Background It has been demonstrated that children who had been breastfed remain better protected against various infections, and notably respiratory tract infections, well beyond infancy. Since the role of breastfeeding to explain why children are less affected by COVID-19 has not been studied until now, the aim of this study was to determine whether any history of breastfeeding reduces the incidence rate of COVID-19 in children. Methods This was a secondary analysis of an observational study on clinical and epidemiological characteristics of pediatric COVID-19 in Majorca. A total of 691 children were recruited during the 5 months of August–December 2020. Eligible participants were children under 14 who were tested for SARS-CoV-2 in pediatric emergency services. The independent explanatory variable was any breastfeeding. Bivariate analyses were conducted through the Chi-square test, the Fisher’s Exact test or the Student’s T test. All children had the same demographic, epidemiological and clinical data collected through a study team member interview and via the participants medical records. Results Within the sample of children who visited emergency services with symptoms of potential COVID-19, we found higher prevalence of positive SARS-CoV-2 RT-PCR test results among those who were exclusively formula fed compared with those who were ever breastfed (OR 2.48; 95% CI 1.45, 3.51; P = 0.036). Conclusions The present study suggests that ever breastfeeding reduces the risk of COVID-19 among children, as documented for other infections.
In Reply In our study, 1 we pursued an exhaustive crossvalidated grid search to identify the optimal hyperparameters for the extreme gradient descent boosting model (XG-B oost), a standard approach to the selection of hyperparameters that included searching over the learning rate, number of trees trained, maximum tree depth, and minimum loss reduction required for partition on a leaf node on a tree. 2 To permit comparison of area under the receiver operator characteristic (AUROC) curves, we focused on defining their variance in iterative cross validation and reported as a 95% CI. Moreover, this approach allowed comparison of other metrics, such as the precision and recall, using a consistent approach for reporting confidence intervals. As reported in the study, XGBoost did not have better discrimination for inhospital mortality in acute myocardial infarction (AMI) than a logistic regression model (XGBoost: AUROC, 0.89; 95% CI, 0.88-0.89; logistic regression: AUROC, 0.88; 95% CI, 0.88-0.88) despite the large sample size and selection of optimal hyperparameters. 1 For the neural network, we chose a routinely used model architecture-a feedforward artificial neural network-that we then trained for predicting in-hospital mortality in AMI. 3 The network was composed of 5 fully connected hidden layers, each with 100 nodes and a rectified linear unit activation, and an output layer with a sigmoid activation function. While we could have built a more complex neural network by incorporating dropout layers and deeper, more complex architectures, our goal was to test models routinely used for structured data. Moreover, we leveraged the predictive power of individual models in a meta-classifier that was built on learnings from individual models. Therefore, the goal of our work was to evaluate the predictive gain over logistic regression in a series of routinely used models and a meta-learner, rather than iterative design of individual models.With respect to Pieszko and Slomka's question about models among patients with critical illness following AMI, our study proposal was approved for studying the broader population of patients presenting with AMI; models built for other subgroups, such as for those with cardiac arrest and cardiogenic shock, were outside the scope of our work.In sum, our findings are an accurate representation of the limited predictive gain from the use of machine learning in predictive models built with registry data. However, this result is not a verdict on machine learning but a reflection of models built on low-dimensional data that are currently manually abstracted into a set number of discrete fields, 4 as is currently done for clinical registries.
PurposeThis study aims to evaluate the need to modify the levothyroxine dose after surgery. Additional goals are to estimate the increase or decrease of total dose and weight-adjusted dose, assess the influence of weight loss on the levothyroxine requirements, and identify predictors.Material and methodsRetrospective study in patients with treated hypothyroidism that underwent bariatric surgery. The required levothyroxine dose was evaluated before the surgery and at 6, 12, and 24 months post-surgery. Dose modification during follow-up and its association with weight loss and other potential predictors were assessed. ResultsOf the 63 patients included, 82.54% needed an adjustment of dose during the follow-up. The total dose of levothyroxine decreased significantly at 6 months post-surgery (-49.1 µg/week; 95%CI= -93.7 to -4.5; p=0.031) and 12 months (-54.9 µg/week; 95%CI= -102 to -7.8; p=0.022), with no significant change at 24 months (p=0.114). The weight-adjusted dose increased at 6 months (1.37 µg/kg/week; 95%CI=0.91 to 1.83; p<0.001), 12 months (2.05 µg/kg/week; 95%CI=1.43 to 2.67; p<0.001), and 24 months (2.52 µg/kg/week; 95%CI=1.74 to 3.30; p<0.001). Weight loss showed significant association with weight-adjusted dose (OR=1.07; 95%CI=1.02 to 1.12; p=0.004) and did not with the total dose (p=0.320).ConclusionsIt is expected that the levothyroxine requirements change in the firsts years after bariatric surgery. This study shows a significant decrease in the total dose during the first year of follow-up and an increase in the weight-adjusted dose over the two first years. No predictors for the modification in the total dose of levothyroxine have been identified.
Background: It has been demonstrated that children who had been breastfed remain better protected against various infections, and notably respiratory tract infections, well beyond infancy. Since the role of breastfeeding to explain why children are less affected by COVID-19 has not been studied until now, the aim of this study was to determine whether any history of breastfeeding reduces the incidence rate of COVID-19 in children.Methods: This was a secondary analysis of an observational study on clinical and epidemiological characteristics of pediatric COVID-19 in Majorca. A total of 691 children were recruited. Eligible participants were children under 14 who were tested for SARS-CoV-2 in pediatric emergency services. The independent explanatory variable was initial breastfeeding. Bivariate analyses were conducted through the Chi-square test, the Fisher's Exact test or the Student’s T test. All children had the same demographic, epidemiological and clinical data collected through a study team member interview and via the participants medical records. Aspredicted Trials Registry number is #62721. Results: Within the sample of children who visited emergency services with symptoms of potential COVID-19, we found higher prevalence of positive SARS-CoV-2 RT-PCR test results among those who were exclusively formula fed compared with those who were ever breastfed (OR, 2.48; 95%CI, 1.45-3.51; P=0.036). Conclusions: Since approximately 1 in 60 ever breastfed symptomatic children had tested positive for SARS-CoV-2 versus 1 in 25 never breastfed symptomatic children, this study shows that initially breastfed children remain at lower risk of COVID-19.
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