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Energy balance in obese, mechanically ventilated intensive care unit patients.

ABSTRACT: OBJECTIVES:The aims of this study were, first, to compare the predicted (calculated) energy requirements based on standard equations with target energy requirement based on indirect calorimetry (IC) in critically ill, obese mechanically ventilated patients; and second, to compare actual energy intake to target energy requirements. METHODS:We conducted a prospective cohort study of mechanically ventilated critically ill patients with body mass index ?30.0 kg/m2 for whom enteral feeding was planned. Clinical and demographic data were prospectively collected. Resting energy expenditure was measured by open-circuit IC. American Society of Parenteral and Enteral Nutrition (APSPEN)/Society of Critical Care Medicine (SCCM) 2016 equations were used to determine predicted (calculated) energy requirements. Target energy requirements were set at 65% to 70% of measured resting energy expenditure as recommended by ASPEN/SCCM. Nitrogen balance was determined via simultaneous measurement of 24-h urinary nitrogen concentration and protein intake. RESULTS:Twenty-five patients (mean age: 64.5 ± 11.8 y, mean body mass index: 35.2 ± 3.6 kg/m2) underwent IC. The mean predicted energy requirement was 1227 kcal/d compared with mean measured target energy requirement of 1691 kcal/d. Predicted (calculated) energy requirements derived from ASPEN/SCCM equations were less than the target energy requirements in most cases. Actual energy intake from enteral nutrition met 57% of target energy requirements. Protein intake met 25% of target protein requirement and the mean nitrogen balance was -2.3 ± 5.1 g/d. CONCLUSIONS:Predictive equations underestimated target energy needs in this population. Further, we found that feeding to goal was often delayed resulting in failure to meet both protein and energy intake goals.

SUBMITTER: Vest MT

PROVIDER: S-EPMC6702036 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Energy balance in obese, mechanically ventilated intensive care unit patients.

Vest Michael T MT Newell Emma E Shapero Mary M McGraw Patricia P Jurkovitz Claudine C Lennon Shannon L SL Trabulsi Jillian J

Nutrition (Burbank, Los Angeles County, Calif.) 20190426

<h4>Objectives</h4>The aims of this study were, first, to compare the predicted (calculated) energy requirements based on standard equations with target energy requirement based on indirect calorimetry (IC) in critically ill, obese mechanically ventilated patients; and second, to compare actual energy intake to target energy requirements.<h4>Methods</h4>We conducted a prospective cohort study of mechanically ventilated critically ill patients with body mass index ≥30.0 kg/m<sup>2</sup> for whom ...[more]

PMID: 31207439

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Project description:BackgroundMechanical ventilation services are an important driver of the high costs of intensive care. An optimal interaction between a patient and a ventilator is therefore paramount. Suboptimal interaction is present when patients repeatedly demand, but do not receive, breathing support from a mechanical ventilator (> 30 times in 3 min), also known as an ineffective effort event (IEEV). IEEVs are associated with increased hospital mortality prolonged intensive care stay, and prolonged time on ventilation and thus development of real-time analytics that identify IEEVs is essential. To assist decision-making about further development we estimate the potential cost-effectiveness of real-time analytics that identify ineffective effort events.MethodsWe developed a cost-effectiveness model combining a decision tree and Markov model for long-term outcomes with data on current care from a Greek hospital and literature. A lifetime horizon and a healthcare payer perspective were used. Uncertainty about the results was assessed using sensitivity and scenario analyses to examine the impact of varying parameters like the intensive care costs per day and the effectiveness of treatment of IEEVs.ResultsUse of the analytics could lead to reduced mortality (3% absolute reduction), increased quality adjusted life years (0.21 per patient) and cost-savings (€264 per patient) compared to current care. Moreover, cost-savings for hospitals and health improvements can be incurred even if the treatment's effectiveness is reduced from 30 to 10%. The estimated savings increase to €1,155 per patient in countries where costs of an intensive care day are high (e.g. the Netherlands). There is considerable headroom for development and the analytics generate savings when the price of the analytics per bed per year is below €7,307. Furthermore, even when the treatment's effectiveness is 10%, the probability that the analytics are cost-effective exceeds 90%.ConclusionsImplementing real-time analytics to identify ineffective effort events can lead to health and financial benefits. Therefore, it will be worthwhile to continue assessment of the effectiveness of the analytics in clinical practice and validate our findings. Eventually, their adoption in settings where costs of an intensive care day are high and ineffective efforts are frequent could yield a high return on investment.

Dataset Information

Energy balance in obese, mechanically ventilated intensive care unit patients.

Publications

Energy balance in obese, mechanically ventilated intensive care unit patients.

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