METHODS: Case information from 192 children was collected from outpatient and inpatient clinics using a survey questionnaire. These included 90 pediatric burn cases and 102 controls who were children without burns. A stepwise logistic regression analysis was used to determine the risk factors for pediatric burns in order to establish a model. The goodness-of-fit for the model was assessed using the Hosmer and Lemeshow test as well as receiver operating characteristic and internal calibration curves. A nomogram was then used to analyze the contribution of each influencing factor to the pediatric burns model.

RESULTS: Seven variables, including gender, age, ethnic minority, the household register, mother's employment status, mother's education and number of children, were analyzed for both groups of children. Of these, age, ethnic minority, mother's employment status and number of children in a household were found to be related to the occurrence of pediatric burns using univariate logistic regression analysis (p 0.2 and variance inflation factor <5 showed that age was a protective factor for pediatric burns [odds ratio (OR) = 0.725; 95% confidence interval (CI): 0.665-0.801]. Compared with single-child parents, those with two children were at greater risk of pediatric burns (OR = 0.389; 95% CI: 0.158-0.959). The ethnic minority of the child and the mother's employment status were also risk factors (OR = 6.793; 95% CI: 2.203-20.946 and OR = 2.266; 95% CI: 1.025-5.012, respectively). Evaluation of the model used was found to be stable. A nomogram showed that the contribution in the children burns model was age > mother's employment status > number of children > ethnic minority.

METHODS: This is a post hoc analysis of a cluster-randomized clinical trial that assesses the effect of implementing a feeding protocol on mortality in critically ill patients. Patients who stayed in the ICUs for at least 7 days and received exclusive EN were included in this analysis. Multivariable Cox hazard regression models and restricted cubic spline models were used to assess the relationship between the different doses of EN delivery and 28-day mortality. Subgroups with varying lactate levels at enrollment were additionally analyzed to address the potential confounding effect brought in by the presence of shock-related hypoperfusion.

RESULTS: Overall, 1322 patients were included in the analysis. The median (interquartile range) daily energy and protein delivery during the first week of enrollment were 14.6 (10.3-19.6) kcal/kg and 0.6 (0.4-0.8) g/kg, respectively. An increase of 5 kcal/kg energy delivery was associated with a significant reduction (approximately 14%) in 28-day mortality (adjusted hazard ratio [HR] = 0.865, 95% confidence interval [CI]: 0.768-0.974, P = 0.016). For protein intake, a 0.2 g/kg increase was associated with a similar mortality reduction with an adjusted HR of 0.868 (95% CI 0.770-0.979). However, the benefits associated with enhanced nutrition delivery could be observed in patients with lactate concentration ≤ 2 mmol/L (adjusted HR = 0.804 (95% CI 0.674-0.960) for energy delivery and adjusted HR = 0.804 (95% CI 0.672-0.962) for protein delivery, respectively), but not in those > 2 mmol/L.

CONCLUSIONS: During the first week of critical illness, enhanced nutrition delivery is associated with reduced mortality in critically ill patients receiving exclusive EN, only for those with lactate concentration ≤ 2 mmol/L.