Published online Apr 21, 2022. doi: 10.3748/wjg.v28.i15.1588
Peer-review started: November 20, 2021
First decision: January 11, 2022
Revised: February 2, 2022
Accepted: March 6, 2022
Article in press: March 6, 2022
Published online: April 21, 2022
The severity of acute pancreatitis in pregnancy is correlated with higher risks of maternal and fetal death.
There is a lack of a scoring model for predicting the moderately severe and severe acute pancreatitis in pregnancy (MSIP).
We aimed to develop a prediction model for moderately severe and severe acute pancreatitis in pregnancy.
The training set and test set were randomly divided at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used to select potential prognostic factors. A nomogram was developed by logistic regression. A random forest model was used to validate the stability of the of prediction factors. Receiver operating characteristic curves and calibration curves were used to evaluate the model’s predictive performance.
A total of 190 patients were included in this study. Four predictors including lactate dehydrogenase, triglyceride, cholesterol, and albumin levels constitute the prediction model. The model had areas under the curve of 0.865 and 0.853 in the training and validation sets, respectively. The calibration curves showed that the prediction model has a good consistency.
An effective prediction model that can predict MSIP was constructed.
Our model could help to predict moderately severe and severe acute pancreatitis in pregnancy. Usability of the model needs validation by other center data.