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.
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BACKGROUND: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.METHODS: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.RESULTS: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.CONCLUSIONS: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.
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BackgroundFluid therapy is a common intervention in critically ill patients. It is increasingly recognised that deresuscitation is an essential part of fluid therapy and delayed deresuscitation is associated with longer invasive ventilation and length of intensive care unit (ICU) stay. However, optimal timing and rate of deresuscitation remain unclear. Lung ultrasound (LUS) may be used to identify fluid overload. We hypothesise that daily LUS-guided deresuscitation is superior to deresuscitation without LUS in critically ill patients expected to undergo invasive ventilation for more than 24 h in terms of ventilator free-days and being alive at day 28.MethodsThe “effect of lung ultrasound-guided fluid deresuscitation on duration of ventilation in intensive care unit patients” (CONFIDENCE) is a national, multicentre, open-label, randomised controlled trial (RCT) in adult critically ill patients that are expected to be invasively ventilated for at least 24 h. Patients with conditions that preclude a negative fluid balance or LUS examination are excluded. CONFIDENCE will operate in 10 ICUs in the Netherlands and enrol 1000 patients. After hemodynamic stabilisation, patients assigned to the intervention will receive daily LUS with fluid balance recommendations. Subjects in the control arm are deresuscitated at the physician’s discretion without the use of LUS. The primary endpoint is the number of ventilator-free days and being alive at day 28. Secondary endpoints include the duration of invasive ventilation; 28-day mortality; 90-day mortality; ICU, in hospital and total length of stay; cumulative fluid balance on days 1–7 after randomisation and on days 1–7 after start of LUS examination; mean serum lactate on days 1–7; the incidence of reintubations, chest drain placement, atrial fibrillation, kidney injury (KDIGO stadium ≥ 2) and hypernatremia; the use of invasive hemodynamic monitoring, and chest-X-ray; and quality of life at day 28.DiscussionThe CONFIDENCE trial is the first RCT comparing the effect of LUS-guided deresuscitation to routine care in invasively ventilated ICU patients. If proven effective, LUS-guided deresuscitation could improve outcomes in some of the most vulnerable and resource-intensive patients in a manner that is non-invasive, easy to perform, and well-implementable.Trial registrationClinicalTrials.gov NCT05188092. Registered since January 12, 2022
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Digital innovations in the field of immersive Augmented Reality (AR) can be a solution to offer adults who are mentally, physically or financially unable to attend sporting events such as premier league football a stadium and match experience. This allows them to continue to connect with their social networks. In the intended project, AR content will be further developed with the aim of evoking the stadium experience of home matches as much as possible. The extent to which AR enriches the experience is then tested in an experiment, in which the experience of a football match with and without AR enrichment is measured in a stadium setting and in a home setting. The experience is measured with physiological signals. In addition, a subjective experience measure is also being developed and benchmarked (the experience impact score). Societal issueInclusion and health: The joint experience of (top) sports competitions forms a platform for vulnerable adults, with a limited social capital, to build up and maintain the social networks that are so necessary for them. AR to fight against social isolation and loneliness.
Geavanceerde hoortesten die worden ingezet om slechthorendheid te diagnosticeren en hoortoestellen af te regelen worden standaard uitgevoerd door een geoefend professional in een face-to-face consult. In de context van Covid-19 afstandsmaatregelen brengt dit voor vele slechthorenden een belangrijk gezondheidsrisico mee. Dit is in het bijzonder zo voor de kwetsbare groep van 65-plussers met slechthorendheid die vaak bijkomende aandoeningen hebben. Zij kiezen er om die reden niet zelden voor om te verzaken aan de noodzakelijke hoorzorg. De centrale doelstelling van dit project is om een objectief meetinstrument te ontwikkelen om spraakverstaan geautomatiseerd en online te toetsen. Deze testprocedure dient een valide alternatief te vormen voor face-to-face testconsults. De resultaten van deze online test dienen professionals toe te laten om het functionele horen van cliënten op afstand in kaart te brengen en zo nodig te optimaliseren dankzij een aangepaste fijnstelling van het hoortoestel. Aldus biedt het online testen voor bepaalde groepen van slechthorende cliënten een kostenefficiënte en veilige manier om communicatief zo goed mogelijk aangesloten te blijven op de maatschappij. De beide praktijkpartners zullen aan de hand van surveys eerst de belangrijkste communicatieve uitdagingen en behoeften van de doelgroep van slechthorenden in kaart brengen. De resultaten hiervan leveren de nodige input voor het gericht ontwikkelen van het testinstrumentarium en de experimentele testcondities. Om de nieuwe testprocedure te valideren zullen tot slot de spraakverstaanscores van state-of-the-art manuele on-site procedures worden vergeleken met deze van een geautomatiseerde online procedure. Verder willen we de PPS samenwerking consolideren en een belangrijke opstap maken naar breedschalig vervolgonderzoek binnen het ‘SME Instrument’ van het Horizon Europe Programma 2021-2017 met als doel een volledig aanbod van online audiologische revalidatie te bieden. Dankzij dit KIEM project kan een eerste cruciale stap worden gezet in het deblokkeren van de rechte lijn naar dit einddoel.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
