Search Results (9)

Introduction: Accurately meeting the energy requirements of patients in intensive care units (ICUs) is crucial to prevent catabolism, muscle loss, and complications. We assessed their energy needs in this study using indirect calorimetry (IC) and predictive formulas, comparing the results with delivered energy intake and evaluating agreement. Materials and Methods: A total of 38 mechanically ventilated patients in seven ICUs at Kayseri City Hospital were included; eligible patients were ≥18 years old and mechanically ventilated for at least 24 h. Disease severity and nutritional risk were evaluated using validated indices (prognostic nutritional index (PNI) and Modified Nutrition Risk in the Critically Ill (mNUTRIC)), and basal energy expenditure (BEE) was measured by IC and calculated using the Harris–Benedict (HB) and ESPEN formulas. IC measurements lasted 15 min under resting conditions in conscious patients and, according to acute phase criteria, in unconscious patients in a quiet, temperature-controlled environment. Nutrition was provided enterally or parenterally based on patient condition and disease severity. Agreement between IC and predictive formulas was assessed using Bland–Altman analysis, a statistical method that evaluates agreement between two measurement techniques. Results: Estimated energy requirements differed significantly from delivered energy intake (p < 0.001). IC-derived values were significantly lower than those estimated by the HB equation and ESPEN recommendations (p < 0.001), suggesting that predictive equations may overestimate energy requirements in this population. By contrast, delivered energy intake was lower than IC-measured values, with a mean difference of approximately 503 kcal, indicating a potential risk of underfeeding in clinical practice. Weak correlations were observed between methods (IC vs. HB: r = 0.35, p = 0.003; IC vs. ESPEN: r = −0.21, p = 0.02), indicating limited agreement between predictive equations and IC measurements, and Passing–Bablok regression analysis further supported this lack of agreement between methods. Conclusions: The energy intake delivered to patients was lower than the calculated values. Indirect calorimetry is important for accurately monitoring and determining energy requirements based on delivered energy intake, and further research in this area is needed. These findings highlight the importance of individualized monitoring of energy expenditure in critically ill patients and suggest that reliance solely on predictive equations may lead to clinically relevant discrepancies in energy delivery. Full article

Background: Vitamin D (VD) insufficiency is present in chronic pancreatitis (CP), leading to increased cardiovascular risk, bone complications, impaired quality of life, and increased mortality. This study aimed to determine the prevalence of VD deficiency in patients with CP and to assess its relationship to CP progression and associated cardiovascular complications. Methods: Seventy patients were enrolled and evaluated for pancreatic exocrine insufficiency by fecal elastase-1, CP severity by M-ANNHEIM classification, cardiovascular risk by 10-year risk mortality scores (SCORE and FRS), and for arterial stiffness using pulse wave velocity (PWV) at a. carotis and a. femoralis. Determination of 25-hydroxyvitamin D was performed by an LC-MS/MS method. Resting energy expenditure was calculated using the Harris–Benedict formula. Results: Mean VD levels were 37.86 ± 24.36 nmol/L (range 3.854–99.874 nmol/L); only five patients were in sufficiency status. VD levels correlated significantly with body mass index (BMI) and resting energy expenditure. In patients with severe structural changes, we observed lower VD levels regardless of etiology (p < 0.01). VD levels were lower in patients with pancreatic exocrine insufficiency (PEI), p < 0.05. Patients with mild CP by M-ANNHEIM had lower levels of VD compared to moderate and advanced CP, p < 0.05. At a cut-off of VD 11.95 nmol/L, we verified pancreatic lithiasis with 89.4% sensitivity, 83.3% specificity, and AUC of 0.826 ± 0.113 (95% CI, 0.61–1). VD status worsened with the increase in the 10-year risk mortality by both SCORE and FRS and PWV, p < 0.05. Conclusions: Most of our patients with CP were VD insufficient. Monitoring of nutritional status in patients with CP is mandatory to prevent the development of malnutrition complications and the associated morbidity and mortality. Full article

Medical nutrition should be tailored to cover a patient’s needs, taking into account medical and organizational possibilities and obstacles. This observational study aimed to assess calories and protein delivery in critically ill patients with COVID-19. The study group comprised 72 subjects hospitalized in the intensive care unit (ICU) during the second and third SARS-CoV-2 waves in Poland. The caloric demand was calculated using the Harris–Benedict equation (HB), the Mifflin–St Jeor equation (MsJ), and the formula recommended by the European Society for Clinical Nutrition and Metabolism (ESPEN). Protein demand was calculated using ESPEN guidelines. Total daily calorie and protein intakes were collected during the first week of the ICU stay. The median coverages of the basal metabolic rate (BMR) during day 4 and day 7 of the ICU stay reached: 72% and 69% (HB), 74% and 76% (MsJ), and 73% and 71% (ESPEN), respectively. The median fulfillment of recommended protein intake was 40% on day 4 and 43% on day 7. The type of respiratory support influenced nutrition delivery. A need for ventilation in the prone position was the main difficulty to guarantee proper nutritional support. Systemic organizational improvement is needed to fulfill nutritional recommendations in this clinical scenario. Full article

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris–Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro–Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women. Full article

Establishing the amount of energy needed to cover the energy demand of children doing sport training and thus ensuring they achieve an even energy balance requires the resting energy expenditure (REE) to be estimated. One of the methods that measures REE is the indirect calorimetry method, which may be influenced by many factors, including body composition, gender, age, height or blood pressure. The aim of the study was to assess the correlation between the resting energy expenditure of children regularly playing football and selected factors that influence the REE in this group. The study was conducted among 219 children aged 9 to 17 using a calorimeter, a device used to assess body composition by the electrical bioimpedance method by means of segment analyzer and a blood pressure monitor. The results of REE obtained by indirect calorimetry were compared with the results calculated using the ready-to-use formula, the Harris Benedict formula. The results showed a significant correlation of girls’ resting energy expenditure with muscle mass and body height, while boys’ resting energy expenditure was correlated with muscle mass and body water content. The value of the REE was significantly higher (p ≤ 0.001) than the value of the basal metabolic rate calculated by means of Harris Benedict formula. The obtained results can be a worthwhile suggestion for specialists dealing with energy demand planning in children, especially among those who are physically active to achieve optimal sporting successes ensuring proper functioning of their body. Full article

A number of equations have been proposed to predict resting energy expenditure (REE). The role of nutritional status in the accuracy and validity of the REE predicted in older patients has been paid less attention. We aimed to compare REE measured by indirect calorimetry (IC) and REE predicted by the Harris–Benedict formula in malnourished older hospitalized patients. Twenty-three malnourished older patients (age range 67–93 years, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Long Form (MNA-SF) score of less than 17. REE was measured (REE_measured) and predicted (REE_predicted) on admission and at discharge. REE_predicted within ±10% of the REE_measured was considered as accuracy. Nutritional support was provided to all malnourished patients during hospitalization. All patients were malnourished with a median MNA-LF score of 14. REE_measured and REE_predicted increased significantly during 2-week nutritional therapy (+212.6 kcal and +19.5 kcal, respectively). Mean REE_predicted (1190.4 kcal) was significantly higher than REE_measured (967.5 kcal) on admission (p < 0.001). This difference disappeared at discharge (p = 0.713). The average REE_predicted exceeded the REE_measured on admission and at discharge by 29% and 11%, respectively. The magnitude of difference between REE_measured and REE_predicted increased along with the degree of malnutrition (r = 0.42, p = 0.042) as deviations ranged from −582 to +310 kcal/day in severe to mildly malnourished patients, respectively. REE_predicted by the Harris–Benedict formula is not accurate in malnourished older hospitalized patients. REE measured by IC is considered precise, but it may not represent the true energy requirements to recover from malnutrition. Therefore, the effect of malnutrition on measured REE must be taken into account when estimating energy needs in these patients. Full article

The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2–17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris–Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (R² = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children. Full article

Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed. Full article

The optimal nutritional support in Acute Kidney Injury (AKI) still remains an open issue. The present study was aimed at evaluating the validity of conventional predictive formulas for the calculation of both energy expenditure and protein needs in critically ill patients with AKI. A prospective, multicenter, observational study was conducted on adult patients hospitalized with AKI in three different intensive care units (ICU). Nutrient needs were estimated by different methods: the Guidelines of the European Society of Parenteral and Enteral Nutrition (ESPEN) for both calories and proteins, the Harris-Benedict equation, the Penn-State and Faisy-Fagon equations for energy. Actual energy and protein needs were repeatedly measured by indirect calorimetry (IC) and protein catabolic rate (PCR) until oral nutrition start, hospital discharge or renal function recovery. Forty-two patients with AKI were enrolled, with 130 IC and 123 PCR measurements obtained over 654 days of artificial nutrition. No predictive formula was precise enough, and Bland-Altman plots wide limits of agreement for all equations highlight the potential to under- or overfeed individual patients. Conventional predictive formulas may frequently lead to incorrect energy and protein need estimation. In critically ill patients with AKI an increased risk for under- or overfeeding is likely when nutrient needs are estimated instead of measured. Full article