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Individualized Prediction of Acute Pancreatitis Recurrence Using a Nomogram

Supplemental digital content is available in the text. Objectives The objective of this study was to develop and validate a model, based on the blood biochemical (BBC) indexes, to predict the recurrence of acute pancreatitis patients. Methods We retrospectively enrolled 923 acute pancreatitis patients (586 in the primary cohort and 337 in the validation cohort) from January 2014 to December 2016. Aiming for an extreme imbalance between recurrent acute pancreatitis (RAP) and non-RAP patients (about 1:4), we designed BBC index selection using least absolute shrinkage and selection operator regression, along with an ensemble-learning strategy to obtain a BBC signature. Multivariable logistic regression was used to build the RAP predictive model. Results The BBC signature, consisting of 35 selected BBC indexes, was significantly higher in patients with RAP (P < 0.001). The area under the curve of the receiver operating characteristic curve of BBC signature model was 0.6534 in the primary cohort and 0.7173 in the validation cohort. The RAP predictive nomogram incorporating the BBC signature, age, hypertension, and diabetes showed better discrimination, with an area under the curve of 0.6538 in the primary cohort and 0.7212 in the validation cohort. Conclusions Our study developed a RAP predictive nomogram with good performance, which could be conveniently and efficiently used to optimize individualized prediction of RAP.