Generalization of map estimation in SAAM II: Validation against ADAPT II in a glucose model case study

Tiziano Callegari, Andrea Caumo, Claudio Cobelli

Research output: Contribution to journalArticlepeer-review

Abstract

Bayesian approaches to model indentification [e.g., maximum a posteriori (MAP) estimation] are receiving increasing attention in metabolism since important quantitative knowledge has become available in the last decades, e.g., from tracer experiments. By suitably exploiting this knowledge, more complex physiological models than those solely based on experimental data (Fisherian approach) become resolvable. While ADAPT II is the reference software for MAP estimation in pharmacokinetic/phasmacodynamic/metabolic system analysis, another popular, user-friendly and state-of-the-art software in SAAM II. However, SAAM II does not handle a priori information on correlation among parameters, thus allowing a limited version of MAP estimation to be performed. The aim here is twofold. First, we show that this limitation of SAAM II can be easily overcome by resorting to a probability theory result. Second, we test SAAM II vs ADAPT II implementation of MAP estimation in a real case study: the Bayesian identification of a recently proposed two-compartment minimal model of glucose kinetics during an intravenous glucose tolerance test. SAAM II MAP estimates of glucose effectiveness (SG) and insulin sensitivity (SI) obtained in a group of 22 healthy humans are in excellent agreement with those of ADAPT II: SG = 2.84±0.27 vs. 2.84±0.27 (ml min-1 kg-1, mean±SD) and SI=11.46±1.69 vs. 11.47±1.69 [10-2 ml kg-1 min-1/(μU ml-1)]. The SAAM II vs. ADAPT II estimates are virtually identical (P>0.44 and 0.68 for SG and SI, respectively) and also closely correlated (ρ= 0.9998 and 0.9999).

Original languageEnglish
Pages (from-to)961-968
Number of pages8
JournalAnnals of Biomedical Engineering
Volume30
Issue number7
DOIs
Publication statusPublished - Jul 2002

Keywords

  • Bayes estimation
  • Glucose kinetics
  • Maximum a posteriori estimation
  • Parameter estimation

ASJC Scopus subject areas

  • Biomedical Engineering

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