【摘要】 智能通气闭环控制中的呼吸系统生理模型必须符合患者的实际情况，且模型的参数估计必须实时、有效。利用约束优化的参数估计方法，筛选既反映慢性阻塞性肺疾病（慢阻肺）患者病理条件，同时又能用于实时估计的模型。本研究利用数值模拟方法和临床数据，对不同复杂程度的呼吸力学模型进行参数估计，并利用估计结果对口端压力进行重建，利用估计结果对比和重建结果对比的规律，找到适用于机械通气慢阻肺呼吸力学问题的数学描述。针对4种不同复杂程度的模型，建立了相应的参数估计方法；并利用模拟数据和临床数据，获得不同模型应用参数估计方法的结果。其中利用阻力和弹性的估计参数重建的口端压力与输入的口端压力对比，对于模拟数据，弹性非线性和阻力非线性模型相较于其他模型，均方根误差最小；对于6例临床病例的12个呼吸周期数据，阻力非线性模型的均方根误差（均值=1.55 cmH₂O,标准差=0.69 cmH₂O）最小；重复测量方差分析的多重比较结果显示，阻力非线性模型在统计上显著优于其他模型（P<0.05）。综合所有结果显示，阻力随容积变化的非线性模型是慢阻肺机械通气下更为合适的表达模型备选。在应用约束优化方法进行机械通气实时参数估计的基础上，阻力非线性模型更能够反映慢阻肺的呼吸力学特性。研究结果不但能用于慢阻肺的呼吸力学监测，还可以为呼吸机的智能化参数设置和调节研究提供基础。更多还原

【Abstract】 In intelligent closed-loop ventilation control, the physiological model of the respiratory system must be consistent with the patient′s actual condition, and its parameter estimation must be real-time and effective. Using the parameter estimation method of constrained optimization, models that can reflect the pathological conditions of chronic obstructive pulmonary disease（COPD） patients as well as can be used for real-time estimation were compared and verified. In this study, we used numerical simulation data and clinical data to estimate parameters of respiratory mechanics models with different complexities. The estimated results were used to reconstruct airway pressure. By comparing the estimation results and reconstruction results among different models, the mathematical description of respiratory mechanics problems suitable for mechanical ventilation COPD was selected. For four models with the different complexities, the corresponding parameter estimation methods were established. By using the simulation and clinical data, a constrained optimization parameter estimation method was applied to different models. Comparative analysis between the reconstructed airway pressure（using estimated resistance and elasticity） and input airway pressure demonstrated that the elastic nonlinear and resistance nonlinear models exhibited the smallest root mean square error（RMSE） in simulation data. For the 12 respiratory cycle data of 6 clinical cases, the resistance nonlinear model achieved the lowest RMSE（M=1.55 cmH₂O, SD=0.69 cmH₂O） among the evaluated models. The multiple comparison results of repeated measures ANOVA showed that the resistance nonlinear model statistically significantly outperformed the other models（P<0.05）. The results showed that according to the simulation data and clinical data in this study, the resistance nonlinear model was more appropriate model of ventilated COPD. Based on the application of constraint optimization method for real-time parameter estimation of mechanical ventilation, the resistance nonlinear model reflected the respiratory mechanics characteristics of COPD better. The results can be used for monitoring respiratory mechanics of COPD, and also provide a basis for the intelligent parameter setting and setting of ventilators.更多还原