It is important that sick children are screened for malnutrition and growth faltering to ensure that those in need of intervention receive optimal healthcare. Paediatric malnutrition screening tools and the acquisition and plotting of anthropometric measurements are not routinely used and this means that, at best, there is substantial reliance placed on visual inspection when it comes to screening for nutritional problems (1). The extent to which healthcare professionals can visually identify children whose physique deviates from norms, and how valid this approach would be for routine screening on hospital admission, has not been studied thoroughly, particularly in sick children. This study assessed how well healthcare professionals screened children for short stature, underweight and over nutrition by just using visual inspection. We also explored the implications of substituting anthropometric measurements with visual inspection when a popular paediatric malnutrition screening tool was used.An available convenience sample of sick children admitted for care to the inpatient wards of the Royal Hospital for Sick Children, Glasgow, UK, were recruited. Anthropometry was obtained as described previously (2) and used to classify children using body mass index (BMI) based on 1990 UK data: underweight (BMI < 2 nd centile), normal (BMI 2 nd to 91 st centile), overweight (BMI 91 st to 98 th centile) and obese (BMI > 98 th centile). A height measurement below the 2 nd centile was classified as short stature. The children's risk of malnutrition was classified as low risk, medium risk or high risk, according to the Paediatric Yorkhill Malnutrition Score (PYMS) (2).Attending dieticians, doctors and nurses were informed about the study and were invited to participate. A study researcher and the healthcare professionals independently commented on the physique of the participant based only on a visual inspection. As the
Accepted ArticleThis article is protected by copyright. All rights reserved. results of the researcher were comparable with those of the healthcare professionals ( Figure 1), only the latter, which have clinical relevance, are presented in the text.The researcher also provided a numerical estimation of each child's height and weight, which was compared with their actual measurements. Visual inspection was performed prior to measuring each child. Neither the researcher nor the healthcare professionals were aware of the child's clinical condition prior to performing the assessment.Children, their carers and all healthcare professionals provided written informed consent.Ethical approval was obtained from the West of Scotland Research Ethics Committee (Reference: 13-WS-0327).The proportion of children misclassified between visual inspection and objective assessments was calculated and sensitivity, specificity, positive predictive values and negative predictive values were computed. Associations between height and weight prediction errors relating to age and actual measurements were explored with the Pearson correla...
Aims:We investigated a point of admission metric of glycemia, the Admission Glucose Number (AGN), and its relationship with both high risk inpatient glucose patterns and mortality in hospital inpatients with type 2 diabetes (T2DM).
Methods:Inpatient capillary blood glucose (CBG) data for patients with T2DM in our health board were identified for a 5-year period and associated with most recent preadmission HbA1c. AGN was calculated as first CBG measured during admission (mmol/L), subtracted from most recent preadmission HbA1c (converted to estimated median glucose mmol/l) within 15 months preadmission. The association between AGN and CBG variability (interquartile range), hypoglycemia free survival (HR) and both inpatient and 100-day mortality (HR) were investigated.Results: A total of 21 045 first admissions with available HbA1c data were identified. A positive correlation between AGN and glycemic variability was described (partial correlation coefficient 0.25, P < .001), which was stronger than the correlation of either of AGNs' individual components: adjusted CBG1 = 0.07 (P < .001), eAG = 0.08 (P < .001). The hazard ratio for time to first recorded CBG < 3 mmol/L for high AGN versus low AGN was 1.74 (95% CI 1.55-1.96), P < .001. A high AGN was associated with increased 100-day mortality (HR 1.26, P = .005), however not with in-hospital mortality (HR = 1.31, P = .08).
Conclusion:AGN is a simple metric that combines 2 readily available measures associated with adverse outcome in T2DM. AGN may be a useful tool to stratify patients for risk of hypoglycemia and postdischarge death.
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