Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (= one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
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Background & aims: Low muscle mass and -quality on ICU admission, as assessed by muscle area and -density on CT-scanning at lumbar level 3 (L3), are associated with increased mortality. However, CT-scan analysis is not feasible for standard care. Bioelectrical impedance analysis (BIA) assesses body composition by incorporating the raw measurements resistance, reactance, and phase angle in equations. Our purpose was to compare BIA- and CT-derived muscle mass, to determine whether BIA identified the patients with low skeletal muscle area on CT-scan, and to determine the relation between raw BIA and raw CT measurements. Methods: This prospective observational study included adult intensive care patients with an abdominal CT-scan. CT-scans were analysed at L3 level for skeletal muscle area (cm2) and skeletal muscle density (Hounsfield Units). Muscle area was converted to muscle mass (kg) using the Shen equation (MMCT). BIA was performed within 72 h of the CT-scan. BIA-derived muscle mass was calculated by three equations: Talluri (MMTalluri), Janssen (MMJanssen), and Kyle (MMKyle). To compare BIA- and CT-derived muscle mass correlations, bias, and limits of agreement were calculated. To test whether BIA identifies low skeletal muscle area on CT-scan, ROC-curves were constructed. Furthermore, raw BIA and CT measurements, were correlated and raw CT-measurements were compared between groups with normal and low phase angle. Results: 110 patients were included. Mean age 59 ± 17 years, mean APACHE II score 17 (11–25); 68% male. MMTalluri and MMJanssen were significantly higher (36.0 ± 9.9 kg and 31.5 ± 7.8 kg, respectively) and MMKyle significantly lower (25.2 ± 5.6 kg) than MMCT (29.2 ± 6.7 kg). For all BIA-derived muscle mass equations, a proportional bias was apparent with increasing disagreement at higher muscle mass. MMTalluri correlated strongest with CT-derived muscle mass (r = 0.834, p < 0.001) and had good discriminative capacity to identify patients with low skeletal muscle area on CT-scan (AUC: 0.919 for males; 0.912 for females). Of the raw measurements, phase angle and skeletal muscle density correlated best (r = 0.701, p < 0.001). CT-derived skeletal muscle area and -density were significantly lower in patients with low compared to normal phase angle. Conclusions: Although correlated, absolute values of BIA- and CT-derived muscle mass disagree, especially in the high muscle mass range. However, BIA and CT identified the same critically ill population with low skeletal muscle area on CT-scan. Furthermore, low phase angle corresponded to low skeletal muscle area and -density. Trial registration: ClinicalTrials.gov (NCT02555670).
Objective To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors. Design A prospective cohort study with a 6-month follow-up between January 2020 and March 2023. Setting 30 physiotherapy primary care practices. Participants Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset. Baseline measures Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude. Outcome measures Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain. Results 62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer–Lemeshow test yielded a p-value of 0.7167, indicating a good model fit. Conclusion This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.
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Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
Een belangrijk vraagstuk waar we als samenleving een antwoord op moeten geven is: ?Wat te doen als grondstoffen niet meer voor handen zijn??. Want onze grondstoffenvoorraad is eindig. Het goede antwoord is dan: ?dat lossen we op door het sluiten van kringlopen en de realisatie van een circulaire economie?. Maar hoe doen we dat? Op dit moment weten we nog niet hoe een circulaire economie er idealiter uit zou moeten zien. Veel wordt erover gepraat en geschreven, maar te weinig aandacht bestaat nog voor de toepassing van circulaire processen in de praktijk. En juist in de bouw- en installatiesector, die vaak een conservatieve sector genoemd wordt, staan circulaire toepassingen nog vóór in de innovatie adoptie curve. En uitgerekend deze sector is een enorme materiaal- en grondstoffenverbruiker. De installaties in een gebouw zijn daarbij van groot belang. Bij renovatie van gebouwen heeft maar liefst 50 % van de totale aanneemsom betrekking op installaties. Grote winsten zijn dus te behalen bij de toepassing van circulaire uitgangspunten. De circulaire economie biedt hier kansen. De ontwikkeling van een aantal kennisproducten helpt de samenwerkingspartners in dit onderzoeksproject bij het ontdekken van en stappen zetten op het gebied van een circulaire economie en biedt kansen voor de sector in zijn geheel. Dit onderzoek draagt bij aan de realisatie van de circulaire economie door de ontwikkeling van verschillende kennisproducten. Deze kennisproducten worden ontwikkeld uitgaande van een relevante praktijkcase in de keten onderwijs, onderzoek, bedrijfsleven (Het Utrechtse Model). De praktijkcase is te vinden in de grootschalige renovatie van twee gebouwen. De Hogeschool heeft opdracht gegeven om in 2016 haar eigen gebouwen te renoveren: het gaat in deze samenwerking om de panden van de Hogeschool Utrecht aan de Padualaan 99 en 101 te Utrecht. Bij de aanbesteding van deze renovatie is geen expliciete uitvraag gedaan naar het circulair maken van de renovatie, maar wel naar duurzaamheid en energiebesparende maatregelen. De Hogeschool Utrecht (al drie jaar achtereenvolgend de meest duurzame hogeschool van Nederland ? aldus Studenten van Morgen) ziet deze renovatie als een kans om tevens een bijdrage te kunnen leveren aan de kennis over circulaire processen en circulaire mogelijkheden voor toekomstige renovaties.
Background:Many business intelligence surveys demonstrate that Digital Realities (Virtual reality and Augmented Reality) are becoming a huge market trend in many sectors, and North America is taking the lead in this emerging domain. Tourism is no exception and the sector in Europe must innovate to get ahead of the curve of this technological revolution, but this innovation needs public support.Project partnership:In order to provide labs, startups and SMEs willing to take this unique opportunity with the most appropriate support policies, 9 partner organizations from 8 countries (FR, IT, HU, UK, NO, ES, PL, NL) decided to work together: regional and local authorities, development agencies, private non-profit association and universities.Objective of the project:Thanks to their complementary experiences and know-how, they intend to improve policies of the partner regions (structural funds and regional policies), in order to foster a tourist channeled innovation in the Digital Realities sector.Approach:All partners will work together on policy analysis tasks before exchanging their best initiatives and transferring them from one country to another. This strong cooperation will allow them to build the best conditions to foster innovation thanks to more effective structural funds policies and regional policies.Main activities & outputs:8 policy instruments are addressed, among which 7 relate to structural funds programmes. Basis for exchange of experience: Reciprocal improvement analysis and 8 study trips with peer-review of each partner’s practices. Video reportages for an effective dissemination towards other territories in Europe.Main expected results:At least 16 good practices identified. 8 targeted policy instruments improved. At least 27 staff members will transfer new capacities in their intervention fields. At least 8 involved stakeholders with increased skills and knowledge from exchange of experience. Expected 17 appearances in press and media, including at European level.
