Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity

Area of Science:

• Geriatric Medicine
• Nutritional Science
• Biostatistics

Background:

• Accurate resting energy expenditure (REE) estimation is vital for nutritional management in older Asian adults with multiple chronic conditions.
• Standard predictive equations (PEs) often lack precision for this specific demographic.
• Existing models demonstrate significant proportional bias, particularly in underweight individuals.

Purpose of the Study:

• To recalibrate existing predictive equations (PEs) for resting energy expenditure (REE) using BMI-stratified, slope-only regression.
• To enhance the accuracy of bedside REE estimation for older Asian adults with multimorbidity.
• To develop a more precise tool for nutritional management in geriatric patients.

Main Methods:

• Indirect calorimetry was used to measure REE in 400 hospitalized patients aged 65 and older.
• Sensitivity analyses were performed to identify bias in standard PEs.
• Models were recalibrated and validated using 1000-iteration bootstrap resampling.

Main Results:

• Standard PEs showed significant bias, overpredicting energy needs for 68% of underweight patients.
• The new Singapore Older Adults Resting energy expenditure (SOAR) PE 1 demonstrated the elimination of weight-dependent systematic errors.
• Recalibrated models achieved up to 64% accuracy in obese cohorts, comparable to complex adjusted-weight protocols.

Conclusions:

• Population-specific recalibration of REE predictive equations is essential for geriatric rehabilitation.
• This approach mitigates risks of malnutrition and overfeeding in older adults.
• BMI-stratified multipliers offer a clinically efficient framework for individualized nutritional care.