• J Emerg Med · Jul 2024

    Analysis of Risk Factors for Severe Acute Pancreatitis in the Early Period (<24 h) After Admission.

    • Qian Yang, Yunhan Gao, Zhongfu Li, Jiang Zheng, Hong Fu, and Yu Ma.
    • Department of General Surgery, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 400014, China.
    • J Emerg Med. 2024 Jul 1; 67 (1): e1e9e1-e9.

    BackgroundSevere acute pancreatitis (SAP) has high mortality. Early identification of high-risk factors that may progress to SAP and active intervention measures may improve the prognosis of SAP patients.ObjectiveClinical data within 24 h after admission were retrospectively analyzed to provide an evidence for early screening of high-risk factors in patients with SAP.MethodsA review of clinical data of acute pancreatitis patients from January 1, 2018, to December 31, 2022, was conducted. We compared the clinical data of SAP and non-SAP patients, and a multivariable logistic regression model was used to identify the independent predictors of SAP. The receiver operating characteristic (ROC) curve of SAP was drawn for continuous numerical variables to calculate the optimal clinical cutoff value of each variable, and the predictive value of each variable was compared by the area under the ROC curve.ResultsBased on the multivariate logistic regression analysis of Age (odds ratio (OR), 1.032;95% confident interval (CI),1.018-1.046, p < 0.001), body mass index (BMI) (OR, 1.181; 95% CI,1.083-1.288, p < 0.001), Non-HTGAP (nonhypertriglyceridemic acute pancreatitis) (OR, 2.098; 95% CI,1.276-3.45, p = 0.003), white blood cell count (WBC) (OR,1.072; 95% CI,1.034-1.111, p < 0.001), procalcitonin (PCT) (OR, 1.060; 95% CI, 1.027-1.095, p < 0.001), serum calcium (Ca) (OR,0.121; 95% CI, 0.050-0.292, p < 0.001), computed tomography severity index (CTSI) ≥4 (OR,12.942;95% CI,7.267-23.049, p < 0.001) were identified as independent risk factors for SAP. The area under the ROC curve (AUC) and optimal CUT-OFF values of continuous numerical variables for predicting SAP were Age (0.6079,51.5), BMI (0.6,23.25), WBC (0.6701,14.565), PCT (0.7086, 0.5175), Ca (0.7787,1.965), respectively.ConclusionAge, BMI, non-HTGAP, WBC, PCT, serum Ca and CTSI≥4 have good predictive value for SAP.Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

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