Nutrients, Vol. 18, Pages 1345: Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity
Nutrients doi: 10.3390/nu18091345
Authors:
Pei San Kua
Musfirah Albakri
Su Mei Tay
Phoebe Si-En Thong
Olivia Jiawen Xia
Wendelynn Hui Ping Chua
Kevin Chong
Nicholas Wei Kiat Tan
Xin Hui Loh
Jia Hui Tan
Lian Leng Low
Background/Objective: Accurate resting energy expenditure (REE) estimation is paramount for the nutritional management of older Asian adults with multimorbidity. However, standard predictive equations (PEs) lack precision for this cohort. This study aimed to recalibrate PEs using BMI-stratified, slope-only regression to enhance bedside accuracy. Methods: REE was measured via indirect calorimetry in 400 hospitalized patients (age ≥ 65). Sensitivity analyses identified significant proportional bias in existing models. Models were recalibrated and validated using 1000-iteration bootstrap resampling. Results: Standard PEs exhibited significant bias, particularly underpredicting requirements for 68% of underweight patients. The new Singapore Older Adults Resting energy expenditure (SOAR) PE 1 (963.67 + 8.56 × weight − 5.6 × age) eliminated weight-dependent systematic errors. The recalibrated models utilizing actual body weight achieved accuracy rates of up to 64% in obese cohorts, comparable to complex adjusted-weight protocols. Conclusions: Population-specific recalibration is essential to mitigate the bidirectional risks of malnutrition and overfeeding in geriatric rehabilitation. The BMI-stratified multipliers provided offer a robust, clinically efficient framework for individualized nutritional care.
Background/Objective: Accurate resting energy expenditure (REE) estimation is paramount for the nutritional management of older Asian adults with multimorbidity. However, standard predictive equations (PEs) lack precision for this cohort. This study aimed to recalibrate PEs using BMI-stratified, slope-only regression to enhance bedside accuracy. Methods: REE was measured via indirect calorimetry in 400 hospitalized patients (age ≥ 65). Sensitivity analyses identified significant proportional bias in existing models. Models were recalibrated and validated using 1000-iteration bootstrap resampling. Results: Standard PEs exhibited significant bias, particularly underpredicting requirements for 68% of underweight patients. The new Singapore Older Adults Resting energy expenditure (SOAR) PE 1 (963.67 + 8.56 × weight − 5.6 × age) eliminated weight-dependent systematic errors. The recalibrated models utilizing actual body weight achieved accuracy rates of up to 64% in obese cohorts, comparable to complex adjusted-weight protocols. Conclusions: Population-specific recalibration is essential to mitigate the bidirectional risks of malnutrition and overfeeding in geriatric rehabilitation. The BMI-stratified multipliers provided offer a robust, clinically efficient framework for individualized nutritional care. Read More