Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study

BACKGROUND: Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE: To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS: 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS: Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08–8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67–7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28–5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14–9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION: Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-022-02066-3.