Nutritional compliance as a prognostic and quality indicator in the intensive care unit: Insights from nutrition support team-guided therapy

초록

Objectives: Adequate nutritional support is essential in critical care, yet the gap between prescribed and delivered nutrition remains under-recognized. We evaluated whether "nutritional compliance"-the proportion of actual nutritional intake that aligns with clinical recommendations under Nutrition Support Team (NST) guidance- is associated with clinical outcomes in intensive care unit (ICU) patients. Methods: We retrospectively analyzed ICU patients (n = 79) admitted between April and November 2022 and managed under a standardized NST protocol. Nutritional compliance was defined as the proportion of administered to prescribed energy and protein intake, with >= 70% classified as good compliance and <70% as poor compliance. The primary outcome was in-hospital mortality. Secondary outcomes included changes in nutritional biomarkers, severity scores, and length of stay. Results: Among 79 patients, 42 (53.2%) achieved good compliance and 37 (46.8%) poor compliance. Good compliance was associated with shorter hospital stays (26.5 versus 38.4 days, P = 0.049) and improvements in APACHE II and NRS 2002 scores (both P < 0.001). Poor compliance independently predicted higher in-hospital mortality (adjusted OR 3.84, 95% CI 0.995-4.804, P = 0.041). Kaplan-Meier analysis demonstrated lower mortality in the good compliance group (log-rank P = 0.038). Adding compliance to baseline models improved predictive performance (AUC 0.82 versus 0.65). An inverse relationship was observed between compliance and mortality risk. Conclusions: Nutritional compliance was strongly associated with in-hospital mortality and may serve as a practical quality indicator for ICU nutrition care. These findings highlight the potential role of nutritional compliance as a pragmatic, measurable indicator of care quality in the ICU setting. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, Al training, and similar technologies.

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