Changes in respiratory mechanics and gas exchange during the acute respiratory distress syndrome

BACKGROUND: The time course of impairment of respiratory mechanics and gas exchange in the acute respiratory distress syndrome (ARDS) remains poorly defined. We assessed the changes in respiratory mechanics and gas exchange during ARDS. We hypothesized that due to the changes in respiratory mechanics over time, ventilatory strategies based on rigid volume or pressure limits might fail to prevent overdistension throughout the disease process. METHODS: Seventeen severe ARDS patients {PaO2/FiO2 10.1 (9.2-14.3) kPa; 76 (69-107) mmHg [median (25th-75th percentiles)] and bilateral infiltrates} were studied during the acute, intermediate, and late stages of ARDS (at 1-3, 4-6 and 7 days after diagnosis). Severity of lung injury, gas exchange, and hemodynamics were assessed. Pressure-volume (PV) curves of the respiratory system were obtained, and upper and lower inflection points (UIP, LIP) and recruitment were estimated. RESULTS: (1) UIP decreased from early to established (intermediate and late) ARDS [30 (28-30) cmH2O, 27 (25-30) cmH2O and 25 (23-28) cmH2O (P=0.014)]; (2) oxygenation improved in survivors and in patients with non-pulmonary etiology in late ARDS, whereas all patients developed hypercapnia from early to established ARDS; and (3) dead-space ventilation and pulmonary shunt were larger in patients with pulmonary etiology during late ARDS. CONCLUSION: We found a decrease in UIP from acute to established ARDS. If applied to our data, the inspiratory pressure limit advocated by the ARDSnet (30 cmH2O) would produce ventilation over the UIP, with a consequent increased risk of overdistension in 12%, 43% and 65% of our patients during the acute, intermediate and late phases of ARDS, respectively. Lung protective strategies based on fixed tidal volume or pressure limits may thus not fully avoid the risk of lung overdistension throughout ARDS.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).