Objective To construct a predictive model for acute pancreatitis (AP) secondary to persistent organ failure (POF) based on clinical characteristics and serological indicators using machine learning.
Methods A total of 350 AP patients from October 2019 to December 2024 were retrospectively collected and divided into a training set (n=245) and an internal validation set (n=105) in a 7∶3 ratio. Another 82 patients from Xincheng Hospital during the same period were included as the test set. Quantitative data with normal distribution were described as (
±
s) and analyzed using independent sample
t tests; count data were described as [cases (%)] and analyzed using the
χ2 test. Variables were selected using LASSO regression and models of logistic regression (LR), random forest (RF), and support vector machine (SVM) were constructed to optimize the model using the area under the receiver operating characteristic curve (AUC). The generalization ability of the models was verified using the test set.
Results The AP secondary POF-related variables selected by LASSO regression included body mass index, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, random blood glucose, interleukin 6 (IL-6), C-reactive protein (CRP), lactate dehydrogenase (LDH), and neutrophil count/lymphocyte count (NLR). The AUC of the LR, RF, and SVM models based on clinical characteristics were 0.700, 0.862, and 0.707, respectively; the AUC of the LR, RF, and SVM models based on clinical characteristics and serological indicators were 0.805, 0.904, and 0.840, respectively. The AUC of the serological indicators-based models in the three machine learning models were all better than those of the clinical characteristics alone (P < 0.05). The AUC of the RF model based on clinical characteristics and serological indicators in the internal validation set for predicting AP secondary POF was 0.899. The ROC curve was plotted using the test set data, and the results showed that the AUC value of the RF model based on clinical characteristics and serological indicators for predicting AP secondary POF was 0.896, with a sensitivity of 80.7%, specificity of 87.8%, accuracy of 85.7%, precision of 73.5%, and F1 of 0.831.
Conclusion The RF model based on the combination of clinical characteristics (body mass index, APACHE II score, random blood glucose) and serological indicators has excellent performance and reliable generalization ability for predicting AP secondary POF, and can be used for early identification of high-risk patients and risk stratification.