标题： 极早产婴儿呼吸力学的实际参数可辨识性 显示英文标题

标题： Practical parameter identifiability of respiratory mechanics in the extremely preterm infant

摘要： 近年来，描述呼吸力学的数学模型的复杂性有所增加，然而，这类模型的参数可辨识性仅在过去十年中在可观测数据的背景下进行了研究。 本研究利用全局Morris筛选、局部确定性敏感性分析和基于奇异值分解的子集选择方法，研究了一个针对极早产婴儿生理学调整的非线性呼吸力学模型的参数可辨识性。 该模型预测在不同水平的持续气道正压下以及一系列描述表面活性物质处理和缺乏的肺部参数下的气流和动态肺容积及压力。 敏感性分析表明，在持续气道正压和肺部健康状况的不同范围内，有十一个参数影响模型输出。 该模型通过基于梯度的优化方法适应了一名自主呼吸的1公斤婴儿的数据，以估计描述患者健康状态的参数子集。 显示英文摘要

摘要： The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis, and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. Sensitivity analyses indicated eleven parameters influence model outputs over the range of continuous positive airway pressure and lung health scenarios. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.