Novel Resting Energy Expenditure Prediction Equations for Multi-Ethnic Asian Older Adults with Multimorbidity

Malnutrition is prevalent among hospitalized older adults with multimorbidity, leading to adverse health outcomes and increased healthcare costs. An accurate assessment of resting energy expenditure (REE) is crucial because an inaccurate estimation of energy needs may result in unintentional underfeeding or overfeeding, both of which can worsen nutritional status and clinical outcomes. While indirect calorimetry (IC) is the preferred method, its clinical applicability is limited. Prediction equations are commonly used, but their accuracy in older Asian patients with multimorbidity remains unclear.

This multicenter, cross-sectional study enrolled 400 patients aged ≥65 years from March to December 2023 in Outram Community Hospital (OCH) and Sengkang Community Hospital (SKCH). Participants' REE was measured using indirect calorimetry. We compared the performance of the newly developed novel prediction equations (PEs), derived from readily accessible or easily measured anthropometric data, against established equations. Statistical analysis included the calculation of R², the root mean square error (RMSE), and the intraclass correlation coefficient (ICC) to assess reliability and goodness of fit.

A high prevalence (85%) of multimorbidity was observed among the participants. REE increased progressively with body mass index (BMI) across all groups (865.6-1269.4 kcal in females; 889.1-1269.4 kcal in males). The novel PEs (RMSE: 186-191; ICC: 0.5-0.52) demonstrated improved accuracy and stronger reliability compared to conventional equations (RMSE: 222-258; ICC: 0.271-0.460).

Our newly developed PEs offer potentially valuable tools for precise REE estimation in hospitalized older Asian patients with multimorbidity. Further external validation and investigation in diverse populations are necessary to confirm these results.