Machine learning models for predicting new-onset diabetes following acute pancreatitis using real-world data.

Authors

Djibril M Ba, Alireza Vafaei Sadr, Yue Zhang, Phil A Hart, Nazia Raja-Khan, Ruizhe Zhou, Ayesha Siddiqui, Tian Qiu, Jennifer Maranki, Vernon M Chinchilli, Vida Abedi

Published in

BMJ public health. Volume 4. Issue 3. Pages e004661. Epub Aug 25, 2026.

Abstract

About one-quarter of patients with acute pancreatitis (AP) will develop diabetes mellitus (DM) within 3 years, but risk factors remain unclear. This study aims to determine whether machine learning models (ML) can be trained to accurately predict new-onset DM following AP and identify key clinical features using real-world data.
This retrospective cohort study used de-identified data from the TriNetX federated electronic health records (EHR) network from 1 January 2017 to 11 March 2024. A total of 58 746 patients with AP (International Classification of Diseases-10 code K85) and no prior diagnosis of DM were included. New-onset DM following AP was the main outcome of interest. Five ML models were trained across four prediction windows, including logistic regression (LR), eXtreme Gradient Boosting, Adaptive Boosting, Random Forest and support vector machine. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC).
Among the 58 746 patients with AP (mean (SD) age, 50.2 (16.6) years), the LR model demonstrated the highest overall performance, with a mean accuracy of 0.72 (SD, 0.009) and an AUROC of 0.79 (SD, 0.009). Key clinical features across models included age, pancreatic necrosis, body weight, body mass index, systolic blood pressure, number of medical visits and prior AP laboratory values such as glucose, anion gap, blood urea nitrogen (BUN) and total protein.
In this first real-world evidence study using EHR data, we have developed and demonstrated the feasibility of using ML to predict the new onset of DM after AP. Clinical features such as age, pancreatic necrosis, prior glucose, BUN and anion gap had the highest overall importance scores and may inform tailored prevention strategies.

PMID:
42688606
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

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