Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation
Background: Patient-ventilator asynchrony is common during pressure support ventilation (PSV) because of the constant cycling-off criteria and variation of respiratory system mechanical properties in individual patients. Automatic adjustment of inspiratory triggers and cycling-off criteria based on...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:e400f54f08df47dca3cbfd668a49f3f02021-11-12T05:09:00ZAutomatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation2296-858X10.3389/fmed.2021.752508https://doaj.org/article/e400f54f08df47dca3cbfd668a49f3f02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.752508/fullhttps://doaj.org/toc/2296-858XBackground: Patient-ventilator asynchrony is common during pressure support ventilation (PSV) because of the constant cycling-off criteria and variation of respiratory system mechanical properties in individual patients. Automatic adjustment of inspiratory triggers and cycling-off criteria based on waveforms might be a useful tool to improve patient-ventilator asynchrony during PSV.Method: Twenty-four patients were enrolled and were ventilated using PSV with different cycling-off criteria of 10% (PS10), 30% (PS30), 50% (PS50), and automatic adjustment PSV (PSAUTO). Patient-ventilator interactions were measured.Results: The total asynchrony index (AI) and NeuroSync index were consistently lower in PSAUTO when compared with PS10, PS30, and PS50, (P < 0.05). The benefit of PSAUTO in reducing the total AI was mainly because of the reduction of the micro-AI but not the macro-AI. PSAUTO significantly improved the relative cycling-off error when compared with prefixed controlled PSV (P < 0.05). PSAUTO significantly reduced the trigger error and inspiratory effort for the trigger when compared with a prefixed trigger. However, total inspiratory effort, breathing patterns, and respiratory drive were not different among modes.Conclusions: When compared with fixed cycling-off criteria, an automatic adjustment system improved patient-ventilator asynchrony without changes in breathing patterns during PSV. The automatic adjustment system could be a useful tool to titrate more personalized mechanical ventilation.Ling LiuYue YuXiaoting XuQin SunHaibo QiuDavide ChiumelloDavide ChiumelloDavide ChiumelloYi YangFrontiers Media S.A.articleautomatic adjustment systempressure support ventilationpatient-ventilator asynchronycycling-offtriggerMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021) |
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automatic adjustment system pressure support ventilation patient-ventilator asynchrony cycling-off trigger Medicine (General) R5-920 |
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automatic adjustment system pressure support ventilation patient-ventilator asynchrony cycling-off trigger Medicine (General) R5-920 Ling Liu Yue Yu Xiaoting Xu Qin Sun Haibo Qiu Davide Chiumello Davide Chiumello Davide Chiumello Yi Yang Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
description |
Background: Patient-ventilator asynchrony is common during pressure support ventilation (PSV) because of the constant cycling-off criteria and variation of respiratory system mechanical properties in individual patients. Automatic adjustment of inspiratory triggers and cycling-off criteria based on waveforms might be a useful tool to improve patient-ventilator asynchrony during PSV.Method: Twenty-four patients were enrolled and were ventilated using PSV with different cycling-off criteria of 10% (PS10), 30% (PS30), 50% (PS50), and automatic adjustment PSV (PSAUTO). Patient-ventilator interactions were measured.Results: The total asynchrony index (AI) and NeuroSync index were consistently lower in PSAUTO when compared with PS10, PS30, and PS50, (P < 0.05). The benefit of PSAUTO in reducing the total AI was mainly because of the reduction of the micro-AI but not the macro-AI. PSAUTO significantly improved the relative cycling-off error when compared with prefixed controlled PSV (P < 0.05). PSAUTO significantly reduced the trigger error and inspiratory effort for the trigger when compared with a prefixed trigger. However, total inspiratory effort, breathing patterns, and respiratory drive were not different among modes.Conclusions: When compared with fixed cycling-off criteria, an automatic adjustment system improved patient-ventilator asynchrony without changes in breathing patterns during PSV. The automatic adjustment system could be a useful tool to titrate more personalized mechanical ventilation. |
format |
article |
author |
Ling Liu Yue Yu Xiaoting Xu Qin Sun Haibo Qiu Davide Chiumello Davide Chiumello Davide Chiumello Yi Yang |
author_facet |
Ling Liu Yue Yu Xiaoting Xu Qin Sun Haibo Qiu Davide Chiumello Davide Chiumello Davide Chiumello Yi Yang |
author_sort |
Ling Liu |
title |
Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
title_short |
Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
title_full |
Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
title_fullStr |
Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
title_full_unstemmed |
Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation |
title_sort |
automatic adjustment of the inspiratory trigger and cycling-off criteria improved patient-ventilator asynchrony during pressure support ventilation |
publisher |
Frontiers Media S.A. |
publishDate |
2021 |
url |
https://doaj.org/article/e400f54f08df47dca3cbfd668a49f3f0 |
work_keys_str_mv |
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