Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: promise and reality.

This paper reviews and critically appraises the application of compartmental models for generating glycaemic predictions and offering clinical decision support in diabetes care. Comparisons are made with alternative algorithmic-based approaches. Unresolved issues raised for model-based techniques include the relative lack of input data necessary for generating reasonable blood glucose predictions, and the high level of uncertainty associated with such predictions which limits their use as guides for therapeutic insulin-dosage adjustments. It is concluded that compartmental model-based approaches, while not offering much benefit for clinical/therapeutic application, will have a role to play as research tools and for educational use. By contrast it is proposed that algorithmic-based approaches, especially in conjunction with telemedicine and Internet applications, are likely to see growing use for day-to-day therapeutic decision support. Randomised controlled clinical trials however will be required, together with other evaluation efforts, before algorithmic-based approaches-like any other clinical technique-can be widely adopted into routine medical practice.