Drinking Patterns and CT / MRI Feature‐Based Nomogram Models Can More Accurately Predict Acute Alcoholic Pancreatitis Recurrence

BACKGROUND: To develop and test a nomogram model based on drinking patterns and computed tomography and magnetic resonance imaging (CT/MRI) characteristics for recurrence of acute alcoholic pancreatitis (AAP) following its first onset. METHODS: Patients with initial AAP at the Affiliated Hospital of North Sichuan Medical College were retrospectively enrolled and categorized them into recurrence and non-recurrence groups. They were randomly assigned in a 7:3 ratio to form training and independent test sets, and their clinical and imaging data were collected and analyzed. Nomogram models were established to predict the recurrence of AAP. RESULTS: Among 152 cases of initial AAP, 37 cases were recurrent and 115 were non-recurrent, with a mean (SD) age of 43.84 ± 10.28 years and 91.45% male participants. In the training set, 26 cases were recurrent and 80 were non-recurrent; in the independent test set, 11 cases were recurrent and 35 were non-recurrent. Multivariable logistic regression analysis showed that hyperlipidemia, pre-onset alcohol consumption, alcohol cessation, the Bedside Index for Severity in AP (BISAP) score, extrapancreatic inflammation on CT/MRI (EPIC/EPIM) score, and CT/MRI severity index (CTSI/MRSI) score were independent predictors of recurrence after initial AAP onset. Integrating these factors into a nomogram prediction model resulted in the area under the curve (AUC), sensitivity, and specificity values of 0.924 (95% CI 0.871-0.977), 0.885, and 0.838 for the training set and 0.843 (95% CI 0.714-0.972), 0.727, and 0.857 for the independent test set. The clinical model alone and the imaging model achieved AUCs of 0.799 (95% CI 0.703-0.896) and 0.827 (95% CI 0.742-0.912) in the training set and 0.800 (95% CI 0.646-0.954) and 0.686 (95% CI 0.492-0.880) in the independent test set, respectively. CONCLUSIONS: Nomogram models based on drinking patterns and CT/MRI characteristics can more accurately predict AAP recurrence. The model serves as an effective tool for clinical prediction of AAP and helps clinicians in developing personalized prevention and treatment strategies.