Estimating patient spontaneous breathing effort in mechanical ventilation using a b-splines function approach

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS
Qianhui Sun , J. Geoffrey Chase , Cong Zhou , Merryn H. Tawhai , Jennifer L. Knopp , Knut Möller , Geoffrey M. Shaw , Thomas Desaive
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Abstract

Background:

Patient work of breathing is a key clinical metric strongly to guide patient care and weaning from mechanical ventilation (MV). Measurement requires added equipment, well-trained clinicians, or/and extra interventions. This study combines a spontaneous breathing effort model using b-spline functions with a nonlinear, predictive MV digital-twin model to monitor patient effort in real-time.

Methods:

Data from 22 patients for two assisted spontaneous breathing MV modes, NAVA (neurally adjusted ventilatory assist) and PSV (pressure support ventilation), are employed. The patient effort function estimates a pleural pressure Pˆp surrogate of muscular work of breathing induced pressure. To ensure identifiability Pˆp is identified with a negative constraint level of 75%. Estimated patient effort is compared to electrical activity of the diaphragm (EAdi) signals from the NAVA naso-gastric tude, airway pressure, and tidal volume (VT) as well as physiological and clinical expectations.

Results:

Pˆp generalizes well across the digital twin model and MV modes in comparison to the original single compartment lung model. Strong neuro-muscular correlations are identified with Pˆp compared to EAdi, VT, and airway pressure in NAVA. They are lower in PSV, as expected, as pressure delivery is not a function of EAdi in this MV mode, while the uncontrolled variable VT shows a stronger association with Pˆp than EAdi.

Conclusion:

The digital twin model relates patient-specific induced breathing effort, modeled as Pˆp, as well as or better than EAdi in both assisted breathing MV modes. Results differ between NAVA and PSV modes due to the poorer patient–ventilator interaction typical in PSV. The ability to estimate patient work of breathing allows non-invasive, real-time quantification of ventilator unloading, heretofore not possible without extra sensors or maneuvers, to help guide weaning or changes in MV settings for assisted spontaneous breathing (ASB) MV modes.

使用 b-splines 函数法估算机械通气时患者的自主呼吸强度
背景:患者的呼吸功是一项关键的临床指标,主要用于指导患者护理和机械通气(MV)的断奶。测量需要额外的设备、训练有素的临床医生或/和额外的干预措施。本研究将使用 b-spline 函数的自主呼吸工作量模型与非线性、预测性 MV 数字双模型相结合,实时监测患者的工作量。方法:本研究采用了 22 名患者在两种辅助自主呼吸 MV 模式 NAVA(神经调节通气辅助)和 PSV(压力支持通气)下的数据。患者努力功能可估算胸膜压力 Pˆp 代替呼吸诱导压力的肌肉功。为确保可识别性,Pˆp 以 75% 的负约束水平进行识别。结果:与原始单腔肺模型相比,Pˆp 在数字孪生模型和 MV 模式中具有良好的通用性。在 NAVA 中,与 EAdi、VT 和气道压力相比,Pˆp 与神经肌肉有很强的相关性。结论:在两种辅助呼吸 MV 模式中,数字孪生模型与患者特异性诱导呼吸努力(建模为 Pˆp)的关系与 EAdi 的关系一样好,甚至更好。NAVA 和 PSV 模式的结果有所不同,这是因为 PSV 模式中患者与呼吸机之间的相互作用较弱。估算患者呼吸功的能力允许对呼吸机卸载进行无创、实时量化,这在没有额外传感器或操作的情况下是不可能实现的,有助于指导辅助自主呼吸(ASB)MV 模式的断奶或 MV 设置的改变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IFAC Journal of Systems and Control
IFAC Journal of Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
3.70
自引率
5.30%
发文量
17
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