Multivariable optimization of mechanical ventilation. A linear programming approach.

The proposed method aims at improved ventilatory care with reduced morbidity. It combines two important aspects of mechanical ventilation: gas exchange and lung mechanics. A single criterion was selected as optimization index of lung trauma: peak respiratory power (PRP) defined as the maximum product of pressure times flow during inspiration. Arterial blood gases reflect gas exchange and constitute the constraints of the problem. The constraints as well as the optimization index are expressed as linear functions of the input variables (frequency of breathing, tidal volume, and positive end expiratory pressure). A linear programming approach can therefore be used to determine the values of input variables that minimize PRP and at the same time keep arterial blood gases within the prescribed limits. The coefficients of the constraints and the optimization index equation are found by manipulating input variables in order to obtain four different values of PaO2, PaCO2 and PRP (there are four coefficients in each equation). The coefficients can then be calculated and the optimization procedure run. In a pilot study 5 patients suffering from diseases of varying pulmonary pathology were investigated with this method. In 4 out of 5 the ventilator treatment improved in terms of blood gas values (mean increase in PaO2 was 4.7%) and reduction of mechanical load on the lungs (mean PRP reduction was 20%). Lower PRP is accompanied by lower mean power and pressure values, which results in increased cardiac output. Presently, the main problem is the time it takes to determine the patient coefficients (approx one hour), a procedure that needs to be simplified.

Lung function analysis and optimization during artificial ventilation. A personal computer-based system.

In an intensive care unit a personal computer (PC) application for lung function analysis has been in use for 5 years. The PC system is applied to measure conventional and new parameters for diagnosis and therapy. The primary goal was to find parameters which could be used as optimization indices in optimal control systems for mechanical ventilation. Another clinical application of the PC system was as an automatic controller that stabilizes end-tidal CO2 concentration. The controller and the next application, the optimizer, could be integrated into an optimal control system. Such a system is described and a simulation trial of the integrated structure has demonstrated the potential.

A microcomputer system for on-line monitoring of pulmonary function during artificial ventilation.

Standard monitoring of the artificially ventilated patient in the intensive care unit (ICU) and during anaesthesia includes repeated determinations of arterial blood gases, airway pressure and expired volume. However, there is a need for more extensive monitoring of the critically ill ventilator treated patient, and this is possible by better utilization of modern technology. Information on a variety of variables related to both pulmonary mechanics and gas exchange has long been accessible in the lung-function laboratory. Small, inexpensive microcomputers (PCs), accurate and fast bedside monitors and modern ventilators have also made this information directly available to the ICU staff. This paper describes a microcomputer (PC-XT) system for on-line bedside monitoring of pulmonary function. The microcomputer receives airway pressure, gas-flow and timing signals from the ventilator and signals for carbon dioxide concentration from an infrared analyzer. Data related to pulmonary mechanics and gas exchange are derived and displayed on the computer screen, both numerically and as graphs. In studies of ten artificially ventilated patients the coefficients of variation (CV) were below 10% for directly obtained variables (tidal volume, airway pressure, end-tidal and mixed expired carbon dioxide, carbon dioxide production, airway dead space), whereas the derived variables (compliance, phase III carbon dioxide slope) were associated with greater variability, with CVs ranging from 1.3 to 24% (median 6.25% and 8.65% respectively). The accuracy in estimating dead space variations was checked in two ventilator-treated patients by adding known dead space volumes. Simple regression analysis yielded an r value of 0.98 indicating adequate correctness of measurements and calculations.