Intelligent model-based advisory system for the management of ventilated intensive care patients: Hybrid blood gas patient model

  • Authors:

  • A. Wang;M. Mahfouf;G. H. Mills;G. Panoutsos;D. A. Linkens;K. Goode;H. F. Kwok;M. Denaï

  • Affiliations:

  • Process Automation, ABB Limited, Howard Road, Eaton Socon, Cambridgeshire PE19 8EU, UK;Department of Automatic Control 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 Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Automatic Control 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;Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2010

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Abstract

Arterial blood gas (ABG) analyses are essential for assessing the acid-base status and guiding the adjustment of mechanical ventilation in critically ill patients. Conventional ABG sampling requires repeated arterial punctures or the insertion of an arterial catheter causing pain, haemorrhage and thrombosis to the patients. Less invasive and non-invasive blood gas analysers, with a technology still in transition, have offered some promise in the recent years. SOPAVent (Simulation of Patients under Artificial Ventilation) is a five compartment blood gas model which captures the basic features of respiratory physiology and gas exchange in the human lungs. It uses ventilator settings and routinely monitored physiological parameters as inputs to produce steady-state estimates of the patient's ABG. This paper overviews the original SOPAVent model and presents an improved data-driven hybrid model that is patient-specific and gives continuous and totally non-invasive ABG predictions. The model has been comprehensively tested in simulations and validated using recorded measurements of ABG and ventilator parameters from ICU patients.