To establish a model for predicting the recurrence of acute pancreatitis (AP) based on the model of CT plain scan image and systemic immune inflammatory index (SII), and to verify its predictive efficiency.

A total of 145 patients with AP admitted to Funan County Hospital of Traditional Chinese Medicine from January 2020 to December 2022 were selected and included in the training set (n=102) and the verification set (n=43) according to the ratio of 7∶3. The recurrence of AP during patients' follow-up was recorded, and the CT plain scan performance and SII of patients with and without AP recurrence during the follow-up of the training set were compared. The features of CT plain scan images were extracted and screened using variance threshold method, univariate selection method, minimum absolute contraction and selection operator, and an image omics model was established using random forest classification method. The receiver operating characteristic curve (ROC) was drawn for predicting AP recurrence in patients using CT plain scan imaging omics models combined with SII prediction training and validation sets, and the area under the curve (AUC) and prediction efficiency were calculated.

Among the 102 patients in the training set, 29 patients recurred during the follow-up period, with a recurrence rate of 28.43%. Among the 43 patients in the verification set, 13 cases recurred, with a recurrence rate of 30.23%. There was no statistically significant difference in AP recurrence rate between the training group and the verification group (P=0.827). There were statistically significant differences in AP etiology, CT plain scan imaging omics features and SII between recurrent and non-recurrent patients in the training set (P<0.05). The CT plain scan imaging model included five features, namely fatty liver, pleural effusion, suprahepatic space effusion, adrenal gland invasion and gastric bare area invasion. The best cutoff value of SII calculated by Jordan index was 1 109.59×10⁹/L. The AUC of CT imaging characteristics combined with SII in predicting the recurrence of AP in the training set and the verification set were 0.892 and 0.837, respectively, with sensitivity of 79.31% and 76.92% and specificity of 93.15% and 86.67%, respectively.

The recurrence rate of AP patients is high, and it is related to the etiology, CT imaging features and SII. The combination of CT imaging features and SII can provide reliable reference for the prediction of AP recurrence.