Control of positive end-expiratory pressure (PEEP) for small animal ventilators

<p>Abstract</p> <p>Background</p> <p>The positive end-expiratory pressure (PEEP) for the mechanical ventilation of small animals is frequently obtained with water seals or by using ventilators developed for human use. An alternative mechanism is the use of an on-off expiratory valve closing at the moment when the alveolar pressure is equal to the target PEEP. In this paper, a novel PEEP controller (PEEP-new) and the PEEP system of a commercial small-animal ventilator, both based on switching an on-off valve, are evaluated.</p> <p>Methods</p> <p>The proposed PEEP controller is a discrete integrator monitoring the error between the target PEEP and the airways opening pressure prior to the onset of an inspiratory cycle. In vitro as well as in vivo experiments with rats were carried out and the PEEP accuracy, settling time and under/overshoot were considered as a measure of performance.</p> <p>Results</p> <p>The commercial PEEP controller did not pass the tests since it ignores the airways resistive pressure drop, resulting in a PEEP 5 cmH₂O greater than the target in most conditions. The PEEP-new presented steady-state errors smaller than 0.5 cmH₂O, with settling times below 10 s and under/overshoot smaller than 2 cmH₂O.</p> <p>Conclusion</p> <p>The PEEP-new presented acceptable performance, considering accuracy and temporal response. This novel PEEP generator may prove useful in many applications for small animal ventilators.</p

Effects of descending positive end-expiratory pressure on lung mechanics and aeration in healthy anaesthetized piglets

INTRODUCTION: Atelectasis and distal airway closure are common clinical entities of general anaesthesia. These two phenomena are expected to reduce the ventilation of dependent lung regions and represent major causes of arterial oxygenation impairment in anaesthetic conditions. The behaviour of the elastance of the respiratory system (E(rs)), as well as the lung aeration assessed by computed tomography (CT) scan, was evaluated during a descendent positive end-expiratory pressure (PEEP) titration. This work sought to evaluate the potential usefulness of E(rs )monitoring to set the PEEP in order to prevent tidal recruitment and hyperinflation of healthy lungs under general anaesthesia. METHODS: PEEP titration (from 16 to 0 cmH(2)O, tidal volume of 8 ml/kg) was performed, and at each PEEP, CT scans were obtained during end-expiratory and end-inspiratory pauses in six healthy, anaesthetized and paralyzed piglets. The distribution of lung aeration was determined and the tidal re-aeration was calculated as the difference between end-expiratory and end-inspiratory poorly aerated and normally aerated areas. Similarly, tidal hyperinflation was obtained as the difference between end-inspiratory and end-expiratory hyperinflated areas. E(rs )was estimated from the equation of motion of the respiratory system during all PEEP titration with the least-squares method. RESULTS: Hyperinflated areas decreased from PEEP 16 to 0 cmH(2)O (ranges decreased from 24–62% to 1–7% at end-expiratory pauses and from 44–73% to 4–17% at end-inspiratory pauses) whereas normally aerated areas increased (from 30–66% to 72–83% at end-expiratory pauses and from 19–48% to 73–77% at end-inspiratory pauses). From 16 to 8 cmH(2)O, E(rs )decreased with a corresponding reduction in tidal hyperinflation. A flat minimum of E(rs )was observed from 8 to 4 cmH(2)O. For PEEP below 4 cmH(2)O, E(rs )increased in association with a rise in tidal re-aeration and a flat maximum of the normally aerated areas. CONCLUSION: In healthy piglets under a descending PEEP protocol, the PEEP at minimum E(rs )presented a compromise between maximizing normally aerated areas and minimizing tidal re-aeration and hyperinflation. High levels of PEEP, greater than 8 cmH(2)O, reduced tidal re-aeration but increased hyperinflation with a concomitant decrease in normally aerated areas

Intelligent Decision Support Systems in Ventilation Management

Introduction: Intensive Care Unit (ICU) medical personnel, in an ongoing process termed ventilation management, utilize patient physiology and pathology data to define ventilator apparatus settings. Aims: The aim of the research is to develop and evaluate in comparison hybrid ventilation advisor systems, that could support ventilation management process, specific to lung pathology for patients ventilated in control mode. Methodology: A questionnaire was designed and circulated to Intensivists. Patient data, as defined by the questionnaire analysis, were collected and categorized into three lung pathologies. Three ICU doctors evaluated correlation analysis of the recorded data. Evaluation results were used for identifying models basic architecture. Two custom software toolboxes were developed for developing hybrid systems; namely the EVolution Of Fuzzy INference Engines (EVOFINE) and the FUzzy Neural (FUN) toolbox. Eight hybrid systems developed with EVOFINE, FUN, ANFIS and ANN techniques were evaluated against applied clinical decisions and patient scenarios. Results: Seventeen (17) models were designed for each of the eight (8) modeling techniques. The modelled process consisted of twelve physiology variables and six ventilator settings. The number of models’ inputs ranged from single to six based on correlation and evaluation findings. Evaluation against clinical recommendations has shown that ANNs performed better; mean average error as percentage for four of the applied techniques was 0.16%, 1.29% & 0.62 for ANN empirical, 0.05%, 2.23% & 2.30% for ANFIS, 0.93%, 2.33% & 1.89% for EVOFINE and 0.73%, 2.63% & 6.56 for FUN NM, in Normal, COPD and ALI-ARDS categories respectively. Additionally evaluation against clinical disagreement SD has shown that 70.6% of the NN empirical models were performing in 90% of their suggestions within clinical SD, while the percentages were 53%, 53% and 59% for the EVOFINE, ANFIS and NN Normalized models respectively. The EVOFINE and ANFIS produced Fuzzy Systems whose architecture is transparent for the user. Visual observation of ANFIS architectures revealed possibly hazardous advices. Evaluation against clinical disagreement has shown that the NN empirical was not producing hazardous advices, while EVOFINE, ANFIS and NN Normalized were shown to produce potentially hazardous advice in 17.6%, 23% and 5.8% of the developed models