Minimal sampling schedule for identification of dynamic models of metabolic systems of clinical interest: case studies for two liver function tests

The parameter estimation problem for dynamic system models designed for diagnostic use is often a difficult one, due to severe practical constraints imposed on the clinical procedure. This is particularly true in many endocrine and metabolic system studies, where blood sampling provides the basic data, and the number of samples or the observation interval must be minimized. We have applied some recent developments in optimal sampling schedule design, in two case studies for two different liver function tests, to determine minimum size optimal schedules for these tests. We have assessed the effects of these schedules on parameter estimation accuracy and reliability, compared with nonoptimal schedules 2–5 times larger in number, using a Monte Carlo simulation approach. The results are encouraging, as they indicate this approach to be a practical and efficient alternative to more conventional ones.