Jörn Kretschmer

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.

Successful application of mechanical ventilation as a life-saving therapy implies appropriate ventilator settings. Decision making is based on clinicians’ knowledge, but can be enhanced by mathematical models that determine the individual patient state by calculating parameters that are not directly measurable. Evaluation of models may support the clinician to reach a defined treatment goal. Bedside applicability of mathematical models for decision support requires a robust identification of the model parameters with a minimum of measuring effort. The influence of appropriate data selection on the identification of a two-parameter model of pulmonary gas exchange was analyzed. The model considers a shunt as well as ventilation-perfusion-mismatch to simulate a variety of pathologic pulmonary gas exchange states, i.e. different severities of pulmonary impairment. Synthetic patient data were generated by model simulation. To incorporate more realistic effects of measurement errors, the simulated data were corrupted with additive noise. In addition, real patient data retrieved from a patient data management system were used retrospectively to confirm the obtained findings. The model was identified to a wide range of different FiO 2 settings. Just one single measurement was used for parameter identification. Subsequently prediction performance was obtained by comparing the identified model predicted oxygen level in arterial blood either to exact data taken from simulations or patients measurements. Structural identifiability of the model using one single measurement for the identification process could be demonstrated. Minimum prediction error of blood oxygenation depends on blood gas level at the time of system identification i.e. the measurement situation. For severe pulmonary impairment, higher FiO 2 settings were required to achieve a better prediction capability compared to less impaired pulmonary states. Plausibility analysis with real patient data could confirm this finding. Dependent on patients’ pulmonary state, the influence of ventilator settings (here FiO 2) on model identification of the gas exchange model could be demonstrated. To maximize prediction accuracy i.e. to find the best individualized model with as few data as possible, best ranges of FiO 2-settings for parameter identification were obtained. A less effort identification process, which depends on the pulmonary state, can be deduced from the results of this identifiability analysis.

Mechanical ventilation is a life-saving intervention, which despite its use on a routine basis, poses the risk of inflicting further damage to the lung tissue if ventilator settings are chosen inappropriately. Medical decision support systems may help to prevent such injuries while providing the optimal settings to reach a defined clinical goal. In order to develop and verify decision support algorithms, a test bench simulating a patient’s behaviour is needed. We propose a Java based system that allows simulation of respiratory mechanics, gas exchange and cardiovascular dynamics of a mechanically ventilated patient. The implemented models are allowed to interact and are interchangeable enabling the simulation of various clinical scenarios. Model simulations are running in real-time and show physiologically plausible results.

In physiotherapy, rehabilitation outcome is majorly dependent on the patient continuing exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, Sony PlayStation or Microsoft Xbox360 that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications. We present a concept to employ the Microsoft Kinect system as means to support patients during physiotherapy exercises at home. The system is intended to allow a physiotherapist to compose an individual set of exercises and to control the correct execution of those exercises through tracking the patient’s motions.

Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation in critically ill patients should not only consider respiratory mechanics but should also consider other systems of the human organism such as gas exchange or blood circulation. A specially designed framework allows combining three model families (respiratory mechanics, cardiovascular dynamics and gas exchange) to predict the outcome of a therapy setting. Elements of the three model families are dynamically combined to form a complex model system with interacting submodels. Tests revealed that complex model combinations are not computationally feasible. In most patients, cardiovascular physiology could be simulated by simplified models decreasing computational costs. Thus, a simplified cardiovascular model that is able to reproduce basic physiological behavior is introduced. This model purely consists of difference equations and does not require special algorithms to be solved numerically. The model is based on a beat-to-beat model which has been extended to react to intrathoracic pressure levels that are present during mechanical ventilation. The introduced reaction to intrathoracic pressure levels as found during mechanical ventilation has been tuned to mimic the behavior of a complex 19-compartment model. Tests revealed that the model is able to represent general system behavior comparable to the 19-compartment model closely. Blood pressures were calculated with a maximum deviation of 1.8 % in systolic pressure and 3.5 % in diastolic pressure, leading to a simulation error of 0.3 % in cardiac output. The gas exchange submodel being reactive to changes in cardiac output showed a resulting deviation of less than 0.1 %. Therefore, the proposed model is usable in combinations where cardiovascular simulation does not have to be detailed. Computing costs have been decreased dramatically by a factor 186 compared to a model combination employing the 19-compartment model.