Objective: To present a decision support system for optimising mechanical ventilation in patients residing in the intensive care unit.
Methods: Mathematical models of oxygen transport, carbon dioxide transport and lung mechanics are combined with penalty functions describing clinical preference toward the goals and side-effects of mechanical ventilation in a decision theoretic approach. Penalties are quantified for risk of lung barotrauma, acidosis or alkalosis, oxygen toxicity or absorption atelectasis, and hypoxaemia.
Results: The system is presented with an example of its use in a post-surgical patient. The mathematical models describe the patient's data, and the system suggests an optimal ventilator strategy in line with clinical practice.
Conclusions: The system illustrates how mathematical models combined with decision theory can aid in the difficult compromises necessary when deciding on ventilator settings.