Expiratory model-based method to monitor ARDS disease state

Abstract

IntroductionModel-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection.MethodsThree fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty.ResultsThe overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics.ConclusionOverall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management

“…The existing methods of respiratory mechanics monitoring are invasive and/or interrupt care, and thus cannot be performed continuously. In particular, most ventilator continuous monitoring methods use two point respiratory mechanics estimation, which can only be performed in one specific set ventilation mode using an additional end of inspiratory pause (EIP) [ 26 ], which can be automated by some ventilators (For example: the Engstrom Carestation and Puritan Bennett 980). Equally, this method heavily relies on the duration of the EIP [ 26 , 27 ] and provides only a local estimation at that point and pressure of the breath, which may not match what is obtained via a model-based approach that requires no EIP.…”

“…In particular, most ventilator continuous monitoring methods use two point respiratory mechanics estimation, which can only be performed in one specific set ventilation mode using an additional end of inspiratory pause (EIP) [ 26 ], which can be automated by some ventilators (For example: the Engstrom Carestation and Puritan Bennett 980). Equally, this method heavily relies on the duration of the EIP [ 26 , 27 ] and provides only a local estimation at that point and pressure of the breath, which may not match what is obtained via a model-based approach that requires no EIP. Thus, in comparison, CURE Soft uses a model-based approach using readily available airway pressure and flow profile, adds no additional burden to patients, and provides more information than was previously unavailable.…”

Parameter estimation in a minimal model of cardio-pulmonary interactions

“…The respiratory model must also be minimal to ensure identifiability and computational speed. Many respiratory system models exist [38,45,46], including complex, three-dimensional finite element models [39,47], as well as simpler lumped parameter models [38,45,48]. This research considers a clinically validated linear single compartment model [38,45] (Fig.…”

“…This lung model has been extensively validated against clinical data and captures all fundamental mechanics [37,38,41,48]. An expression of the lung volume can be integrated from the airflow Q aw (t) entering the lungs, as:…”

An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

“…Incorporating algorithmic classifiers to MV practice has the potential to detect diagnostic events, pathologic phenomena such as patient-ventilator asynchronies (PVA), and improve clinical outcomes. (8)(9)(10)(11)(12)(13)(14)(15)(16). To develop and validate high performance classifiers, clinicians have historically been required to manually annotate large volumes of ventilator waveform data (VWD) by visually analyzing the shape and magnitude of air flow and pressure data.…”