PURPOSE: The Nasality Severity Index 2.0 (NSI 2.0) forms a new, multiparametric approach in the assessment of hypernasality. To enable clinical implementation of this index, the short- and long-term test-retest reliability of this index was explored. METHODS: In 40 normal-speaking adults (mean age 32y, SD 11, 18-56y) and 29 normal-speaking children (mean age 8y, SD 2, 4-12y), the acoustic parameters included in the NSI 2.0 (i.e. nasalance of the vowel /u/ and an oral text, and the voice low tone to high tone ratio (VLHR) of the vowel /i/) were obtained twice at the same test moment and during a second assessment two weeks later. After determination of the NSI 2.0, a comprehensive set of statistical measures was applied to determine its reliability. RESULTS: Long-term variability of the NSI 2.0 and its parameters was slightly higher compared to the short-term variability, both in adults and in children. Overall, a difference of 2.82 for adults and 2.68 for children between the results of two consecutive measurements can be interpreted as a genuine change. With an ICC of 0.84 in adults and 0.77 in children, the NSI 2.0 additionally shows an excellent relative consistency. No statistically significant difference was withheld in the reliability of test-retest measurements between adults and children. CONCLUSION: Reliable test-retest measurements of the NSI 2.0 can be performed. Consequently, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores can be reliably compared and interpreted. LEARNING OUTCOMES: The reader will be able to describe and discuss both the short-term and long-term test-retest reliability of the Nasality Severity Index 2.0, a new multiparametric approach to hypernasality, and its parameters. Based on this information, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores, e.g. before and after surgery or speech therapy, can be compared and interpreted.
PURPOSE: We investigated changes in ARDS severity and associations with outcome in COVID-19 ARDS patients.METHODS: We compared outcomes in patients with ARDS classified as 'mild', 'moderate' or 'severe' at calendar day 1, and after reclassification at calendar day 2. The primary endpoint was 28-day mortality. We also identified which ventilatory parameters had an association with presence of severe ARDS at day 2. We repeated the analysis for reclassification at calendar day 4.RESULTS: Of 895 patients, 8.5%, 60.1% and 31.4% had mild, moderate and severe ARDS at day 1. These proportions were 13.5%, 72.6% and 13.9% at day 2. 28-day mortality was 25.3%, 31.3% and 32.0% in patients with mild, moderate and severe ARDS at day 1 (p = 0.537), compared to 28.6%, 29.2% and 44.3% in patients reclassified at day 2 (p = 0.005). No ventilatory parameter had an independent association with presence of severe ARDS at day 2. Findings were not different reclassifying at day 4.CONCLUSIONS: In this cohort of COVID-19 patients, ARDS severity and mortality between severity classes changed substantially over the first 4 days of ventilation. These findings are important, as reclassification could help identify target patients that may benefit from alternative approaches.
QUESTION: In adults who experience nocturnal leg cramps, does stretching of the calf and hamstring muscles each day just before sleep reduce the frequency and severity of the cramps?DESIGN: A randomised trial with concealed allocation and intention-to-treat analysis.PARTICIPANTS: Eighty adults aged over 55 years with nocturnal leg cramps who were not being treated with quinine.INTERVENTION: The experimental group performed stretches of the calf and hamstring muscles nightly, immediately before going to sleep, for six weeks. The control group performed no specific stretching exercises. Both groups continued other usual activities.OUTCOME MEASURES: Participants recorded the frequency of nocturnal leg cramps in a daily diary. Participants also recorded the severity of the pain associated with nocturnal leg cramps on a 10-cm visual analogue scale. Adverse events were also recorded.RESULTS: All participants completed the study. At six weeks, the frequency of nocturnal leg cramps decreased significantly more in the experimental group, mean difference 1.2 cramps per night (95% CI 0.6 to 1.8). The severity of the nocturnal leg cramps had also decreased significantly more in the experimental group than in the control group, mean difference 1.3 cm (95% CI 0.9 to 1.7) on the 10-cm visual analogue scale.CONCLUSION: Nightly stretching before going to sleep reduces the frequency and severity of nocturnal leg cramps in older adults.
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
In the SensEQuake project, the Research Centre for Built Environment NoorderRuimte of Hanze University of Applied Sciences, StabiAlert, Target Holding and NHL Stenden Leeuwarden are investigating the following question:How can we provide relevant and understandable information to support decision makers when an earthquake has occurred?In case of a crisis such as an earthquake, parties such as the provincial government, large company sites, airports or hospitals need information on the scope and severity of the effect of the crisis.Systematic updates of the actual situation on site are of the essence for emergency services. At present only a small amount of the data necessary for this information needed is being collected. And the data that is collected is not processed into relevant and easily understandable information for the decision makers. This project aims to fill this gap.The objective of the project is to integrate the existing sensor technologies into a decision support system, allowing a wider and more immediate use of sensor data for public interest, particularly in crisis times.A heat-map will be produced based on scenario earthquakes and loss (hazard and risk assessment) estimation tools. After running several scenario quakes, critical points in respect to the expected damages and the distribution of existing sensors will be defined. More sensors in critical locations will also be placed to create a high enough resolution.
