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.
The Nutri-Score front-of-pack label, which classifies the nutritional quality of products in one of 5 classes (A to E), is one of the main candidates for standardized front-of-pack labeling in the EU. The algorithm underpinning the Nutri-Score label is derived from the Food Standard Agency (FSA) nutrient profile model, originally a binary model developed to regulate the marketing of foods to children in the UK. This review describes the development and validation process of the Nutri-Score algorithm. While the Nutri-Score label is one of the most studied front-of-pack labels in the EU, its validity and applicability in the European context is still undetermined. For several European countries, content validity (i.e., ability to rank foods according to healthfulness) has been evaluated. Studies showed Nutri-Score's ability to classify foods across the board of the total food supply, but did not show the actual healthfulness of products within different classes. Convergent validity (i.e., ability to categorize products in a similar way as other systems such as dietary guidelines) was assessed with the French dietary guidelines; further adaptations of the Nutri-Score algorithm seem needed to ensure alignment with food-based dietary guidelines across the EU. Predictive validity (i.e., ability to predict disease risk when applied to population dietary data) could be re-assessed after adaptations are made to the algorithm. Currently, seven countries have implemented or aim to implement Nutri-Score. These countries appointed an international scientific committee to evaluate Nutri-Score, its underlying algorithm and its applicability in a European context. With this review, we hope to contribute to the scientific and political discussions with respect to nutrition labeling in the EU.
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Purpose: The primary aim of this study was to investigate the concurrent validity of the PAM AM400 accelerometer for measuring physical activity in usual care in hospitalized patients by comparing it with the ActiGraph wGT3X-BT accelerometer. Materials and methods: This was a prospective single centre observational study performed at the University Medical Centre Utrecht in The Netherlands. Patients admitted to different clinical wards were included. Intraclass Correlation Coefficients (ICCs) were computed using a two-way mixed model with random subjects. Additionally, Bland-Altman plots were made to visualize the level of agreement of the PAM with the ActiGraph. To test for proportional bias, a regression analysis was performed. Results: In total 17 patients from different clinical wards were included in the analyses. The level of agreement between the PAM and ActiGraph was found strong with an ICC of 0.955. The Bland-Altman analyses showed a mean difference of 1.12min between the two accelerometers and no proportional bias (p¼0.511). Conclusions: The PAM is a suitable movement sensor to validly measure the active minutes of hospitalized patients. Implementation of this device in daily care might be helpful to change the immobility culture in hospitals.
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The Water Framework Directive imposes challenges regarding the environmental risk of plastic pollution. The quantification, qualification, monitoring, and risk assessment of nanoplastics and small microplastic (<20 µm) is crucial. Environmental nano- and micro-plastics (NMPs) are highly diverse, accounting for this diversity poses a big challenge in developing a comprehensive understanding of NMPs detection, quantification, fate, and risks. Two major issues currently limit progress within this field: (a) validation and broadening the current analytical tools (b) uncertainty with respect to NMPs occurrence and behaviour at small scales (< 20 micron). Tracking NMPs in environmental systems is currently limited to micron size plastics due to the size detection limit of the available analytical techniques. There are currently no methods that can detect nanoplastics in real environmental systems. A major bottleneck is the incompatibility between commercially available NMPs and those generated from plastic fragments degradation in the environment. To track nanoplastics in environmental and biological systems, some research groups synthesized metal-doped nanoplastics, often limited to one polymer type and using high concentrations of surfactants, rendering these synthesized nanoplastics to not be representative of nanoplatics found in real environment. NanoManu proposes using Electrohydrodynamic Atomization to generate metal doped NMPs of different polymers types, sizes, and shapes, which will be representative of the real environmental nanoplastics. The synthesized nanoplastics will be used as model particles in environmental studies. The synthesized nanoplastics will be characterized and tested using different analytical methods, e.g., SEM-EDX, TEX, GCpyrMS, FFF, µFTIR and SP-ICP-MS. NanoManu is a first and critical step towards generating a comprehensive state-of-the-art analytical and environmental knowledge on the environmental fate and risks of nanoplastics. This knowledge impacts current risk assessment tools, efficient interventions to limit emissions and adequate regulations related to NMPs.
