Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Mechanical Engineering & Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
Biomed Eng Online. 2023 Oct 24;22(1):102. doi: 10.1186/s12938-023-01165-0.
Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop.
Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.
The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.
The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.
在重症监护病房（ICU）进行机械通气（MV）时，患者-呼吸机失同步很常见，导致 MV 治疗结果更差。识别失同步对于优化 MV 设置以减少或消除失同步至关重要，而目前对所有典型类型的异步呼吸进行临床视觉检查既困难又低效。患者失步会在压力-容积（PV）环中呈现出滞后呼吸行为的独特扭曲模式。
提出了一种基于滞后肺力学和滞后环分析的识别方法，以描绘 PV 环中滞后呼吸期间肺力学的变化，从而检测其发生率和 7 种主要类型。将性能与临床患者数据进行测试，并与临床医生进行的视觉检查进行比较。
针对 11 名患者的 500 次呼吸，每种患者的 7 种类型的识别灵敏度和特异性均高于 89.5%和 96.8%。所有病例的平均灵敏度和特异性分别为 94.6%和 99.3%，表明准确性非常高。识别和人工检查之间的统计分析比较表明，对每位患者的患者失步状态的临床判断基本相同，这可能会导致对设置调整的相同临床决策。
总体结果验证了该识别方法用于床边监测的准确性和稳健性，以及为呼吸机设置的临床决策提供量化指标的能力。因此，该方法显示出在不影响当前临床实践效果的情况下，辅助更一致和客观评估失步的潜力。
