Abstract
Critically ill patients have higher energy expenditure and increased nutritional needs compared to non-critically patients. This can lead to medical malnutrition if nutritional interventions are not implemented quickly and accurately in the disease course. Obtaining an accurate estimation of nutritional needs is paramount in establishing nutritional support. The literature review was designed to look at the numerous predictive equations and determine the accuracy among different critically ill patient populations. Data Sources: Using keywords energy estimation, nutritional predictive equations, and critical illness, systematic reviews, meta-analyses, and validation studies were identified through electronic database searches and citation tracking. Study Selection: Articles were selected that evaluated individual predictive equations in critically ill patients, their accuracy compared to indirect calorimetry, and their use in special patient populations. Data Extraction and Synthesis: A total of 12 articles were selected using the inclusion and exclusion criteria. Accuracy of the predictive equations was separated into overall accuracy among all patients, all ages, non-obese, young obese and non-obese, and elderly obese and non-obese. Conclusions: There are several validated predictive equations for estimating energy expenditure in critical illness. The most accurate equations are the 2003 Penn State equation, for obese and non-obese adult patients, and the 2010 Penn State equation, for elderly obese patients. Given the trend of obesity in the United States, further validated studies are needed to look at the individual classes of obesity and the accuracy with this specific patient population.
Highlights
; Malnutrition; Energy Estimation; Predictive Equations demands and places the body in a hypermetabolic state.If this new energy requirement is not met or if adequate nutritional reserves are absent, lean body mass is lost very quickly in order to support the patient through the illness.This loss of lean body mass in critically ill patients has been shown to reduce the overall chance of survival during the course of the illness.2Failing to provide adequate nutrition during critical illness increases complications while in the hospital and includes increased risk for all infections, increased hospital length of stay, and increased risk for organ failure.1Overfeeding a critical care patient can be just as detrimental and complications can include hyperglycemia, azotemia, and hypercapnia.[3]
There are several modalities available to measure energy expenditure, but, not all are applicable to medical patients and even fewer still can be used in patients with critical illnesses
This study found that the Faisy equation was inaccurate in the older population and tended to overestimate energy needs, but was accurate and unbiased in younger patients.15Frankenfield found an overall accuracy of 53% among all patients, but only 37% with elderly non-obese patients and 39% with elderly obese patients.15This is the first equation that has been studied that excluded surgery and trauma patients and focused primarily on medical patients
Summary
; Malnutrition; Energy Estimation; Predictive Equations demands and places the body in a hypermetabolic state.If this new energy requirement is not met or if adequate nutritional reserves are absent, lean body mass is lost very quickly in order to support the patient through the illness.This loss of lean body mass in critically ill patients has been shown to reduce the overall chance of survival during the course of the illness.2Failing to provide adequate nutrition during critical illness increases complications while in the hospital and includes increased risk for all infections, increased hospital length of stay, and increased risk for organ failure.1Overfeeding a critical care patient can be just as detrimental and complications can include hyperglycemia, azotemia, and hypercapnia.[3]. There are several modalities available to measure energy expenditure, but, not all are applicable to medical patients and even fewer still can be used in patients with critical illnesses. The most readily available means to estimate energy expenditure in the hospital setting has been with predictive equations. These formulas use measureable data, which range for simple anthropometric measurements to minute ventilation, to calculate an estimate of energy expenditure for an individual. Several of the current predictive equations available for critical care patients are the Ireton-Jones, Penn State, Swinamer, Brandi, and Faisy equations
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