Structural identifiability and practical applicability of an alveolar recruitment model for ARDS patients
Mathematical models of respiratory mechanics can offer substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus, they offer significant potential to evaluate and guide patient-specific lung protective ventilator strategies for Acute Respiratory Distress Syndrome (ARDS) patients. To assure bedside-applicability, the model has to be computationally efficient and identifiable from the available data, while also capturing dominant dynamics observed in ARDS patients. In this work, a recruitment model is enhanced by considering alveolar distension and implemented in a time-continuous respiratory mechanics model. A hierarchical gradient decent approach is used to fit the model to low-flow test responses of 12 ARDS patients. The reported parameter values were physiologically plausible and capable of reproducing the measured pressure responses with high accuracy. Structural identifiability of the model is proven, but a practical identifiability analysis of the results shows a lack of convexity on the error-surface. Covariance analyses reveal limited influence of particular model parameters during parameter identification indicating that successful parameter identification is currently not assured in all test sets. Overall, the presented model is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show its ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics can currently not be directly measured or established without such a validated model.