Six methods to determine expiratory time constants in mechanically ventilated patients: a prospective observational physiology study.

BACKGROUND: Expiratory time constant (τ) objectively assesses the speed of exhalation and can guide adjustments of the respiratory rate and the I:E ratio with the goal of achieving complete exhalation. Multiple methods of obtaining τ are available, but they have not been compared. The purpose of this study was to compare six different methods to obtain τ and to test if the exponentially decaying flow corresponds to the measured time constants. METHODS: In this prospective study, pressure, flow, and volume waveforms of 30 postoperative patients undergoing volume (VCV) and pressure-controlled ventilation (PCV) were obtained using a data acquisition device and analyzed. τ was measured as the first 63% of the exhaled tidal volume (VT) and compared to the calculated τ as the product of expiratory resistance (RE) and respiratory system compliance (CRS), or τ derived from passive flow/volume waveforms using previously published equations as proposed by Aerts, Brunner, Guttmann, and Lourens. We tested if the duration of exponentially decaying flow during exhalation corresponded to the duration of the predicted second and third τ, based on multiples of the first measured τ. RESULTS: Mean (95% CI) measured τ was 0.59 (0.57-0.62) s and 0.60 (0.58-0.63) s for PCV and VCV (p = 0.45), respectively. Aerts method showed the shortest values of all methods for both modes: 0.57 (0.54-0.59) s for PCV and 0.58 (0.55-0.61) s for VCV. Calculated (CRS * RE) and Brunner's τ were identical with mean τ of 0.64 (0.61-0.67) s for PCV and 0.66 (0.63-069) s for VCV. Mean Guttmann's τ was 0.64 (0.61-0.68) in PCV and 0.65 (0.62-0.69) in VCV. Comparison of each τ method between PCV and VCV was not significant. Predicted time to exhale 95% of the VT (i.e., 3*τ) was 1.77 (1.70-1.84) s for PCV and 1.80 (1.73-1.88) s for VCV, which was significantly longer than measured values: 1.27 (1.22-1.32) for PCV and 1.30 (1.25-1.35) s for VCV (p < 0.0001). The first, the second and the third measured τ were progressively shorter: 0.6, 0.4 and 0.3 s, in both ventilation modes (p < 0.0001). CONCLUSION: All six methods to determine τ show similar values and are feasible in postoperative mechanically ventilated patients in both PCV and VCV modes.

Modeling the Steady-State Effects of Mean Arterial Pressure on the Kidneys.

Goal: We describe the relationship between mean arterial pressure (MAP) and glomerular filtration rate (GFR) since therapies affecting MAP can have large effects on kidney function. Methods: We developed a closed-loop, steady-state mechanistic model of the human kidney with a reduced parameter set estimated from measurements. Results: The model was first validated against literature models. Further, GFR was validated against intensive care patient data (root mean squared error (RMSE) 13.5 mL/min) and against hypertensive patients receiving sodium nitroprusside (SNP) (RMSE less than 5 mL/min). A sensitivity analysis of the model reinforced the fact that vascular resistance is inversely related to GFR and showed that changes to either vascular resistance or renal autoregulation cause a significant change in sodium concentration in the descending limb of Henle. Conclusions: This model can be used to determine the impact of MAP on GFR and overall kidney health. The modeling framework lends itself to personalization of the model to a specific human.

Alveolar Tissue Fiber and Surfactant Effects on Lung Mechanics-Model Development and Validation on ARDS and IPF Patients.

Goal: Alveolar compliance is a main determinant of lung airflow. The compliance of the alveoli is a function of their tissue fiber elasticity, fiber volume, and surface tension. The compliance varies during respiration because of the nonlinear nature of fiber elasticity and the time-varying surface tension coating the alveoli. Respiratory conditions, like acute respiratory distress syndrome (ARDS) and idiopathic pulmonary fibrosis (IPF) affect fiber elasticity, fiber volume and surface tension. In this paper, we study the alveolar tissue fibers and surface tension effects on lung mechanics. Methods: To better understand the lungs, we developed a physiology-based mathematical model to 1) describe the effect of tissue fiber elasticity, fiber volume and surface tension on alveolar compliance, and 2) the effect of time-varying alveolar compliance on lung mechanics for healthy, ARDS and IPF conditions. Results: We first present the model sensitivity analysis to show the effects of model parameters on the lung mechanics variables. Then, we perform model simulation and validate on healthy non-ventilated subjects and ventilated ARDS or IPF patients. Finally, we assess the robustness and stability of this dynamic system. Conclusions: We developed a mathematical model of the lung mechanics comprising alveolar tissue and surfactant properties that generates reasonable lung pressures and volumes compared to healthy, ARDS, and IPF patient data.

Modeling of Transport Mechanisms in the Respiratory System: Validation via Congestive Heart Failure Patients.

The heart and lungs are intricately related. For congestive heart failure patients, fluid (plasma) backs up into the pulmonary system. As a result, pulmonary capillary pressure increases, causing fluid to seep into the lungs (pulmonary edema) within minutes. This excess fluid induces extra stress during breathing that affects respiratory health. In this paper, we focus on the effect that high pulmonary capillary pressure has on the development of this extravascular lung water (EVLW). A mathematical model of pulmonary fluid and mass transport mechanisms is developed in order to quantitatively analyze the transport phenomena in the pulmonary system. The model is then validated on 15 male heart failure patients from published literature [1]. The model shows reasonable estimation of EVLW in heart failure patients, which is useful in assessing the severity of pulmonary edema.

Clinical knowledge-based inference model for early detection of acute lung injury.

Acute lung injury (ALI) is a devastating complication of acute illness and one of the leading causes of multiple organ failure and mortality in the intensive care unit (ICU). The detection of this syndrome is limited due to the complexity of the disease, insufficient understanding of its development and progression, and the large amount of risk factors and modifiers. In this preliminary study, we present a novel mathematical model for ALI detection. It is constructed based on clinical and research knowledge using three complementary techniques: rule-based fuzzy inference systems, Bayesian networks, and finite state machines. The model is developed in Matlab(®)'s Simulink environment and takes as input pre-ICU and ICU data feeds of critically ill patients. Results of the simulation model were validated against actual patient data from an epidemiologic study. By appropriately combining all three techniques the performance attained is in the range of 71.7-92.6% sensitivity and 60.3-78.4% specificity.