BackgroundIndirect calorimetry (IC) is the gold standard for determining energy requirement. Due to lack of availability in many institutions, predictive equations are used to estimate energy requirements. The purpose of this study is to determine the accuracy of predictive equations (ie, Harris-Benedict equation (HBE), Mifflin-St Jeor equation (MSJ), and Penn State University equation (PSU)) used to determine energy needs for critically ill, ventilated patients compared with measured resting energy expenditure (mREE).MethodsThe researchers examined data routinely collected as part of clinical care for patients within intensive care units (ICUs). The final sample consisted of 68 patients. All studies were recorded during a single inpatient stay within an ICU.ResultsPatients, on average, had an mREE of 33.9 kcal/kg using IC. The estimated energy requirement when using predictive equations was 24.8 kcal/kg (HBE×1.25), 24.0 kcal/kg (MSJ×1.25), and 26.8 kcal/kg (PSU).DiscussionThis study identified significant differences between mREE and commonly used predictive equations in the ICU.Level of evidenceIII.

These results support the need for a technological platform that directly transmits EN pump volumes in real time to the EMR.

Background: Provision of enteral nutrition (EN) support is historically inadequate in the critically ill population. An interdisciplinary approach utilizing various strategies has been shown to improve initiation of timely EN support. The purpose of this study was to examine whether the implementation of a series of interventions led by an interdisciplinary team was associated with changes in the initiation of nutrition support in a level 1 trauma center. Methods: Patients admitted between 2009 and 2013 with isolated closed head trauma injuries were identified through the hospital’s trauma center database. The initial population consisted of 159 patients; after exclusion criteria, 141 patients were included in the statistical analyses. Two statistical analyses were conducted. The first calculated the average days to the initiation of nutrition start by admission year. The second estimated the association between admission year and time to nutrition initiation with a generalized linear model. Results: Time to initiate nutrition therapy was estimated to decrease by 1.46 days (47.31%) from 2009 to 2013. The time to initiate nutrition in 2013 was 1.63 days. A significant association was found between the time to initiate nutrition and the 2012 and 2013 binary variables while controlling for confounding variables. The time frame was estimated to be 1.09 (P = .008) and 1.75 (P = .000) days shorter in 2012 and 2013 relative to 2009. Conclusions: An interdisciplinary effort utilizing multiple strategies identified and addressed barriers, resulting in a reduction of variability and a proactive approach to early EN.