Validation and improvement of the predictive equation for resting energy expenditure in advanced gastrointestinal...

The usual predictive equations for estimating resting energy expenditure (REE) seem to be associated with significant inaccuracy in patients with advanced cancer. Recently, our group developed a predictive equation for patients with advanced head and neck cancer, showing a better accuracy when compared with indirect calorimetry. The aim of this study was to validate this predictive equation and, if necessary, to elaborate a new predictive equation for patients with advanced gastrointestinal (GI) cancer.

This was a retrospective, unicentric observational study. Data regarding the characteristics of the study were collected using an electronic medical record from June 2016 to January 2018. The nutritional status was calculated by the body mass index (BMI). Patients with nutritional risk, by the Nutritional Risk Screening 2002, were subjectively evaluated in relation to the nutritional status by the Patient-Generated Subjective Global Assessment (PG-SGA). Sarcopenia was defined as fat-free mass index ≤17.4 kg/m² for men and ≤15 kg/m² for women. Body composition and phase angle values were evaluated by electrical bioimpedance. REE was measured by indirect calorimetry.

The study included 109 patients with advanced GI tract cancer. Most were male (72.5%), ≥60 y of age (61.5%), and had cancer in the esophagus region (62.4%). Most patients had not undergone any treatment at the time of the examination. Regarding nutritional characteristics, the majority of the patients were malnourished by BMI (71.6%), with a deficit of lean mass (79.8%), sarcopenia (83.5%), and a phase angle below the fifth percentile for age, sex, and BMI, showing in addition to a poor nutritional condition, an impaired cellular integrity. Most of the patients were hypermetabolic (56.9%) and their caloric intake in the preceding 3 d was insufficient in 43.1%. Through the intraclass correlation coefficient (ICC), it was possible to observe the satisfactory agreement between the REE measured by the gold standard (calorimetry) versus the Souza-Singer's formula (ICC, 0.730; 95% confidence interval, 0.659-0.789; P < 0.001). When we did the multiple linear regression model, we figured that in this group of patients with GI cancer, only lean mass, phase angle, and sex were the age-adjusted independent variables that influenced REE, which was different from the Souza-Singer formula. This way a new prediction formula for this population has been created and needs to be validated.

A new equation considering phase angle and body composition can improve the accuracy of the predictive equation.