No abstract

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland–Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Pulmonary mechanics and indirect calorimetry measurements are not influenced after uneventful open ETS in well-sedated patients. The E-COVX is able to reliably record spirometry and metabolic indices as early as 5 min after suctioning at different ventilator modes.

Sepsis is a dysregulated host response to infection related to devastating outcomes. Recently, interest has been shifted towards apoptotic and antiapoptotic pathobiology. Apoptosis is executed through the activation of caspases regulated by a number of antiapoptotic proteins, such as survivin. The survivin and caspases’ responses to sepsis have not yet been elucidated. This is a multicenter prospective observational study concerning patients with sepsis (n = 107) compared to patients with traumatic systemic inflammatory response syndrome (SIRS) (n = 75) and to healthy controls (n = 89). The expression of survivin was quantified through real-time quantitative polymerase chain reaction for the different survivin splice variants (wild type-WT, ΔEx3, 2B, 3B) in peripheral blood leukocytes. The apoptotic or antiapoptotic tendency was specified by measuring survivin-WT, caspase-3, and -9 serum protein concentrations through enzyme-linked immunosorbent assay. The survivin-WT, -2B, -ΔΕx3 mRNA, survivin protein, and caspases showed an escalated increase in SIRS and sepsis, whereas survivin-3B was repressed in sepsis (p < 0.05). Survivin correlated with IL-8 and caspase-9 (p < 0.01). For discriminating sepsis, caspase-9 achieved the best receiver operating characteristic curve (AUROC) of 0.95. In predicting mortality, caspase-9 and survivin protein achieved an AUROC of 0.70. In conclusion, specific apoptotic and antiapoptotic pathways might represent attractive targets for future research in sepsis.