A structural model of perfusion and oxygenation in low-flow states

Abstract

Background Recent investigations underscore the critical importance of ventilation strategies on resuscitation outcomes. In low perfusion states, such as cardiac arrest and traumatic shock, the rise in intrathoracic pressure that accompanies positive-pressure ventilation can significantly impede venous return and lead to a decrease in cardiac output. The optimal ventilation strategy in these “low-flow” states remains unclear. Objective To create a mathematical model of perfusion and oxygenation to predict the effects of PPV with both normotension and hypotension. Methods The lung pressure–volume relationship was modeled using a novel formula allowing manipulation of various lung characteristics. A separate formula was then derived to predict mean intrathoracic pressure (MITP) for specific minute ventilation values using the pressure–volume formula. The addition of positive end-expiratory pressure was also modeled. Finally, a formula was derived to model oxygen absorbance as a function of alveolar surface area and flow based on ventilation rate and MITP. Results Mathematical models of the lung pressure–volume relationship, MITP, and absorbance were successfully derived. Manipulation of total lung capacity, compliance, upper and lower inflection points, positive end-expiratory pressure, and minute ventilation allowed prediction of optimal ventilation rate and tidal volume for a normal lung and with various abnormal characteristics to simulate particular disease states, such as acute respiratory distress syndrome (ARDS). For a normal lung, ventilation rates of 4–6 breaths/min with higher tidal volumes (15–20 mL/kg) resulted in the lowest predicted MITP values (5 cm H 2O) and the highest absorbance. The input of lung parameters that would simulate ARDS resulted in optimal ventilation rates of 10–12 breaths/min with lower tidal volumes (8–10 mL/kg) and higher predicted MITP values (10–15 cm H 2O). Conclusions A mathematical model of ventilation was successfully derived allowing manipulation of multiple pulmonary physiological variables to predict MITP and potentially identify optimal ventilation strategies. This model suggests the use of lower ventilation rates and larger tidal volumes to minimize the hemodynamic effects of positive pressure ventilation in patients with hypoperfusion but normal lung characteristics.