Unsupervised machine learning model for analyzing nutritional support in mechanically ventilated patients (Revista Contexto & Saúde, 2026)
Nutritional support is essential for critically ill patients on mechanical ventilation, helping to prevent malnutrition, optimize clinical outcomes, and reduce mortality. However, there is still no consensus on the ideal timing for achieving caloric and protein goals, highlighting the need for further studies to guide evidence-based practices. This study aimed to model, in an unsupervised manner, nutritional intervention in critically ill patients on mechanical ventilation admitted to an Intensive Care Unit (ICU). A retrospective study was conducted using data from 260 patients treated in the ICU of a tertiary hospital in Maringá, Paraná, Brazil, through multiple correspondence analysis (MCA) and qualitative comparative analysis (QCA). The MCA explained approximately 21.8% of the variation in mortality outcomes, where being male, having no clinical complications or associated comorbidities, using vasopressors, initiating early nutritional therapy within 24 to 48 hours, having a body mass index between 25 and 29.9 kg/m², and achieving caloric and protein goals within five days were more prominently associated with mortality. QCA, in turn, demonstrated that, on average, 23% of deaths could be explained by the combination of dependent variables analyzed in this study, primarily body mass index, timing of enteral therapy initiation, caloric and protein goals, and vasopressor use. These findings suggest that, in the studied population, the combination of adequate nutritional supply and timely management was associated with better clinical outcomes, underscoring the need for care protocols that prioritize early nutritional assessment and intervention in critically ill patients on mechanical ventilation and enteral support.
Keywords: Nutritional therapy; Mechanical ventilation; Critical care