Statistical analysis
REGRESSION, ACUTE PANCREATITIS
Measurement data conforming to the normal distribution and homogeneity of variance were expressed as the mean ± standard deviation. Data between groups were compared using the independent t-test. The unequal variance Due to a large number of variables, the independent prognostic factors were filtered through two steps. First, a preliminary screening was conducted to identify possible predictors using the all-subsets regression method to avoid overfitting and increase the degree of the model. Subsequently, multivariate logistic regression was carried out. A nomogram was then constructed using all the independent prognostic factors.The nomogram was validated using multiple metrics. The discriminative ability of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). The obtained AUC values were compared with AUC values of the bedside index for the severity in acute pancreatitis (BISAP) scores, Ranson scores, and acute physiology and chronic health evaluation (APACHE) II scores. Further, a calibration curve was plotted to evaluate the calibrating ability of the nomogram. Moreover, Decision curve analysis (DCA) was used to assess the clinical significance of the nomogram. A 