Malnutrition-Related Liver Steatosis, CONUT Score and Poor Clinical Outcomes in an Internal Medicine Department.
Nicoletta MianoGiorgia TodaroMaurizio Di MarcoSabrina ScillettaGiosiana BoscoFrancesco Di Giacomo BarbagalloFrancesco PurrelloSalvatore PiroFrancesco PurrelloAntonino Di PinoPublished in: Nutrients (2024)
Fatty liver disease has been identified as a marker of malnutrition in different clinical settings. Recently, the COntrolling NUTritional status score (CONUT score) emerged as a promising tool for malnutrition assessment. Our aim was to evaluate short-term outcomes among patients with malnutrition-related liver steatosis in an Internal Medicine department. Furthermore, we evaluated the association of the CONUT score with malnutrition-related liver steatosis. Data from 247 patients hospitalized in an Internal Medicine department were retrospectively collected. The study population was stratified into three groups based on hepatic radiodensity assessed with computed tomography: mild steatosis (≥56.1 HU), moderate steatosis (between 49.7 and 56 HU), and severe steatosis (≤49.6 HU). We then calculated the CONUT score. Severe steatosis patients had higher in-hospital mortality (18.2 vs. 15.5%) and longer in-hospital stays compared with the mild steatosis group (length of in-hospital stay longer than 12 days: 45% vs. 40%). Logistic regression analysis showed that severe steatosis was not significantly associated with in-hospital all-cause death, while a high CONUT score was an independent risk factor for sepsis. We found an independent relationship between malnutrition-associated liver steatosis and the CONUT score. These results identified the CONUT score as a tool for nutritional assessment of hospitalized patients.
Keyphrases
- insulin resistance
- high fat diet
- high fat diet induced
- computed tomography
- end stage renal disease
- healthcare
- ejection fraction
- adipose tissue
- newly diagnosed
- skeletal muscle
- intensive care unit
- early onset
- type diabetes
- acute kidney injury
- drug induced
- machine learning
- electronic health record
- acute care
- adverse drug
- patient reported
- data analysis
- pet ct
- contrast enhanced
- dual energy