Redmond Daniel P, Docherty Paul D, Chase J Geoffrey
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:4532-5. doi: 10.1109/EMBC.2015.7319402.
Patient breathing efforts occurring during controlled ventilation causes perturbations in pressure data, which cause erroneous parameter estimation in conventional models of respiratory mechanics. A polynomial model of patient effort can be used to capture breath-specific effort and underlying lung condition. An iterative multiple linear regression is used to identify the model in clinical volume controlled data. The polynomial model has lower fitting error and more stable estimates of respiratory elastance and resistance in the presence of patient effort than the conventional single compartment model. However, the polynomial model can converge to poor parameter estimation when patient efforts occur very early in the breath, or for long duration. The model of patient effort can provide clinical benefits by providing accurate respiratory mechanics estimation and monitoring of breath-to-breath patient effort, which can be used by clinicians to guide treatment.
在控制通气期间患者的呼吸努力会导致压力数据出现波动，这会在传统呼吸力学模型中导致错误的参数估计。患者努力的多项式模型可用于捕捉特定呼吸的努力情况和潜在的肺部状况。迭代多元线性回归用于在临床容量控制数据中识别该模型。与传统的单室模型相比，在存在患者努力的情况下，多项式模型具有更低的拟合误差以及对呼吸弹性和阻力更稳定的估计。然而，当患者努力在呼吸早期就出现或持续时间较长时，多项式模型可能会收敛到较差的参数估计。患者努力模型可以通过提供准确的呼吸力学估计和逐次呼吸的患者努力监测来提供临床益处，临床医生可利用这些信息来指导治疗。
