Background: Disease-related malnutrition is highly prevalent among hospitalized patients and is associated with increased mortality, complications, and prolonged hospital stays. Early identification of patients at nutritional risk is therefore essential to improve clinical outcomes. The Controlling Nutritional Status (CONUT) score is an objective prognostic immunometabolic marker derived from serum albumin, total cholesterol, and lymphocyte count. This study aimed to evaluate the prognostic value of the CONUT score for in-hospital mortality and length of hospital stay (LOS) in hospitalized patients with moderate-to-severe nutritional risk and to determine whether incorporating CONUT improves the predictive performance of a clinical model based on routine admission variables. Methods: A retrospective observational cohort study was conducted, including 671 adult patients admitted to a tertiary university hospital with CONUT ≥ 6. Multivariable logistic regression was used to assess predictors of in-hospital mortality, while LOS was analyzed using multivariable linear regression. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis and comparison of the area under the curve (AUC). Results: Higher CONUT scores were independently associated with increased in-hospital mortality. Each one-point increase in CONUT was associated with 28% higher odds of death (OR 1.28; 95% CI 1.13–1.46; p < 0.001). Patients with a severe CONUT score had significantly higher mortality compared with those with a moderate CONUT score (OR 1.77; 95% CI 1.12–2.81; p = 0.004). Incorporating CONUT into the clinical prediction model significantly improved discrimination, increasing the AUC from 0.728 to 0.753 (DeLong p = 0.035). Higher CONUT values were also associated with longer hospital stays: each additional point corresponded to a 5.4% increase in LOS (p = 0.009), and a severe CONUT score was associated with a 17.6% longer stay (p = 0.027). Conclusions: the CONUT score is independently associated with in-hospital mortality and prolonged hospitalization. While its incremental discriminative improvement is modest, its automated calculation from routine laboratory data makes it a practical and scalable tool for early risk stratification.
Romero-Márquez et al. (Wed,) studied this question.