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The complexity of mathematical models describing respiratory mechanics hasgrown in recent years to integrate with cardiovascular models and incorporatenonlinear dynamics. However, additional model complexity has rarely beenstudied in the context of patient-specific observable data. This studyinvestigates parameter identification of a previously developed nonlinearrespiratory mechanics model (Ellwein Fix, PLoS ONE 2018) tuned to thephysiology of 1 kg preterm infant, using local deterministic sensitivityanalysis, subset selection, and gradient-based optimization. The model consistsof 4 differential state equations with 31 parameters to predict airflow anddynamic pulmonary volumes and pressures generated under six simulationconditions. The relative sensitivity solutions of the model state equationswith respect to each of the parameters were calculated with finite differencesand a sensitivity ranking was created for each parameter and simulation. Subsetselection identified a set of independent parameters that could be estimatedfor all six simulations. The combination of these analyses produced a subset of6 independent sensitive parameters that could be estimated given idealizedclinical data. All optimizations performed using pseudo-data with perturbednominal parameters converged within 40 iterations and estimated parameterswithin ~8% of nominal values on average. This analysis indicates thefeasibility of performing parameter estimation on real patient-specific dataset described by a nonlinear respiratory mechanics model for studying dynamicsin preterm infants.

Parameter identifiability of a respiratory mechanics model in an idealized preterm infant