Establishment of a nomogram prediction model for poor prognosis of acute pancreatitis based on inflammatory factors， lung ultrasound， and CT scores
- VernacularTitle:基于炎症因子、肺超声和CT评分系统的急性胰腺炎预后不良列线图预测模型的构建
- Author:
Xia REN
1
;
Ye YE
1
;
Luojie LIU
1
;
Xiaodan XU
1
;
Yan ZHANG
2
Author Information
- Publication Type:Journal Article
- Keywords: Pancreatitis; C-Reactive Protein; Interleukins; Lung Ultrasound; Nomograms
- From: Journal of Clinical Hepatology 2025;41(4):713-721
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the independent risk factors for poor prognosis in patients with acute pancreatitis （AP） by analyzing inflammatory factors， lung ultrasound （LUS） scores， and CT scores， to establish a nomogram prediction model， and to provide a basis for early clinical intervention. MethodsA total of 409 patients with AP who were admitted to Changshu Hospital Affiliated to Soochow University from January 2021 to October 2023 were enrolled as subjects， and they were divided into modeling group with 288 patients and validation group with 121 patients using the simple random sampling method at a ratio of 7∶3. According to the prognosis， each group was further divided into poor prognosis group and good prognosis group. The levels of C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， interleukin-10 （IL-10）， and tumor necrosis factor-α （TNF-α） were measured for both groups within 72 hours after admission， and LUS scores， modified CT severity index （MCTSI）， and extrapancreatic inflammation on computed tomography （EPIC） scores were assessed within 48 — 72 hours after admission. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A LASSO regression analysis was used to screen for the variables that were included in the multivariate logistic regression model to identify the independent risk factors for the poor prognosis of AP， and then a nomogram prediction model was established. The receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and goodness of fit of the nomogram model， and a decision curve analysis was used to assess the clinical applicability of the model. ResultsAmong the 288 patients with AP in the modeling group， there were 33 （11.46%） in the poor prognosis group and 255 （88.54%） in the good prognosis group； among the 121 patients with AP in the validation group， there were 13 （10.74%） in the poor prognosis group and 108 （89.26%） in the good prognosis group. Compared with the good prognosis group， the poor prognosis group had significantly higher levels of CRP （Z=3.607， P<0.05）， IL-6 （Z=4.189， P<0.05）， and TNF-α （t=2.584， P<0.05）， and significantly higher scores of LUS （t=8.075， P<0.05）， MCTSI （t=5.929， P<0.05）， and EPIC （t=8.626， P<0.05）. The multivariate logistic regression analysis showed that CRP （odds ratio ［OR］=3.592， 95% confidence interval ［CI］： 1.272 — 10.138， P<0.05）， IL-6 （OR=4.225， 95%CI： 1.468 — 12.156， P<0.05）， TNF-α （OR=3.540， 95%CI： 1.205 — 10.401， P<0.05）， LUS （OR=7.094， 95%CI： 2.398 — 20.986， P<0.05）， MCTSI （OR=7.612， 95%CI： 2.832 — 20.462， P<0.05）， and EPIC （OR=11.915， 95%CI： 4.007 — 35.432， P<0.05） were independent risk factor for poor prognosis in patients with AP. A nomogram prediction model was established based on the above 6 indicators， which had an area under the ROC curve of 0.924 （95%CI： 0.883 — 0.964）， and the Youden index for the optimal cut-off value was 0.670， with a sensitivity of 0.909 and a specificity of 0.761. The calibration curve showed good consistency between the predicted and observed results in both the modeling group and the validation group. The decision curve analysis showed that the predictive model had certain clinical effectiveness. ConclusionThe nomogram model for predicting the risk of poor prognosis in AP patients based on CRP， IL-6， TNF-α， LUS score， MCTSI score， and EPIC score has relatively good predictive performance and can provide important strategic guidance for developing early intensified treatment regimens for AP patients in clinical practice.
