Construction and evaluation of early prediction model for severe acute pancreatitis complicated with acute renal injury / 中华胰腺病杂志

ABSTRACT

Objective:To establish an early prediction Nomogram model for severe acute pancreatitis(SAP) complicated with acute renal injury (AKI), and evaluate the prediction efficiency of the model.Methods:The clinical data of 295 SAP patients hospitalized in Zhejiang Rongjun Hospital from July 2017 to June 2021 were retrospectively analyzed, and the patients were divided into AKI group ( n=61) and non-AKI group ( n=234) according to whether complicated with AKI. The common characters, clinical data and laboratory examination results were compared. The risk factors for SAP complicated with AKI was analyzed by multivariate logistic regression analysis, and a nomogram prediction model was established by R software. The receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to evaluate its prediction performance. Results:The acute physiology and chronic health assessment Ⅱ (APACHEⅡ) and Ranson score, the incidence of abdominal compartment syndrome (ACS) and systemic inflammatory response syndrome (SIRS), the cases of shock and mechanical ventilation, and the levels of blood lactic acid (BLA), blood creatinine (Scr), urea nitrogen (BUN), C-reactive protein (CRP), procalcitonin (PCT) and cystatin C(Cys C) in peripheral blood were significantly higher in AKI group than those in non-AKI group, while the levels of blood calcium were lower than those in non-AKI group, and the differences were statistically significant (all P value <0.05). Multivariate logistic regression analysis showed that APACHEⅡ score ( OR=1.185, 95% CI 1.074-1.308, P=0.001), Ranson score ( OR=12.668, 95% CI 5.102-31.456, P<0.001), Scr ( OR=1.028, 95% CI 1.002-1.054, P=0.034), PCT ( OR=4.298, 95% CI 1.379-13.395, P<0.001) and Cys C ( OR=38738.38, 95% CI 43.190-347459.41, P<0.001) were independent risk factors for SAP complicated AKI. Serum calcium ( OR=0.0001, 95% 0.000-0.048, P<0.001) was an independent protective factor for SAP complicated AKI. A Nomogram prediction model based on the six factors above were established, and its AUC, sensitivity and specificity to predict AKI were 0.987, 99.0% and 98.5% in the training set, and were 0.976, 98.6% and 94.2% in the validation set. Conclusions:This study successfully established an early prediction model with high predict value for SAP complicated with AKI, which can efficiently predict the risk of SAP with concurrent AKI.