[Elaboration and psychometric properties of a well-being scale at work. The Serenat study among employees in occupational medicine unit]. / Élaboration et qualités psychométriques d'une échelle de bien-être au travail. Étude SERENAT auprès de salariés vus en médecine du travail.

BACKGROUND: Well-being at work is nowadays a major public health challenge. It includes, among others, absence of psychological (anxio-depressive) symptoms, perceived positive work conditions (environment and organization), happiness and good quality of life at work. Many studies have shown that social support and control at work protect mental health while high job demands and effort-reward imbalance are risk factors for anxiety and depression. There is currently no global indicator to measure both the state of mental health and social working conditions. The main objective of this work is to construct and explore the psychometric properties of scale of well-being at work called "Serenat" in order to validate it. METHODS: The Serenat Scale is a self-report questionnaire composed of 20 items. All items are scored on a four-point Likert scale ranging from 0 (strongly disagree) to 3 (strongly agree) resulting in a range of 0 to 60. It was constructed from data collected from the literature and from consultations in an Occupational Health Unit. From January 2014 to May 2017 193 subjects who have consulted an occupational doctor are included in this cross sectional survey. Validation included item quality and data structure diagnosis, internal consistency, intraobserver reliability evaluation and external consistency. RESULTS: The Serenat scale showed very good item quality, with a maximal non-response rate of 0.01 % per item, and no floor effect. Factor analysis concluded that the scale can be considered unidimensional. Cronbach's alpha of internal consistency was 0.89. The intraclass correlation coefficient for intraobserver reliability was 0.89. Serenat scale was correlated with HADS (r=-0.54; P<0.001), STAI-Y (r=-0.78; P<0.001) and BDI-13 (r=-0.57; P<0.001). CONCLUSION: Serenat's well-being at work scale shows good psychometric properties for final validation. It could be useful to occupational physicians for individual and collective screening. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02905071.

A hospital wide predictive model for unplanned readmission using hierarchical ICD data.

BACKGROUND AND OBJECTIVE: Hospitals already acquire a large amount of data, mainly for administrative, billing and registration purposes. Tapping on these already available data for additional purposes, aiming at improving care, without significant incremental effort and cost. This potential of secondary patient data is explored through modeling administrative and billing data, as well as the hierarchical structure of pathology codes of the International Classification of Diseases (ICD) in the prediction of unplanned readmissions, as a clinically relevant outcome parameter that can be impacted on in a quality improvement program. METHODS: In this single-center, hospital-wide observational cohort study, we included all adult patients discharged in 2016 after applying an exclusion protocol (n = 29,702). In addition to administrative variables, such as age and length of stay, structured pathology data were taken into account in predictive models. As a first research question, we compared logistic regression against penalized logistic regression, gradient boosting and Random Forests to predict unplanned readmission. As a second research goal, we investigated the level of hierarchy within the pathology data needed to achieve the best accuracy. Finally, we investigated which prediction variables play a prominent role in predicting hospital readmission. The performance of all models was evaluated using the Area Under the ROC Curve (AUC) measure. RESULTS: All models have the best predictive results using Random Forests. An added value of 7% is observed compared to a baseline method such as logistic regression. The best model, based on Random Forests, achieved an AUC of 0.77, using the diagnosis category and procedure code as lowest level of the hierarchical pathology data. CONCLUSIONS: The most accurate model to predict hospital wide unplanned readmission is based on Random Forests and includes the ICD hierarchy, especially diagnosis category. Such an approach lowers the number of predictor variables and yields a higher interpretability than a model based on a detailed diagnosis. The performance of the model proved high enough to be used as a decision support tool.

Survival and function of mesenchymal stem cells (MSCs) depend on glucose to overcome exposure to long-term, severe and continuous hypoxia.

Use of mesenchymal stem cells (MSCs) has emerged as a potential new treatment for various diseases but has generated marginally successful results. A consistent finding of most studies is massive death of transplanted cells. The present study examined the respective roles of glucose and continuous severe hypoxia on MSC viability and function with respect to bone tissue engineering. We hereby demonstrate for the first time that MSCs survive exposure to long-term (12 days), severe (pO(2) < 1.5 mmHg) hypoxia, provided glucose is available. To this end, an in vitro model that mimics the hypoxic environment and cell-driven metabolic changes encountered by grafted sheep cells was established. In this model, the hallmarks of hypoxia (low pO(2) , hypoxia inducible factor-1α expression and anaerobic metabolism) were present. When conditions switched from hypoxic (low pO(2) ) to ischemic (low pO(2) and glucose depletion), MSCs exhibited shrinking, decreased cell viability and ATP content due to complete exhaustion of glucose at day 6; these results provided evidence that ischemia led to the observed massive cell death. Moreover, MSCs exposed to severe, continuous hypoxia, but without any glucose shortage, remained viable and maintained both their in vitro proliferative ability after simulation with blood reperfusion at day 12 and their in vivo osteogenic ability. These findings challenge the traditional view according to which severe hypoxia per se is responsible for the massive MSC death observed upon transplantation of these cells and provide evidence that MSCs are able to withstand exposure to severe, continuous hypoxia provided that a glucose supply is available.