Short-term effects of positive end-expiratory pressure on breathing pattern: an interventional study in adult intensive care patients

INTRODUCTION: Positive end-expiratory pressure (PEEP) is used in mechanically ventilated patients to increase pulmonary volume and improve gas exchange. However, in clinical practice and with respect to adult, ventilator-dependent patients, little is known about the short-term effects of PEEP on breathing patterns. METHODS: In 30 tracheally intubated, spontaneously breathing patients, we sequentially applied PEEP to the trachea at 0, 5 and 10 cmH(2)O, and then again at 5 cmH(2)O for 30 s each, using the automatic tube compensation mode. RESULTS: Increases in PEEP were strongly associated with drops in minute ventilation (P < 0.0001) and respiratory rate (P < 0.0001). For respiratory rate, a 1 cmH(2)O change in PEEP in either direction resulted in a change in rate of 0.4 breaths/min. The effects were exclusively due to changes in expiratory time. Effects began to manifest during the first breath and became fully established in the second breath for each PEEP level. Identical responses were found when PEEP levels were applied for 10 or 60 s. Post hoc analysis revealed a similar but stronger response in patients with impaired respiratory system compliance. CONCLUSION: In tracheally intubated, spontaneously breathing adult patients, the level of PEEP significantly influences the resting short-term breathing pattern by selectively affecting expiratory time. These findings are best explained by the Hering–Breuer inflation/deflation reflex

Expiratory automatic endotracheal tube compensation reduces dynamic hyperinflation in a physical lung model

INTRODUCTION: The effect of expiratory endotracheal tube (ETT) resistance on dynamic lung inflation is unknown. We hypothesized that ETT resistance causes dynamic lung hyperinflation by impeding lung emptying. We further hypothesized that compensation for expiratory ETT resistance by automatic tube compensation (ATC) attenuates dynamic lung hyperinflation. METHODS: A ventilator equipped with the original ATC mode and operating in a pressure-targeted mode was connected to a physical lung model that consists of four equally sized glass bottles filled with copper wool. Inspiratory pressure, peak expiratory flow, trapped lung volume and intrinsic positive end-expiratory pressure (PEEP) were assessed at combinations of four inner ETT diameters (7.0, 7.5, 8.0 and 8.5 mm), four respiratory rates (15, 20, 25 and 30/minute), three inspiratory pressures (3.0, 4.5 and 6.0 cmH2O) and four lung compliances (113, 86, 58 and 28 ml/cmH2O). Intrinsic PEEP was measured at the end of an expiratory hold manoeuvre. RESULTS: At a given test lung compliance, inspiratory pressure and ETT size, increasing respiratory rates from 15 to 30/minutes had the following effects: inspiratory tidal volume and peak expiratory flow were decreased by means of 25% (range 0% to 51%) and 11% (8% to 12%), respectively; and trapped lung volume and intrinsic PEEP were increased by means of 25% (0% to 51%) and 26% (5% to 45%), respectively (all P < 0.025). At otherwise identical baseline conditions, introduction of expiratory ATC significantly attenuated (P < 0.025), by approximately 50%, the respiratory rate-dependent decreases in inspiratory tidal volume and the increases in trapped lung volume and intrinsic PEEP. CONCLUSIONS: In a lung model of pressure-targeted ventilation, expiratory ETT resistance caused dynamic lung hyperinflation during increases in respiratory rates, thereby reducing inspiratory tidal volume. Expiratory ATC attenuated these adverse effects

Equation discovery for model identification in respiratory mechanics of the mechanically ventilated human lung

Lung protective ventilation strategies reduce the risk of ventilator associated lung injury. To develop such strategies, knowledge about mechanical properties of the mechanically ventilated human lung is essential. This study was designed to develop an equation discovery system to identify mathematical models of the respiratory system in timeseries data obtained from mechanically ventilated patients. Two techniques were combined: (i) the usage of declarative bias to reduce search space complexity and inherently providing the processing of background knowledge. (ii) A newly developed heuristic for traversing the hypothesis space with a greedy, randomized strategy analogical to the GSAT algorithm. In 96.8% of all runs the applied equation discovery system was capable to detect the well-established equation of motion model of the respiratory system in the provided data. We see the potential of this semi-automatic approach to detect more complex mathematical descriptions of the respiratory system from respiratory data

1 Identifying Mathematical Models of the Mechanically Ventilated Lung Using Equation Discovery

Abstract — Mechanical ventilation is the live-saving therapy in intensive care medicine by all means. Nevertheless, it can induce severe mechanical stress to the lung, which generally impairs the outcome of the therapy. To reduce the risk of a ventilator induced lung injury (VILI), lung protective ventilation is essential, especially for patients with a previous medical history like the adult respiratory distress syndrome (ARDS). The prerequisite for lung protective ventilation approaches is the knowledge about the physical behavior of the human lung under the condition of mechanical ventilation. This knowledge is commonly described by mathematical models. Diverse models have been introduced to represent particular aspects of mechanical characteristics of the lung. A commonly accepted general model is the equation of motion, which relates the airway pressure to the airflow and the volume applied by the ventilator and describes the influence of the distensibility and resistance of the respiratory system. Equation Discovery systems extract mathematical models from observed time series data. To reduce the vast search space associated with this task, the LAGRAMGE-system introduced the application of declarative bias in Equation Discovery, which furthermore allows the presentation of domain specific knowledge. We introduce a modification of this system and apply it to data obtained during mechanical ventilation of ARDS-patients. We experimentally validate the effectiveness of our approach and show that the equation of motion model can automatically be rediscovered from real-world data