Background—Self-management interventions are widely implemented in care for patients with heart failure (HF). Trials however show inconsistent results and whether specific patient groups respond differently is unknown. This individual patient data meta-analysis assessed the effectiveness of self-management interventions in HF patients and whether subgroups of patients respond differently. Methods and Results—Systematic literature search identified randomized trials of selfmanagement interventions. Data of twenty studies, representing 5624 patients, were included and analyzed using mixed effects models and Cox proportional-hazard models including interaction terms. Self-management interventions reduced risk of time to the combined endpoint HF-related all-0.71- in Conclusions—This study shows that self-management interventions had a beneficial effect on time to HF-related hospitalization or all-cause death, HF-related hospitalization alone, and elicited a small increase in HF-related quality of life. The findings do not endorse limiting selfmanagement interventions to subgroups of HF patients, but increased mortality in depressed patients warrants caution in applying self-management strategies in these patients.
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.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
Door de vergrijzing ontstaat er een groeiende kloof tussen de behoefte aan zorgondersteuning en beschikbare menskracht om die ondersteuning te leveren. Robots kunnen hierbij mogelijk een rol spelen. Maar dan moeten deze robots wel veilig moeten zijn in hun interactie met mensen. Doel van dit project is het ontwikkelen van een Robot Safety-Module die kan garanderen dat een robot zich op een veilige manier gedraagt, of anders op een veilige manier tot stilstand komt. We richten ons daarbij op zorgrobots als Rose en Pepper, waarbij Rose model staat voor robots die een fysieke interactie met de omgeving kunnen aangaan, terwijl Pepper model staat voor de categorie sociale robots. Een robot die wordt gebruikt in een zorginstelling moet werken zonder enig risico voor lichamelijk of geestelijk gehandicapten die in die zorginstelling wonen. Rose is een semi-autonome servicerobot die zich autonoom kan verplaatsen (ronde lopen) en simpele interactie met de omgeving kan aangaan (bijvoorbeeld iets van de grond oprapen). Voor complexere handelingen kan Rose ook worden bestuurd door een operator op afstand, die Rose nauwkeurig naar een bepaalde locatie kan sturen, objecten herkennen, grijpen en plaatsen. Pepper is een sociale robot, die met armgebaren en body motion emotie ondersteunt, maar zich ook kan verplaatsen. De Robot Safety-Module moet garanderen dat de robot wordt gestopt en in een veilige toestand wordt gebracht, wanneer de signalen van de sensoren vooraf gedefinieerde grenzen overschrijden. Om het benodigde betrouwbaarheidsniveau te verkrijgen, zullen we een veiligheidsanalyse uitvoeren volgens de Failure Modes and Effects Analysis (FMEA). Vervolgens worden drie opties onderzocht: 1) een Robot Safety-Module (RSM) die zijn eigen set sensoren heeft, 2) een RSM die zowel zijn eigen set sensoren gebruikt als die van de zorgrobot en 3) een RSM die zich uitsluitend baseert op de reeds aanwezige sensoren en actuatoren van de zorgrobot.
