Background: INTELLiVENT-adaptive support ventilation (ASV) is an automated closed-loop mode of invasive ventilation for use in critically ill patients. INTELLiVENT-ASV automatically adjusts, without the intervention of the caregiver, ventilator settings to achieve the lowest work and force of breathing. Aims: The aim of this case series is to describe the specific adjustments of INTELLiVENT-ASV in patients with acute hypoxemic respiratory failure, who were intubated for invasive ventilation. Study design: We describe three patients with severe acute respiratory distress syndrome (ARDS) because of COVID-19 who received invasive ventilation in our intensive care unit (ICU) in the first year of the COVID-19 pandemic. Results: INTELLiVENT-ASV could be used successfully, but only after certain adjustments in the settings of the ventilator. Specifically, the high oxygen targets that are automatically chosen by INTELLiVENT-ASV when the lung condition ‘ARDS’ is ticked had to be lowered, and the titration ranges for positive end expiratory pressure (PEEP) and inspired oxygen fraction (FiO2) had to be narrowed. Conclusions: The challenges taught us how to adjust the ventilator settings so that INTELLiVENT-ASV could be used in successive COVID-19 ARDS patients, and we experienced the benefits of this closed-loop ventilation in clinical practice. Relevance to clinical practice: INTELLiVENT-ASV is attractive to use in clinical practice. It is safe and effective in providing lung-protective ventilation. A closely observing user always remains needed. INTELLiVENT-ASV has a strong potential to reduce the workload associated with ventilation because of the automated adjustments.
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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BACKGROUND:Endotracheal suctioning causes discomfort, is associated with adverse effects, and is resource-demanding. An artificial secretion removal method, known as an automated cough, has been developed, which applies rapid, automated deflation, and inflation of the endotracheal tube cuff during the inspiratory phase of mechanical ventilation. This method has been evaluated in the hands of researchers but not when used by attending nurses. The aim of this study was to explore the efficacy of the method over the course of patient management as part of routine care.METHODS:This prospective, longitudinal, interventional study recruited 28 subjects who were intubated and mechanically ventilated. For a maximum of 7 d and on clinical need for endotracheal suctioning, the automatic cough procedure was applied. The subjects were placed in a pressure-regulated ventilation mode with elevated inspiratory pressure, and automated cuff deflation and inflation were performed 3 times, with this repeated if deemed necessary. Success was determined by resolution of the clinical need for suctioning as determined by the attending nurse. Adverse effects were recorded.RESULTS:A total of 84 procedures were performed. In 54% of the subjects, the artificial cough procedure was successful on > 70% of occasions, with 56% of all procedures considered successful. Ninety percent of all the procedures were performed in subjects who were spontaneously breathing and on pressure-support ventilation with peak inspiratory pressures of 20 cm H2O. Rates of adverse events were similar to those seen in the application of endotracheal suctioning.CONCLUSIONS:This study solely evaluated the efficacy of an automated artificial cough procedure, which illustrated the potential for reducing the need for endotracheal suctioning when applied by attending nurses in routine care.
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