Abstract
Abstract
Background
This study aimed to construct and assess a machine learning–random survival forest prediction model for predicting short-term recurrence after first-episode acute pancreatitis (AP).
Methods
A total of 568 patients with AP admitted to Cangzhou Central Hospital from October 2018 to December 2025 were retrospectively analyzed and divided into the recurrent AP group (RAP) and non-RAP group according to whether recurrence occurred within 1 year. In the machine learning section, the patients were randomly divided into a training cohort (
n
= 398) and an internal validation cohort (
n
= 170) at a 7:3 ratio. An external validation cohort of 77 patients from another hospital was used for model evaluation. Cox regression analysis was applied to screen the predictive factors of recurrence, and a random survival forest model was constructed. The performance of the random survival forest prediction model was assessed using the receiver operating characteristic curve, calibration curve, decision curve analysis (DCA), and Shapley Additive exPlanations (SHAP).
Results
Approximately 14.1% of the first-episode AP cases (80/568) recurred. Cox analysis identified alcohol drinking (HR = 2.35, 95% CI = 1.37–4.05,
P
= 0.002), triglyceride (HR = 1.13, 95% CI = 1.10–1.16,
P
P
Conclusion
Random survival forest models are insightful and useful tools for predicting short-term recurrence for patients with first-episode AP.
Clinical trail
Not applicable.