Journal of clinical gastroenterology · 2026
Prediction of Early Clinical Deterioration in Acute Pancreatitis: A Machine Learning-based Approach.
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Korkmaz İbrahim, Eren Korkmaz Özge
Аннотация
INTRODUCTION: Early identification of clinical deterioration in acute pancreatitis patients is critical for improving outcomes in emergency settings. This study aimed to develop a machine learning model using only admission data to predict intensive care unit (ICU) needs within the first 72 hours and to compare its diagnostic performance with conventional scoring systems.
METHODS: This retrospective, single-center study included 448 patients with acute pancreatitis admitted to the emergency department. The dataset was randomly split into 70% training and 30% test sets. After SMOTE-based class balancing and 5-fold cross-validation, a Random Forest model was developed using 35 routinely collected variables. Model performance was evaluated using AUC, sensitivity, specificity, F1 score, and calibration. . The DeLong test compared diagnostic performance with CTSI, HAPS, Ranson, and Glasgow-Imrie scores.
RESULTS: The Random Forest model achieved an AUC of 0.974 (95% CI: 0.940-1.000), sensitivity of 92.9%, and specificity of 94.2% in the test set. It significantly outperformed all conventional scoring systems (P<0.05). Permutation-based feature importance analysis revealed calcium, Delta Neutrophil Index, urea, glucose, and acute peripancreatic fluid collection as the most influential variables.
CONCLUSIONS: Based solely on emergency admission data, our model demonstrated superior diagnostic accuracy compared with established scores and may serve as a practical early warning tool. Prospective multicenter validation is recommended.
METHODS: This retrospective, single-center study included 448 patients with acute pancreatitis admitted to the emergency department. The dataset was randomly split into 70% training and 30% test sets. After SMOTE-based class balancing and 5-fold cross-validation, a Random Forest model was developed using 35 routinely collected variables. Model performance was evaluated using AUC, sensitivity, specificity, F1 score, and calibration. . The DeLong test compared diagnostic performance with CTSI, HAPS, Ranson, and Glasgow-Imrie scores.
RESULTS: The Random Forest model achieved an AUC of 0.974 (95% CI: 0.940-1.000), sensitivity of 92.9%, and specificity of 94.2% in the test set. It significantly outperformed all conventional scoring systems (P<0.05). Permutation-based feature importance analysis revealed calcium, Delta Neutrophil Index, urea, glucose, and acute peripancreatic fluid collection as the most influential variables.
CONCLUSIONS: Based solely on emergency admission data, our model demonstrated superior diagnostic accuracy compared with established scores and may serve as a practical early warning tool. Prospective multicenter validation is recommended.