Bernhard Laufer

The Influence of Airway Resistance in the Dynamic Elastance Model

The selection of optimal positive end-expiratory pressure (PEEP) levels during ventilation therapy of patients with ARDS (acute respiratory distress syndrome) remains a problem for clinicians. A particular mooted strategy states that minimizing the energy transferred to the lung during mechanical ventilation could potentially be used to determine the optimal, patient-specific PEEP levels. The dynamic elastance model of pulmonary mechanics could potentially be used to minimize the energy by localization of the patients’ minimum dynamic elastance range.

Resistance in a non-linear autoregressive model of pulmonary mechanics

Abstract Respiratory system modelling can enable patient-specific mechanical ventilator settings to be found, and can thus reduce the incidence of ventilator induced lung injury in the intensive care unit. The resistance of a simple first order model (FOM) of pulmonary mechanics was compared with a flow dependent term of a non-linear autoregressive (NARX) model. Model parameters were identified for consecutive non-overlapping windows of length 20 breaths. The analysis was performed over recruitment manoeuvres for 25 sedated mechanically ventilated patients. The NARX model term, b1, consistently decreased as positive end expiratory pressure (PEEP) increased, while the FOM resistance behaviour varied. Overall the NARX b1 behaviour is more in-line with expected trends in airway resistance as PEEP increases. This work has further verified the physiologically descriptive capability of the NARX model.

Hierarchical Analysis of Thorax Models to Measure Tidal Volume

Abstract Motion tracking of thorax kinematics can be used to determine respiration. However, determining a minimal sensor configuration from 64 candidate sensor locations is associated with high computational costs. Hence, a hierarchical optimization method was proposed to determine the optimal combination of sensors. The hierarchical method was assessed by its ability to quickly determine the sensor combination that will yield optimal modelled tidal volume compared to body plethysmograph measurements. This method was able to find the optimal sensor combinations, in approximately 2% of the estimated time required by an exhaustive search.

Model based prediction of plateau pressure in mechanically ventilated patients

Abstract The risk of ventilator induced lung injury in mechanically ventilated (MV) critically ill patients can be mitigated by patient-specific optimisation of ventilator settings. Recent studies have shown that driving pressure, i.e. the difference between plateau pressure (Pplat) and PEEP, is a strong indicator for survival in MV patients suffering from ARDS. However, to measure Pplat, an extended end-inspiratory pause (EIP) has to be applied, possibly interrupting ventilation therapy. This study presents a method for predicting Pplat from normal breaths in MV patients. A total of 859 MV breaths with a 5 second EIP were recorded in 27 MV patients with ARDS. Two methods for determining Pplat were tested, one using an exponential fit of the pressure data and the other using a four-parameter viscoelastic model (VEM). Each method was identified using various lengths of data after the peak inspiratory pressure (PIP). Using the identified parameters, both methods were then used to predict the Pplat recorded at 5 seconds. The exponential method showed a median coefficient of variation (CV) from the real Pplat of 42.9% using data from PIP to 0.5 seconds after PIP, 24.9% using 1 second of data and 15.2% using 1.5 seconds of data. The respective VEM prediction median CVs were of 17.2%, 9.7% and 8.4%. Therefore, the VEM showed a better prediction than the non-physiological exponential model, allowing it to be used to reduce the clinical burden of determining Pplat by reducing the required length of the EIP to 1.5 seconds.

Ein softwarebasierter Patientensimulator zur Evaluierung medizinischer Entscheidungssysteme

Zusammenfassung Die Automatisierung der klinischen Intensivtherapie ist eine Entwicklung mit enormem ökonomischem Potential. Die immer komplexer werdenden Algorithmen für eine solche Automatisierung müssen während der Entwicklung aber auch zur Evaluierung und Validierung systematisch getestet werden. Besonders in der Entwicklungsphase eignen sich hierzu Simulationssysteme, die die physiologischen Reaktionen des Patienten abbilden und eine realitätsnahe Evaluierung ermöglichen. Im Folgenden soll ein solcher Patientensimulator vorgestellt werden, der einen mechanisch beatmeten Patienten abbilden kann. Die Ergebnisse der Patientensimulation zeigen in ihrer Gesamtheit physiologisch plausibles Verhalten und können in Echtzeit oder schneller berechnet werden.