ArticleThe Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology2026
Correlation Analysis of Body Muscle-Fat Composition Parameters and CT Severity Index with Disease Severity in Acute Pancreatitis.
Article in The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background/Aims: To investigate the correlation between body muscle-fat composition parameters (visceral adipose tissue (VAT) area, total adipose tissue (TAT) area, skeletal muscle index (SMI)) and the modified computed tomography severity index (MCTSI) with acute pancreatitis (AP) severity and their predictive value for adverse outcomes. Materials and Methods: Two hundred thirty-six AP patients were enrolled. Patients were stratified by severity into 84 mild AP (MAP), 90 moderately severe AP (MSAP), and 62 severe AP (SAP), and were further grouped by 30-day survival (172 survivors, 64 non-survivors). Demographic data, laboratory parameters, and abdominal computed tomography (CT) data were collected. Intergroup differences were analyzed, predictive performance was assessed, and independent risk factors were identified. Results: The scores of VAT, TAT, VAT/TAT, VAT/SAT, and MCTSI progressively increased with the severity of AP (from MAP to MSAP and SAP). No significant difference was observed in SAT among the groups. Non-survivors had higher VAT, SAT, TAT, and MCTSI scores but lower SMI than survivors (all P < .05). Receiver-operating characteristic (ROC) analysis demonstrated prognostic value for VAT (area under the curve (AUC) = 0.823), TAT (AUC = 0.789), SMI (AUC = 0.778), and MCTSI (AUC = 0.819), with combined assessment achieving an AUC of 0.960. Multivariate analysis identified APACHE II score, HDL, VAT, TAT, SMI, and MCTSI as independent predictors (P < .05). Conclusion: Body muscle-fat composition parameters and CT severity index correlate strongly with AP severity and independently predict adverse outcomes. Combined use enhances predictive accuracy, offering valuable insights for risk stratification and individualized treatment.
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