Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation

Authors:
Ang Wang;Mahdi Mahfouf;Gary H. Mills;G. Panoutsos;D. A. Linkens;K. Goode;Hoi-Fei Kwok;Mouloud Denaï
Affiliations:
Process Automation, ABB Limited, Howard Road, Eaton Socon, Cambridgeshire PE19 8EU, UK;Department of Automatic and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Critical Care and Anaesthesia, Northern General Hospital, Herries Road, Sheffield S5 7AU, UK;Department of Automatic and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Automatic and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Postgraduate Medical Institute, The University of Hull, Cottingham Road, Hull HU6 7RX, UK;School of Psychology, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;Process Automation, ABB Limited, Howard Road, Eaton Socon, Cambridgeshire PE19 8EU, UK
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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Abstract

The optimisation of ventilatory support is a crucial issue for the management of respiratory failure in critically ill patients, aiming at improving gas exchange while preventing ventilator-induced dysfunction of the respiratory system. Clinicians often rely on their knowledge/experience and regular observation of the patient's response for adjusting the level of respiratory support. Using a similar data-driven decision-making methodology, an adaptive model-based advisory system has been designed for the clinical monitoring and management of mechanically ventilated patients. The hybrid blood gas patient model SOPAVent developed in Part I of this paper and validated against clinical data for a range of patients lung abnormalities is embedded into the advisory system to predict continuously and non-invasively the patient's respiratory response to changes in the ventilator settings. The choice of appropriate ventilator settings involves finding a balance among a selection of fundamentally competing therapeutic decisions. The design approach used here is based on a goal-directed multi-objective optimisation strategy to determine the optimal ventilator settings that effectively restore gas exchange and promote improved patient's clinical conditions. As an initial step to its clinical validation, the advisory system's closed-loop stability and performance have been assessed in a series of simulations scenarios reconstructed from real ICU patients data. The results show that the designed advisory system can generate good ventilator-setting advice under patient state changes and competing ventilator management targets.