Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort (original) (raw)
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A New Lung Mechanics Model and Its Evaluation with Clinical Data
Journal of Biomedical Science and Engineering, 2016
Acute Respiratory Distress Syndrome (ARDS) is a major cause of morbidity and has a high rate of mortality. ARDS patients in the intensive care unit (ICU) require mechanical ventilation (MV) for breathing support, but inappropriate settings of MV can lead to ventilator induced lung injury (VILI). Those complications may be avoided by carefully optimizing ventilation parameters through model-based approaches. In this study we introduced a new model of lung mechanics (mNARX) which is a variation of the NARX model by Langdon et al. A multivariate process was undertaken to determine the optimal parameters of the mNARX model and hence, the final structure of the model fit 25 patient data sets and successfully described all parts of the breathing cycle. The model was highly successful in predicting missing data and showed minimal error. Thus, this model can be used by the clinicians to find the optimal patient specific ventilator settings.
Simultaneous Parameter and Input Estimation of a Respiratory Mechanics Model
Modeling, Simulation and Optimization of Complex Processes HPSC 2015, 2017
Real-time noninvasive estimation of respiratory mechanics in spontaneously breathing patients is still an open problem in the field of critical care. Even assuming that the system is a simplistic first-order single-compartment model, the presence of unmeasured patient effort still makes the problem complex since both the parameters and part of the input are unknown. This paper presents an approach to overcome the underdetermined nature of the mathematical problem by infusing physiological knowledge into the estimation process and using it to construct an optimization problem subject to physiological constraints. As it relies only on measurements available on standard ventilators, namely the flow and pressure at the patient's airway opening, the approach is noninvasive. Additionally, breath by breath, it continually provides estimates of the patient respiratory resistance and elastance as well as of the muscle effort waveform without requiring maneuvers that would interfere with the desired ventilation pattern.
PloS one, 2015
Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (Edrs) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. A time-varying respiratory elastance model is developed with a negative elastic component (Edemand), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. Edrs of every breathing cycle for each patient at different ventilation modes are presented for comparison. At the start of every breathing cycle initiated by patient, Edrs is < 0. Th...
Results of respiratory mechanics analysis in the critically ill depend on the method employed
Intensive Care Medicine, 2001
The respiratory mechanics of critical care patients with lung injury are characterized by non-linear (amplitudedependent) and heterogeneous (frequency-dependent) behavior [1] and their analysis is influenced by the nature of the input-output signals , which are usually complex periodic with several frequency components. These features are utilized for the diagnosis and to select the optimal mechanical ventilation parameters [3], but they hamper the respiratory mechanics analysis . The usual bedside methods of respiratory mechanics analysis operate in the time domain. The occlusion method is perhaps the most widely utilized and reveals Abstract Objective: To compare
IEEE Transactions on Biomedical Engineering, 2015
This paper presents a method for breath-by-breath noninvasive estimation of respiratory resistance and elastance in mechanically ventilated patients. For passive patients, well-established approaches exist. However, when patients are breathing spontaneously, taking into account the diaphragmatic effort in the estimation process is still an open challenge. Mechanical ventilators require maneuvers to obtain reliable estimates for respiratory mechanics parameters. Such maneuvers interfere with the desired ventilation pattern to be delivered to the patient. Alternatively, invasive procedures are needed. The method presented in this paper is a noninvasive way requiring only measurements of airway pressure and flow that are routinely available for ventilated patients. It is based on a first-order single-compartment model of the respiratory system, from which a cost function is constructed as the sum of squared errors between model-based airway pressure predictions and actual measurements. Physiological considerations are translated into mathematical constraints that restrict the space of feasible solutions and make the resulting optimization problem strictly convex. Existing quadratic programming techniques are used to efficiently find the minimizing solution, which yields an estimate of the respiratory system resistance and elastance. The method is illustrated via numerical examples and experimental data from animal tests. Results show that taking into account the patient effort consistently improves the estimation of respiratory mechanics. The method is suitable for real-time patient monitoring, providing clinicians with noninvasive measurements that could be used for diagnosis and therapy optimization.
Intensive Care Medicine, 1995
To evaluate a least squares fitting technique for the purpose of measuring total respiratory compliance (Crs) and resistance (Rrs) in patients submitted to partial ventilatory support, without the need for esophageal pressure measurement. Prospective, randomized study. A general ICU of a University Hospital. 11 patients in acute respiratory failure, intubated and assisted by pressure support ventilation (PSV). Patients were ventilated at 4 different levels of pressure support. At the end of the study, they were paralyzed for diagnostic reasons and submitted to volume controlled ventilation (CMV). A least squares fitting (LSF) method was applied to measure Crs and Rrs at different levels of pressure support as well as in CMV. Crs and Rrs calculated by the LSF method were compared to reference values which were obtained in PSV by measurement of esophageal pressure, and in CMV by the application of the constant flow, end-inspiratory occlusion method. Inspiratory activity was measured by P0.1. In CMV, Crs and Rrs measured by the LSF method are close to quasistatic compliance (-1.5 +/- 1.5 ml/cmH2O) and to the mean value of minimum and maximum end-inspiratory resistance (+0.9 +/- 2.5 cmH2O/(l/s)). Applied during PSV, the LSF method leads to gross underestimation of Rrs (-10.4 +/- 2.3 cmH2O/(l/s)) and overestimation of Crs (+35.2 +/- 33 ml/cmH2O) whenever the set pressure support level is low and the activity of the respiratory muscles is high (P0.1 was 4.6 +/- 3.1 cmH2O). However, satisfactory estimations of Crs and Rrs by the LSF method were obtained at increased pressure support levels, resulting in a mean error of -0.4 +/- 6 ml/cmH2O and -2.8 +/- 1.5 cmH2O/(l/s), respectively. This condition was coincident with a P0.1 of 1.6 +/- 0.7 cmH2O. The LSF method allows non-invasive evaluation of respiratory mechanics during PSV, provided that a near-relaxation condition is obtained by means of an adequately increased pressure support level. The measurement of P0.1 may be helpful for titrating the pressure support in order to obtain the condition of near-relaxation.
Modeling Lung Functionality in Volume-Controlled Ventilation for Critical Care Patients
2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 2020
Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under volume-controlled ventilation to describe the lung volume and air flow in terms of air pressure signal from the ventilator. A negative feedback is incorporated in the model to balance the lung volume that is influenced by a lung parameter called positive end expiration pressure. We partially solved the lung model equation which takes the form of a first-order differential equation and then unknown parameters associated with the model were computed using a nonlinear least-squares method. Experimental data required for parameter identification and validation of the lung model were obtained by running a volume-controlled ventilator connected to a reference device and an artificial lung. The proposed model considering negative feedback achieves a better accuracy than that with...
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 1 ( 2 0 1 1 ) 126-134 Multiple linear regression Forward-inverse modelling Accuracy analysis a b s t r a c t Many patients undergo long-term artificial ventilation and their respiratory system mechanics should be monitored to detect changes in the patient's state and to optimize ventilator settings. In this work the most popular algorithms for tracking variations of respiratory resistance (R rs ) and elastance (E rs ) over a ventilatory cycle were analysed in terms of systematic and random errors. Additionally, a new approach was proposed and compared to the previous ones. It takes into account an exact description of flow integration by volume-dependent lung compliance. The results of analyses showed advantages of this new approach and enabled to form several suggestions. Algorithms including R rs and E rs dependencies on airflow and lung volume can be effectively applied only at low levels of noise present in measurement data, otherwise the use of the simplest model with constant parameters is