Identifiability as a Crucial Step for Using 0D Models to Derive Deeper Physiological Insights: An Application to Neonatal Cardiovascular Modelling

Models which are identifiable both replicate clinical data and have well-determined parameters and predictions.In the context of model personalisation to patientspecific data, identifiability is crucial if estimated parameters are to be representative of the individual.We collected ultrasound data for term and late preterm babies at two time points to inform personalised 0D closed-loop cardiovascular models.Models were reduced in an iterative manner until all estimated parameters were identifiable, as demonstrated by sensitivity analysis and Markov Chain Monte Carlo analysis.There were no differences in ultrasound measures of cardiovascular structure or function between the term and preterm groups.Although estimated model parameters were similar for the term and preterm group at birth, by three to six weeks of age, the preterm group had significantly higher lower body resistance compared to the term group.As this parameter was shown to be practically identifiable, it is valid to use it to make inferences about the underlying physiology and suggests possible impaired vascular elastogenesis in those born preterm.This provides an example of the way in which identifiable computational modelling can provide further insight into underlying pathophysiological mechanisms in cardiovascular remodelling related to prematurity.