# Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

## TL;DR

This study introduces a metabolic score tool that uses body composition metrics to predict the severity of acute pancreatitis in patients.

## Contribution

A novel metabolic score (SMS) is developed using skeletal muscle index changes and radiodensity to predict acute pancreatitis severity.

## Key findings

- ΔSMI and PreSMR were independent risk factors for AP severity in multivariate analysis.
- The metabolic score (SMS) showed good predictive accuracy with AUCs of 0.764 and 0.741 in two hospital cohorts.
- Patients with higher ΔSMI and PreSMR had better outcomes, suggesting a link between body composition and AP severity.

## Abstract

Increasing evidence suggests that body composition is associated with the development of acute pancreatitis (AP). This study aimed to investigate the applicability of body composition in predicting AP severity. Data of 213 patients with AP from the Affiliated Hospital of Putian University (AHOPTU) were included in this study, whilst data of 173 patients with AP from Fujian Medical University Union Hospital (FMUUH) were used for external validation. Patients were classified into the non-severe and severe groups according to AP severity. After seven days of treatment, in patients from AHOPTU, the difference in skeletal muscle index before and after treatment (ΔSMI) was significantly higher (P ═ 0.002), while the skeletal muscle radiodensity before treatment (PreSMR) was significantly lower (P ═ 0.042) in the non-severe group than in the severe group. The multivariate logistic regression model also revealed that the ΔSMI and PreSMR were independent risk factors for AP severity. The optimal cut-off values of ΔSMI and PreSMR were 1.0 and 43.7, respectively. The following metabolic score (SMS) was established to predict AP severity: 0: ΔSMI < 1.0 and PreSMR < 43.7; 1: ΔSMI ≥ 1.0 and PreSMR < 43.7 or ΔSMI < 1.0 and PreSMR ≥ 43.7; 3: ΔSMI ≥ 1.0 and PreSMR ≥ 43.7. In patients from AHOPTU and FMUUH, the areas under the curves for this model were 0.764 and 0.741, respectively. ΔSMI and PreSMR can accurately predict AP severity. It is recommended to routinely evaluate the statuses of patients with AP using the predictive model presented in this study for individualized treatment.