Development and validation of a predictive model for acute kidney injury in patients with moderately severe and severe acute pancreatitis.

Background: Acute kidney injury is a serious complication of moderately severe and severe acute pancreatitis, which significantly increases mortality. There are currently no reliable tools for early identification of AKI especially severe AKI in these patients. We aim to develop a predictive model so that physicians can assess the risk of AKI and severe AKI, thus take further preventive measures.

Methods: Patients with a diagnosis of MSAP and SAP admitted to our hospital from January 2018 to December 2021 were retrospectively included in the study. The participants were divided into the training and validation cohorts randomly, in a 2:1 ratio. A clinical signature was built based on reproducible features, using the least absolute shrinkage and selection operator method and machine learning. Multivariate logistic regression analysis was used to develop the prediction model. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness.

Results: A total of 996 eligible patients were enrolled. 698 patients were allocated in the training cohort and 298 in the validation cohort. AKI occurred in 148 patients (21%) in the training cohort and 54 (18%) in the validation cohort, respectively. The clinical features, including C-reactive protein, intra-abdominal pressure and serum cysC, were significantly associated with AKI as well as severe AKI. The nomogram showed favorable discrimination, calibration and clinical usefulness.

Conclusions: The novel risk score model has good performance for predicting AKI and severe AKI in MSAP and SAP patients. Application of this model can help clinicians stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and prognosis.