Background: Magnetic resonance imaging (MRI) is being used extensively in the search for pathoanatomical factors contributing to low back pain (LBP) such as Modic changes (MC). However, it remains unclear whether clinical findings can identify patients with MC. The purpose of this explorative study was to assess the predictive value of six clinical tests and three questionnaires commonly used with patients with low-back pain (LBP) on the presence of Modic changes (MC).Methods: A retrospective cohort study was performed using data from Dutch military personnel in the period between April 2013 and July 2016. Questionnaires included the Roland Morris Disability Questionnaire, Numeric Pain Rating Scale, and Pain Self-Efficacy Questionnaire. The clinical examination included (i) range of motion, (ii) presence of pain during flexion and extension, (iii) Prone Instability Test, and (iv) straight leg raise. Backward stepwise regression was used to estimate predictive value for the presence of MC and the type of MC. The exploration of clinical tests was performed by univariable logistic regression models.Results: Two hundred eighty-six patients were allocated for the study, and 112 cases with medical records and MRI scans were available; 60 cases with MC and 52 without MC. Age was significantly higher in the MC group. The univariate regression analysis showed a significantly increased odds ratio for pain during flexion movement (2.57 [95% confidence interval (CI): 1.08-6.08]) in the group with MC. Multivariable logistic regression of all clinical symptoms and signs showed no significant association for any of the variables. The diagnostic value of the clinical tests expressed by sensitivity, specificity, positive predictive, and negative predictive values showed, for all the combinations, a low area under the curve (AUC) score, ranging from 0.41 to 0.53. Single-test sensitivity was the highest for pain in flexion: 60% (95% CI: 48.3-70.4).Conclusion: No model to predict the presence of MC, based on clinical tests, could be demonstrated. It is therefore not likely that LBP patients with MC are very different from other LBP patients and that they form a specific subgroup. However, the study only explored a limited number of clinical findings and it is possible that larger samples allowing for more variables would conclude differently.
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
Background: Due to differences in the definition of frailty, many different screening instruments have been developed. However, the predictive validity of these instruments among community-dwelling older people remains uncertain. Objective: To investigate whether combined (i.e. sequential or parallel) use of available frailty instruments improves the predictive power of dependency in (instrumental) activities of daily living ((I)ADL), mortality and hospitalization. Design, setting and participants: A prospective cohort study with two-year followup was conducted among pre-frail and frail community-dwelling older people in the Netherlands. Measurements: Four combinations of two highly specific frailty instruments (Frailty Phenotype, Frailty Index) and two highly sensitive instruments (Tilburg Frailty Indicator, Groningen Frailty Indicator) were investigated. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for all single instruments as well as for the four combinations, sequential and parallel. Results: 2,420 individuals participated (mean age 76.3 ± 6.6 years, 60.5% female) in our study. Sequential use increased the levels of specificity, as expected, whereas the PPV hardly increased. Parallel use increased the levels of sensitivity, although the NPV hardly increased. Conclusions: Applying two frailty instruments sequential or parallel might not be a solution for achieving better predictions of frailty in community-dwelling older people. Our results show that the combination of different screening instruments does not improve predictive validity. However, as this is one of the first studies to investigate the combined use of screening instruments, we recommend further exploration of other combinations of instruments among other study populations.
The postdoc candidate, Sondos Saad, will strengthen connections between research groups Asset Management(AM), Data Science(DS) and Civil Engineering bachelor programme(CE) of HZ. The proposed research aims at deepening the knowledge about the complex multidisciplinary performance deterioration prediction of turbomachinery to optimize cleaning costs, decrease failure risk and promote the efficient use of water &energy resources. It targets the key challenges faced by industries, oil &gas refineries, utility companies in the adoption of circular maintenance. The study of AM is already part of CE curriculum, but the ambition of this postdoc is that also AM principles are applied and visible. Therefore, from the first year of the programme, the postdoc will develop an AM material science line and will facilitate applied research experiences for students, in collaboration with engineering companies, operation &maintenance contractors and governmental bodies. Consequently, a new generation of efficient sustainability sensitive civil engineers could be trained, as the labour market requires. The subject is broad and relevant for the future of our built environment being more sustainable with less CO2 footprint, with possible connections with other fields of study, such as Engineering, Economics &Chemistry. The project is also strongly contributing to the goals of the National Science Agenda(NWA), in themes of “Circulaire economie en grondstoffenefficiëntie”,”Meten en detecteren: altijd, alles en overall” &”Smart Industry”. The final products will be a framework for data-driven AM to determine and quantify key parameters of degradation in performance for predictive AM strategies, for the application as a diagnostic decision-support toolbox for optimizing cleaning &maintenance; a portfolio of applications &examples; and a new continuous learning line about AM within CE curriculum. The postdoc will be mentored and supervised by the Lector of AM research group and by the study programme coordinator(SPC). The personnel policy and job function series of HZ facilitates the development opportunity.
