients and is associated with increased mortality [1]. There is a need for automated detection of asynchronies since current solutions are suboptimal. In previous research, a method for the automated detection of patient-ventilator asynchrony (PVA) was developed [2]. Proper validation of these methods is difficult because this requires independent datasets containing accurately labeled PVAs. However, these datasets are difficult to obtain, since labeling the data is labor-intensive, time-consuming, and prone to human errors. In contrast, simulating ventilation data allows for the rapid generation of accurately labeled data and comprehensive datasets. In this study, we investigated the feasibility of obtaining accurately labeled ventilation data from simulations, and the generalizability of the previously developed method to this data. A simulator developed in [3] was used to obtain the simulated data. In total, the simulation generated 64898 breathing cycles. The clinical data was the same data utilized in previous research, which consisted of 15 patients and contained 4275 breathing cycles [2]. The model was trained and tested in four different approaches. First, we performed cross-validation as in [2] with only clinical data. Second, the model was trained on clinical data and tested on simulated data. Third, the model was trained on simulated data and tested on clinical data. Finally, the model was cross-validated on clinical data but the training was supplemented with simulated data. The resulting precision and recall were respectively 97.5% and 98.0% for the first approach, 94.3% and 93.5% for the second approach, 94.5% and 97.8% for the third approach, and 97.6% and 98.3% for the Fourth approach. The overall performance was excellent, with the second and third approaches performing remarkably high, almost similar to the first and fourth approaches in which the algorithms were both trained and tested on clinically labeled data. The second and third approaches confirm that the simulations generate accurate ventilation data for which the detection can be easily generalized to different datasets. This approach with simulated datasets will therefore help to accurately detect PVAs in real-time at the bedside of the patients. [1] C. de Haro et. Al., Intensive care Med Exp. (2019) [2] T. Bakkes et. Al., EMBC. (2020)