Identifiability Analysis of a Pressure-Depending Alveolar Recruitment Model

Abstract

Patient-specific physiological models of respiratory mechanics can offer insight into patient state and pulmonary dynamics that are not directly measurable. Thus, significant potential exists to evaluate and guide patient-specific lung protective ventilator strategies for Acute Respiratory Distress Syndrome (ARDS) patients. To assure bedside-applicability, the physiological model must be computationally efficient and identifiable from the limited available data, while also capturing dominant dynamics and trends observed in ARDS patients. In this work, an existing static recruitment model is enhanced by considering alveolar distension and implemented in a novel time-continuous dynamic respiratory mechanics model. A hierarchical gradient descent approach is used to fit the model to low-flow test responses of 12 ARDS patients. Identified parameter values were physiologically plausible and capable of reproducing the measured pressure responses with very high accuracy (Overall median percentage fitting error: MPE = 1.84% [IQR: 1.77% to 2.18%]). Structural identifiability of the model is proven, but a practical identifiability analysis of the results shows a lack of convexity on the error-surface for some patients due to reduced information content within the measured data set. Overall, the model presented is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show their ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics cannot be directly measured without such a validated model.