Expiratory model-based method to monitor ARDS disease state

INTRODUCTION: Model-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. METHODS: Three 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. RESULTS: The 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. CONCLUSION: Overall, 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.Peer reviewe

Local coexpression domains in the genome of rice show no microsynteny with Arabidopsis domains

Chromosomal coexpression domains are found in a number of different genomes under various developmental conditions. The size of these domains and the number of genes they contain vary. Here, we define local coexpression domains as adjacent genes where all possible pair-wise correlations of expression data are higher than 0.7. In rice, such local coexpression domains range from predominantly two genes, up to 4, and make up ∼5% of the genomic neighboring genes, when examining different expression platforms from the public domain. The genes in local coexpression domains do not fall in the same ontology category significantly more than neighboring genes that are not coexpressed. Duplication, orientation or the distance between the genes does not solely explain coexpression. The regulation of coexpression is therefore thought to be regulated at the level of chromatin structure. The characteristics of the local coexpression domains in rice are strikingly similar to such domains in the Arabidopsis genome. Yet, no microsynteny between local coexpression domains in Arabidopsis and rice could be identified. Although the rice genome is not yet as extensively annotated as the Arabidopsis genome, the lack of conservation of local coexpression domains may indicate that such domains have not played a major role in the evolution of genome structure or in genome conservation

Analysis of different model-based approaches for estimating dFRC for real-time application

Background: Acute Respiratory Distress Syndrome (ARDS) is characterized by inflammation, filling of the lung with fluid and the collapse of lung units. Mechanical ventilation (MV) is used to treat ARDS using positive end expiratory pressure (PEEP) to recruit and retain lung units, thus increasing pulmonary volume and dynamic functional residual capacity (dFRC) at the end of expiration. However, simple, non-invasive methods to estimate dFRC do not exist. Methods: Four model-based methods for estimating dFRC are compared based on their performance on two separate clinical data cohorts. The methods are derived from either stress-strain theory or a single compartment lung model, and use commonly controlled or measured parameters (lung compliance, plateau airway pressure, pressure-volume (PV) data). Population constants are determined for the stress-strain approach, which is implemented using data at both single and multiple PEEP levels. Estimated values are compared to clinically measured values to assess the reliability of each method for each cohort individually and combined. Results: The stress-strain multiple breath (at multiple PEEP levels) method produced an overall correlation coefficient R2 = 0.966. The stress-strain single breath method produced R2 = 0.530. The single compartment single breath method produced R2 = 0.415. A combined method at single and multiple PEEP levels produced R2 = 0.963. Conclusions: The results suggest that model-based, single breath and non-invasive approaches to estimating dFRC may be viable in a clinical scenario, ensuring no interruption to MV. The models provide a means of estimating dFRC at any PEEP level. However, model limitations and large estimation errors limit the use of the methods at very low PEEP

Model-based respiratory mechanics to titrate PEEP and monitor disease state for experimental ARDS subjects.

Modelling the respiratory mechanics of mechanically ventilated (MV) patients can provide useful information to guide MV therapy. Two model-based methods were evaluated based on data from three experimental acute respiratory distress syndrome (ARDS) induced piglets and validated against values available from ventilators. A single compartment lung model with integral-based parameter identification was found to be effective in capturing fundamental respiratory mechanics during inspiration. The trends matched clinical expectation and provided better resolution than clinically derived linear model metrics. An expiration time constant model also captured the same trend in respiratory elastance. However, the assumption of constant resistance and a slightly higher fitting error results in less insight than the single compartment model. Further research is required to confirm its application in titrating to optimal MV settings