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Bakkes, T., van Diepen, A., De Bie, A., Montenij, L., Mojoli, F., Bouwman, A., Mischi, M., Woerlee, P., Turco, S. (2023) Automated Detection and Classification of Patient-Ventilator Asynchrony by Means of Machine Learning and Simulated Data. Computer Methods and Programs in Biomedicine, 230, 107333.
https://doi.org/10.1016/j.cmpb.2022.107333

has been cited by the following article:

• TITLE: Patient-Ventilator Asynchrony and Duration of Mechanical Ventilation: An Observational Study

  AUTHORS: Cristiano Gomes da Silva, Alexandre Rosa da Silva, Samantha Sabino, Kenya Rodrigues Marques, Maria Aparecida Marques Alves, Ewerton Rocha dos Santos, Matheus Tojado dos Santos, Nildo Campos Rangel

  KEYWORDS: Mechanical Ventilation, Asynchrony, Intensive Care, Physiotherapy

  JOURNAL NAME: Open Journal of Therapy and Rehabilitation, Vol.14 No.3, August 21, 2026

  ABSTRACT: Patient-ventilator asynchrony (PVA) is defined as a mismatch between the patient’s respiratory demand and the ventilatory support delivered during mechanical ventilation (MV). Although common, PVA is associated with adverse outcomes, including prolonged MV and increased morbidity. Methods: This cross-sectional observational study included adult ICU patients receiving invasive mechanical ventilation for more than 24 hours. PVA was assessed through visual analysis of pressure-time, flow-time, and volume-time waveforms recorded in two-minute videos. The asynchrony index (AI) was calculated and classified as Results: Eighty-three patients were included. PVA was identified in 21.7% of patients, with 14.5% presenting AI ≥ 10%. Patients with PVA had longer MV duration (18.5 vs. 8.0 days; p Conclusion: PVA is frequent and associated with prolonged mechanical ventilation. These findings highlight the importance of systematic monitoring and optimization of ventilatory support.