Frequency of food allergy in Europe:an updated systematic review and meta-analysis

Food allergy (FA) is increasingly reported in Europe, however, the latest prevalence estimates were based on studies published a decade ago. The present work provides the most updated estimates of the prevalence and trends of FA in Europe. Databases were searched for studies published between 2012 and 2021, added to studies published up to 2012. In total, 110 studies were included in this update. Most studies were graded as moderate risk of bias. Pooled lifetime and point prevalence of self-reported FA were 19.9% (95% CI 16.6–23.3) and 13.1% (95% CI 11.3–14.8), respectively. The point prevalence of sensitization based on specific IgE (slgE) was 16.6% (95% CI 12.3–20.8), skin prick test (SPT) 5.7% (95% CI 3.9–7.4), and positive food challenge 0.8% (95% CI 0.5–0.9). While lifetime prevalence of self-reported FA and food challenge positivity only slightly changed, the point prevalence of self-reported FA, sIgE and SPT positivity increased from previous estimates. This may reflect a real increase, increased awareness, increased number of foods assessed, or increased number of studies from countries with less data in the first review. Future studies require rigorous designs and implementation of standardized methodology in diagnosing FA, including use of double-blinded placebo-controlled food challenge to minimize potential biases

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

Introduction: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae. Study methods: We enrolled 257 critically ill children. Nutritional status/vital signs/biochemical values were recorded. We used IC to measure REE. Commonly employed equations/formulae and the VCO2-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system. Results: ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R2 = 0.71) but not better than Talbot tables for weight. After adding vital signs/biochemical values, the model became superior to all equations/formulae (accuracy 82.3%, R2 = 0.80) and comparable to the Mehta equation. Including IC-measured VCO2 increased the accuracy to 89.6%, superior to the Mehta equation. Conclusions: We described the accuracy of REE prediction using models that include demographic/anthropometric/clinical/metabolic variables. ANN may represent a reliable option for REE estimation, overcoming the inaccuracies of traditional predictive equations/formulae

Joint Effort towards Preventing Nutritional Deficiencies at the Extremes of Life during COVID-19

The COVID-19 (Coronavirus disease 2019) pandemic is posing a threat to communities and healthcare systems worldwide. Malnutrition, in all its forms, may negatively impact the susceptibility and severity of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection in both children and older adults. Both undernutrition and obesity have been evoked as conditions associated with a higher susceptibility to the infection and poor prognosis. In turn, the COVID-19 infection may worsen the nutritional status through highly catabolic conditions, exposing individuals to the risk of malnutrition, muscle wasting, and nutritional deficiencies. Accordingly, the relationship between malnutrition and COVID-19 is likely to be bidirectional. Furthermore, the modification of nutritional behaviors and physical activity, required to limit the spread of the virus, are posing a challenge to health at both the extremes of life. Thus far, even the most advanced healthcare systems have failed to address the alarming consequences of malnutrition posed by this pandemic. If not properly addressed, we may run the risk that new and old generations will experience the consequences of COVID-19 related malnutrition

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

Introduction: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae. Study methods: We enrolled 257 critically ill children. Nutritional status/vital signs/biochemical values were recorded. We used IC to measure REE. Commonly employed equations/formulae and the VCO2-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system. Results: ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R2 = 0.71) but not better than Talbot tables for weight. After adding vital signs/biochemical values, the model became superior to all equations/formulae (accuracy 82.3%, R2 = 0.80) and comparable to the Mehta equation. Including IC-measured VCO2 increased the accuracy to 89.6%, superior to the Mehta equation. Conclusions: We described the accuracy of REE prediction using models that include demographic/anthropometric/clinical/metabolic variables. ANN may represent a reliable option for REE estimation, overcoming the inaccuracies of traditional predictive equations/formulae

Frequency of food allergy in Europe: An updated systematic review and meta-analysis

Food allergy (FA) is increasingly reported in Europe, however, the latest prevalence estimates were based on studies published a decade ago. The present work provides the most updated estimates of the prevalence and trends of FA in Europe. Databases were searched for studies published between 2012 and 2021, added to studies published up to 2012. In total, 110 studies were included in this update. Most studies were graded as moderate risk of bias. Pooled lifetime and point prevalence of self-reported FA were 19.9% (95% CI 16.6–23.3) and 13.1% (95% CI 11.3–14.8), respectively. The point prevalence of sensitization based on specific IgE (slgE) was 16.6% (95% CI 12.3–20.8), skin prick test (SPT) 5.7% (95% CI 3.9–7.4), and positive food challenge 0.8% (95% CI 0.5–0.9). While lifetime prevalence of self-reported FA and food challenge positivity only slightly changed, the point prevalence of self-reported FA, sIgE and SPT positivity increased from previous estimates. This may reflect a real increase, increased awareness, increased number of foods assessed, or increased number of studies from countries with less data in the first review. Future studies require rigorous designs and implementation of standardized methodology in diagnosing FA, including use of double-blinded placebo-controlled food challenge to minimize potential biases