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This dissertation has addressed the broad hypothesis as to whether building mathematical models is useful as a tool for translating physiological knowledge into clinical practice. In doing so it describes work on the INtelligent VENTilator project (INVENT), the goal of which is to build, evaluate and integrate into clinical practice, a model-based decision support system for control of mechanical ventilation. The dissertation describes the mathematical models included in INVENT, i.e. a model of pulmonary gas exchange focusing on oxygen transport, and a model of the acid-base status of blood, interstitial fluid and tissues. These models have been validated, and applied in two other systems: ALPE, a system for measuring pulmonary gas exchange and ARTY, a system for arterialisation of the acid-base and oxygen status of peripheral venous blood. The major contributions of this work are as follows. A mathematical model has been developed which can describe pulmonary gas exchange more accurately that current clinical techniques. This model is parsimonious in that it can describe pulmonary gas exchange from measurements easily available in the clinic, along with a readily automatable variation in F(I)O(2). This technique and model have been developed into a research and commercial tool (ALPE), and evaluated both in the clinical setting and when compared to the reference multiple inert gas elimination technique (MIGET). Mathematical models have been developed of the acid- base chemistry of blood, interstitial fluid and tissues, with these models formulated using a mass-action mass-balance approach. The model of blood has been validated against literature data describing the addition and removal of CO(2), strong acid or base, and haemoglobin; and the effects of oxygenation or deoxygenation. The model has also been validated in new studies, and shown to simulate accurately and precisely the mixing of blood samples at different PCO(2) and PO(2) levels. This model of acid-base chemistry of blood has been applied in the ARTY system. ARTY has been shown to accurately and precisely calculate arterial values of acid-base and oxygen status in patients residing in the ICU, and in those with chronic lung disease. The INtelligent VENTilator (INVENT) system has been developed for optimization of mechanical ventilator settings using physiological models and utility/penalty functions, separating physiological knowledge from clinical preference. The models can be tuned to the individual patient via parameter estimation, providing patient specific advice. The INVENT team has shown prospectively that the system provides advice on F(I)O(2) which is as good as clinical practice, and retrospectively that the system provides reasonable suggestions of tidal volume, respiratory frequency and F(I)O(2). In general, this dissertation has illustrated a further example of the role of modeling in describing and understanding complex systems. The dissertation has shown that when dealing with complexity the goal of the model must be in focus if a correct balance is to be maintained between system complexity and model parameterization. The original goal of the INVENT team, i.e. to build, evaluate and integrate a DSS for control of mechanical ventilation has not as yet been completed. However, the broader hypothesis that building models generates new and interesting questions has been successfully demonstrated. The ALPE model and system has been applied in intensive care, post operative care and cardiology and is currently being evaluated in new clinical domains. ARTY has been shown to have potential benefit in eliminating the need for painful arterial punctures, and may also be useful as a screening tool. These systems illustrate the benefits of investing in models as a mechanism for translating physiological knowledge to clinical practice. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.


Stephen E Rees. The Intelligent Ventilator (INVENT) project: the role of mathematical models in translating physiological knowledge into clinical practice. Computer methods and programs in biomedicine. 2011 Dec;104 Suppl 1:S1-29

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PMID: 22152752

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