Establishment and Application of a Patient-Ventilator Asynchrony Remote Network Platform for ICU Mechanical Ventilation: A Retrospective Study

Longxiang Su, Yunping Lan, Yi Chi, Fuhong Cai, Zhenfeng Bai, Xianlong Liu, Xiaobo Huang, Song Zhang, Yun Long, Longxiang Su, Yunping Lan, Yi Chi, Fuhong Cai, Zhenfeng Bai, Xianlong Liu, Xiaobo Huang, Song Zhang, Yun Long

Abstract

Background: In the process of mechanical ventilation, the problem of patient-ventilator asynchrony (PVA) is faced. This study proposes a self-developed remote mechanical ventilation visualization network system to solve the PVA problem.

Method: The algorithm model proposed in this study builds a remote network platform and achieves good results in the identification of ineffective triggering and double triggering abnormalities in mechanical ventilation.

Result: The algorithm has a sensitivity recognition rate of 79.89% and a specificity of 94.37%. The sensitivity recognition rate of the trigger anomaly algorithm was as high as 67.17%, and the specificity was 99.92%.

Conclusions: The asynchrony index was defined to monitor the patient's PVA. The system analyzes real-time transmission of respiratory data, identifies double triggering, ineffective triggering, and other anomalies through the constructed algorithm model, and outputs abnormal alarms, data analysis reports, and data visualizations to assist or guide physicians in handling abnormalities, which is expected to improve patients' breathing conditions and prognosis.

Keywords: ICU mechanical ventilation; double triggering; ineffective triggering; patient-ventilator asynchrony; remote network platform.

Conflict of interest statement

The authors declare that they have no competing interests. The authors and the Shanghai Shumu Medical Technology Co., Ltd. are cooperative and have no conflicts of interest.

Figures

Figure 1
Figure 1
Remote-VentlateView platform architecture.
Figure 2
Figure 2
Remote-VentilateView network topology.
Figure 3
Figure 3
Flow chart of mechanical ventilation event alarm.
Figure 4
Figure 4
Data abnormal waveform. (a) Double triggering waveform. (b) Ineffective triggering waveform.
Figure 5
Figure 5
Double triggering data analysis report.

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Source: PubMed

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