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.
Background: Ventilation with lower positive end–expiratory pressure (PEEP) may cause loss of lung aeration in critically ill invasively ventilated patients. This study investigated whether a systematic lung ultrasound (LUS) scoring system can detect such changes in lung aeration in a study comparing lower versus higher PEEP in invasively ventilated patients without acute respiratory distress syndrome (ARDS). Methods: Single center substudy of a national, multicenter, randomized clinical trial comparing lower versus higher PEEP ventilation strategy. Fifty–seven patients underwent a systematic 12–region LUS examination within 12 h and between 24 to 48 h after start of invasive ventilation, according to randomization. The primary endpoint was a change in the global LUS aeration score, where a higher value indicates a greater impairment in lung aeration. Results: Thirty–three and twenty–four patients received ventilation with lower PEEP (median PEEP 1 (0–5) cm H2O) or higher PEEP (median PEEP 8 (8–8) cm H2O), respectively. Median global LUS aeration scores within 12 h and between 24 and 48 h were 8 (4 to 14) and 9 (4 to 12) (difference 1 (–2 to 3)) in the lower PEEP group, and 7 (2–11) and 6 (1–12) (difference 0 (–2 to 3)) in the higher PEEP group. Neither differences in changes over time nor differences in absolute scores reached statistical significance. Conclusions: In this substudy of a randomized clinical trial comparing lower PEEP versus higher PEEP in patients without ARDS, LUS was unable to detect changes in lung aeration.
BACKGROUND: Increasing evidence indicates the potential benefits of restricted fluid management in critically ill patients. Evidence lacks on the optimal fluid management strategy for invasively ventilated COVID-19 patients. We hypothesized that the cumulative fluid balance would affect the successful liberation of invasive ventilation in COVID-19 patients with acute respiratory distress syndrome (ARDS).METHODS: We analyzed data from the multicenter observational 'PRactice of VENTilation in COVID-19 patients' study. Patients with confirmed COVID-19 and ARDS who required invasive ventilation during the first 3 months of the international outbreak (March 1, 2020, to June 2020) across 22 hospitals in the Netherlands were included. The primary outcome was successful liberation of invasive ventilation, modeled as a function of day 3 cumulative fluid balance using Cox proportional hazards models, using the crude and the adjusted association. Sensitivity analyses without missing data and modeling ARDS severity were performed.RESULTS: Among 650 patients, three groups were identified. Patients in the higher, intermediate, and lower groups had a median cumulative fluid balance of 1.98 L (1.27-7.72 L), 0.78 L (0.26-1.27 L), and - 0.35 L (- 6.52-0.26 L), respectively. Higher day 3 cumulative fluid balance was significantly associated with a lower probability of successful ventilation liberation (adjusted hazard ratio 0.86, 95% CI 0.77-0.95, P = 0.0047). Sensitivity analyses showed similar results.CONCLUSIONS: In a cohort of invasively ventilated patients with COVID-19 and ARDS, a higher cumulative fluid balance was associated with a longer ventilation duration, indicating that restricted fluid management in these patients may be beneficial. Trial registration Clinicaltrials.gov ( NCT04346342 ); Date of registration: April 15, 2020.
Patiëntdata uit vragenlijsten, fysieke testen en ‘wearables’ hebben veel potentie om fysiotherapie-behandelingen te personaliseren (zogeheten ‘datagedragen’ zorg) en gedeelde besluitvorming tussen fysiotherapeut en patiënt te faciliteren. Hiermee kan fysiotherapie mogelijk doelmatiger en effectiever worden. Veel fysiotherapeuten en hun patiënten zien echter nauwelijks meerwaarde in het verzamelen van patiëntdata, maar vooral toegenomen administratieve last. In de bestaande landelijke databases krijgen fysiotherapeuten en hun patiënten de door hen zelf verzamelde patiëntdata via een online dashboard weliswaar teruggekoppeld, maar op een weinig betekenisvolle manier doordat het dashboard primair gericht is op wensen van externe partijen (zoals zorgverzekeraars). Door gebruik te maken van technologische innovaties zoals gepersonaliseerde datavisualisaties op basis van geavanceerde data science analyses kunnen patiëntdata betekenisvoller teruggekoppeld en ingezet worden. Wij zetten technologie dus in om ‘datagedragen’, gepersonaliseerde zorg, in dit geval binnen de fysiotherapie, een stap dichterbij te brengen. De kennis opgedaan in de project is tevens relevant voor andere zorgberoepen. In dit KIEM-project worden eerst wensen van eindgebruikers, bestaande succesvolle datavisualisaties en de hiervoor vereiste data science analyses geïnventariseerd (werkpakket 1: inventarisatie). Op basis hiervan worden meerdere prototypes van inzichtelijke datavisualisaties ontwikkeld (bijvoorbeeld visualisatie van patiëntscores in vergelijking met (beoogde) normscores, of van voorspelling van verwacht herstel op basis van data van vergelijkbare eerdere patiënten). Middels focusgroepinterviews met fysiotherapeuten en patiënten worden hieruit de meest kansrijke (maximaal 5) prototypes geselecteerd. Voor deze geselecteerde prototypes worden vervolgens de vereiste data-analyses ontwikkeld die de datavisualisaties op de dashboards van de landelijke databases mogelijk maken (werkpakket 2: prototypes en data-analyses). In kleine pilots worden deze datavisualisaties door eindgebruikers toegepast in de praktijk om te bepalen of ze daadwerkelijk aan hun wensen voldoen (werkpakket 3: pilots). Uit dit 1-jarige project kan een groot vervolgonderzoek ‘ontkiemen’ naar het effect van betekenisvolle datavisualisaties op de uitkomsten van zorg.
The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
