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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IntroductionThe driving pressure (ΔP) has an independent association with outcome in patients with acute respiratory distress syndrome (ARDS). INTELLiVENT-Adaptive Support Ventilation (ASV) is a closed-loop mode of ventilation that targets the lowest work and force of breathing.AimTo compare transpulmonary and respiratory system ΔP between closed-loop ventilation and conventional pressure controlled ventilation in patients with moderate-to-severe ARDS.MethodsSingle-center randomized cross-over clinical trial in patients in the early phase of ARDS. Patients were randomly assigned to start with a 4-h period of closed-loop ventilation or conventional ventilation, after which the alternate ventilation mode was selected. The primary outcome was the transpulmonary ΔP; secondary outcomes included respiratory system ΔP, and other key parameters of ventilation.ResultsThirteen patients were included, and all had fully analyzable data sets. Compared to conventional ventilation, with closed-loop ventilation the median transpulmonary ΔP with was lower (7.0 [5.0–10.0] vs. 10.0 [8.0–11.0] cmH2O, mean difference − 2.5 [95% CI − 2.6 to − 2.1] cmH2O; P = 0.0001). Inspiratory transpulmonary pressure and the respiratory rate were also lower. Tidal volume, however, was higher with closed-loop ventilation, but stayed below generally accepted safety cutoffs in the majority of patients.ConclusionsIn this small physiological study, when compared to conventional pressure controlled ventilation INTELLiVENT-ASV reduced the transpulmonary ΔP in patients in the early phase of moderate-to-severe ARDS. This closed-loop ventilation mode also led to a lower inspiratory transpulmonary pressure and a lower respiratory rate, thereby reducing the intensity of ventilation.Trial registration Clinicaltrials.gov, NCT03211494, July 7, 2017. https://clinicaltrials.gov/ct2/show/NCT03211494?term=airdrop&draw=2&rank=1.
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Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
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IntroductionMechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT–Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work–and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation and conventional ventilation in critically ill patients.Materials and methodsInternational, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3–hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power.ResultsA total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5–21.0] versus 16.1 [10.9–22.6] J/min; mean difference –0.44 (95%–CI –1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5–22.1] versus 19.0 [14.1–25.0] J/min; mean difference –1.76 (95%–CI –2.47 to –10.34J/min; P < 0.01), and not in active patients (14.6 [11.0–20.3] vs 14.1 [10.1–21.3] J/min; mean difference 0.81 (95%–CI –2.13 to 0.49) J/min; P = 0.23).ConclusionsIn this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients.Study registrationClinicaltrials.gov (study identifier NCT04827927), April 1, 2021URL of trial registry recordhttps://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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In recent years, the effects of the physical environment on the healing process and well-being have proved to be increasingly relevant for patients and their families (PF) as well as for healthcare staff. The discussions focus on traditional and institutionally designed healthcare facilities (HCF) relative to the actual well-being of patients as an indicator of their health and recovery. This review investigates and structures the scientific research on an evidence-based healthcare design for PF and staff outcomes. Evidence-based design has become the theoretical concept for what are called healing environments. The results show the effects on PF and staff from the perspective of various aspects and dimensions of the physical environmental factors of HFC. A total of 798 papers were identified that fitted the inclusion criteria for this study. Of these, 65 articles were selected for review: fewer than 50% of these papers were classified with a high level of evidence, and 86% were included in the group of PF outcomes. This study demonstrates that evidence of staff outcomes is scarce and insufficiently substantiated. With the development of a more customer-oriented management approach to HCF, the implications of this review are relevant to the design and construction of HCF. Some design features to consider in future design and construction of HCF are single-patient rooms, identical rooms, and lighting. For future research, the main challenge will be to explore and specify staff needs and to integrate those needs into the built environment of HCF.
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