# Development of New Equation for Predicting State of Normometabolism from Cohort of Hospitalized Patients with Obesity

## TL;DR

This study created a new equation to better predict resting energy expenditure in hospitalized patients with obesity, improving accuracy over existing methods.

## Contribution

A novel predictive equation for normometabolism in obesity patients with improved accuracy and narrower prediction limits.

## Key findings

- The new equation achieved high accuracy with an R2 of 0.923 and a root mean square error of 81.872 kcal/day.
- The equation had a mean bias of −0.054 kcal/day and narrower limits of agreement compared to existing models.

## Abstract

Background/Objectives: Existing resting energy expenditure (REE) predictive equations, including Mifflin-St Jeor and Harris–Benedict, show limited accuracy, particularly in patients with a BMI over 35, often leading to overestimation or underestimation of REE. This study aimed to develop a new predictive equation specifically designed to identify normometabolic status in patients with obesity, enabling more precise qualitative assessments of basal metabolism through indirect calorimetry. Methods: A cohort of 89 hospitalized patients with obesity (BMI > 30) underwent REE measurement and comprehensive anthropometric assessments. Patients were classified as normometabolic if their REE was within ±10% of the Mifflin-St Jeor prediction or if their fat-free mass-specific REE fell between 23 and 30 kcal/kg. Results: The newly developed equation demonstrated high predictive accuracy (R2 = 0.923, root mean square error = 81.872 kcal/day), with a mean bias of −0.054 kcal/day and narrower limits of agreement (−156.834 to 156.725 kcal/day) compared to widely used models. Conclusions: These advancements could enhance follow-up and management of diet therapy in patients with obesity, allowing for a more tailored approach to their metabolic health over time.