Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques
Authors: de Haro Candelaria , Santos-Pulpón Verónica , Telías Irene , Xifra-Porxas Alba , Subirà Carles , Batlle Montserrat , Fernández Rafael , Murias Gastón , Albaiceta Guillermo M. , Fernández-Gonzalo Sol , Godoy-González Marta , Gomà Gemma , Nogales Sara , Roca Oriol , Pham Tai , López-Aguilar Josefina , Magrans Rudys , Brochard Laurent , Blanch Lluís , Sarlabous Leonardo ,   the BEARDS study investigators, Brochard Laurent , Telias Irene , Damiani Felipe , Artigas Ricard , Santis Cesar , Pham Tài , Mauri Tommaso , Spinelli Elena , Grasselli Giacomo , Spadaro Savino , Volta Carlo Alberto , Mojoli Francesco , Georgopoulos Dimitris , Kondili Eumorfia , Soundoulounaki Stella , Becher Tobias , Weiler Norbert , Schaedler Dirk , Roca Oriol , Santafe Manel , Mancebo Jordi , Rodríguez Nuria , Heunks Leo , de Vries Heder , Chen Chang-Wen , Zhou Jian-Xin , Chen Guang-Qiang , Rit-tayamai Nuttapol , Tiribelli Norberto , Fredes Sebastian , Artigas Ricard Mellado , Ortolá Carlos Ferrando , Beloncle François , Mercat Alain , Arnal Jean-Michel , Diehl Jean-Luc , Demoule Alexandre , Dres Martin , Fossé Quentin , Jochmans Sébastien , Chelly Jonathan , Terzi Nicolas , Guérin Claude , Kassis E. Baedorf , Beitler Jeremy , Chiumello Davide , Bol-giaghi Erica Ferrari Luca , Thille Arnaud W. , Coudroy Rémi , Papazian Laurent 
Published: March 2024

Background

Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients’ ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths.

Methods

Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation.

Results

6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6–88.3], and 86.8% [86.6–87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O.

Conclusions

Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.