Development and validation of a predictive equation for resting energy expenditure in Japanese patients with interstitial lung disease

IF 3.2 3区医学 Q2 NUTRITION & DIETETICS

Nutrition Pub Date : 2025-02-22 DOI:10.1016/j.nut.2025.112729

Keisuke Morikawa M.Sc. , Hiroyuki Takemura , Kana Kitayama , Shogo Inaba , Haruka Imaoka , Yu Hashitsume , Yuta Suzuki , Osamu Hataji M.D. , Kazuyuki Tabira Ph.D.

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引用次数: 0

Abstract

Background and Aims

This study developed a prediction equation for resting energy expenditure (REE) in patients with interstitial lung disease (ILD) using indirect calorimetry and examined the errors in the prediction equation.

Methods

This study consisted of two key phases: Study 1 focused on developing the prediction equation for REE, whereas Study 2 evaluated the accuracy of this equation through validation and error analysis. In Study 1, REE was measured, and a regression model equation was created to predict REE using multiple regression analysis, with measured REE (mREE) as the dependent variable. In Study 2, a Bland-Altman analysis was conducted to examine the phylogenetic error and agreement between predicted REE (pREE) calculated from the prediction equations developed in Study 1 and mREE.

Results

In Study 1, mREE was significantly associated with fat-free mass (FFM), and the prediction equation for REE was 456.988 + 22.539 × FFM. The addition error (0.4 ± 166.1, 95% confidence interval (CI): −55.8 to 56.6, P = 0.988) and proportional error (r = 0.223, P = 0.191) between mREE and pREE were not significantly different, with an agreement of 69.4%.

Conclusions

The mREE prediction equation developed in this study showed no systematic errors and exhibited higher agreement compared with existing prediction equations. The prediction equation for REE specific to patients with ILD obtained in this study has the potential for clinical application.

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日本间质性肺病患者静息能量消耗预测方程的开发与验证

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来源期刊

Nutrition 医学-营养学

CiteScore

7.80

自引率

2.30%

发文量

300

审稿时长

60 days

期刊介绍： Nutrition has an open access mirror journal Nutrition: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Founded by Michael M. Meguid in the early 1980''s, Nutrition presents advances in nutrition research and science, informs its readers on new and advancing technologies and data in clinical nutrition practice, encourages the application of outcomes research and meta-analyses to problems in patient-related nutrition; and seeks to help clarify and set the research, policy and practice agenda for nutrition science to enhance human well-being in the years ahead.