Predictive value of non-enhanced CT combined with clinical indicators in severe acute pancreatitis
10.3760/cma.j.issn.1671-0282.2023.10.007
- VernacularTitle:平扫CT联合临床指标对重症急性胰腺炎预测价值的探讨
- Author:
Qiaoliang CHEN
1
;
Dandan XU
;
Junjie YANG
;
Weisen YANG
;
Yan GU
;
Yeqing WANG
;
Guohua FAN
;
Guojian YIN
;
Liang XU
Author Information
1. 苏州大学附属第二医院影像科，苏州 215004
- Keywords:
Prediction model;
Severe acute pancreatitis;
CT;
Nomogram
- From:
Chinese Journal of Emergency Medicine
2023;32(10):1333-1339
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and validate a nomogram model for early prediction of the risk of acute pancreatitis (AP) progressing to severe acute pancreatitis (SAP).Methods:CT signs and clinical laboratory parameters of 361 AP patients admitted to our Hospital from January 2016 to July 2022 were retrospectively collected. There were 221 males (61.2%) and 140 females (38.8%). According to the Atlantic score, all patients were divided into the SAP group (64 cases) and the non-SAP (NSAP) group (297 cases). Univariate analysis was used to screen out variables with statistically significant differences. Multivariate Logistic regression analysis was used to screen out the independent risk factors of SAP, and finally a nomogram prediction model was established. Receiver operating characteristic (ROC) curve, calibration curve and decision curve (DCA) were used to evaluate the predictive efficacy, accuracy and clinical practicability of the model, and Bootstrap method was used to verify the model internally.Results:Univariate analysis and multivariate Logistic regression analysis showed that pleural effusion ( OR=7.353, 95% CI: 3.344-16.170), posterior pararenal space (PPS) involvement ( OR=3.149, 95% CI: 1.314-7.527), serum creatinine concentration (Cr) ( OR=1.027, 95% CI: 1.017-1.038) and serum calcium concentration (Ca 2+) ( OR=0.038, 95% CI: 0.009-0.166) were independent risk factors for SAP ( P<0.05). A Nomogram model was established based on these four factors. The area under the ROC curve (AUC) of this model was 0.905 (95% CI: 0.869-0.933), indicating high predictive efficiency. Internal verification showed that the model had good accuracy in predicting SAP, and C-index was 0.90. DCA analysis showed that the model had high clinical practicability. Conclusions:The Nomogram model combining pleural effusion, PPS involvement, Cr and Ca 2+ had a good effect on early prediction of SAP, which could provide a new reference tool for clinical diagnosis and treatment.
