Hypothesis-driven modeling of the human lung-ventilator system for Acute Respiratory Distress Syndrome research

Abstract

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the achievable flexibility and fidelity needed to provide individualized clinical support by modeling lung processes. This work proposes a hypothesis-driven strategy for LVS modeling, in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Inference experiments performed on human pressure waveform data indicate the flexible model accurately estimates parameters for a variety of breath types, including breaths of markedly dyssynchronous LVSs. Parameter estimates generate static characterizations of the data that are 50-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpetability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.