Introduction:
Post-acute pancreatitis diabetes mellitus (PPDM-A) is a non-negligible sequela of acute pancreatitis (AP), as it has a greater risk of mortality and development of pancreatic cancer compared to type 2 diabetes mellitus (T2DM). Early screening and diagnosis after the onset of pancreatitis are crucial for the outcome of patients. We aimed to establish a predictive nomogram for PPDM-A for early screening and identification.

Material and methods:
A total of 130 patients diagnosed with PPDM-A and 260 gender-matched non-diabetic AP (non-PPDM-A) patients were retrospectively included in this study. They were assigned to a training cohort and a validation cohort with a ratio of 7:3. General information and essential clinical indicators were collected. The Chinese visceral fat index (CVAI) was calculated. Multiple logistic regression was applied to analyze the risk factors of PPDM-A in the training cohort and a predictive model was built. This model was verified in a validation cohort.

Results:
CVAI, admission blood glucose value (GLU), blood amylase (AMY), recurrent acute pancreatitis (RAP), moderately severe acute pancreatitis (MSAP), and severe acute pancreatitis/critical acute pancreatitis (SAP/CAPA) are risk factors for PPDM-A. The area under the curve (AUC) of the prediction model was 0.917. When the cut-off value was 0.356, the sensitivity was 0.888, the specificity was 0.809, and the k was 0.679. The Hosmer-Lemeshow Hosmer test showed a good fit.

Conclusions:
CVAI, GLU, AMY, RAP, and severity of AP are risk factors for PPDM-A. The predictive nomogram established in this study can effectively predict the occurrence of PPDM-A.