| Autor: |
Yuanyue Zhu, Long Wang, Lin Lin, Yanan Huo, Qin Wan, Yingfen Qin, Ruying Hu, Lixin Shi, Qing Su, Xuefeng Yu, Li Yan, Guijun Qin, Xulei Tang, Gang Chen, Shuangyuan Wang, Hong Lin, Xueyan Wu, Chunyan Hu, Mian Li, Min Xu, Yu Xu, Tiange Wang, Zhiyun Zhao, Zhengnan Gao, Guixia Wang, Feixia Shen, Xuejiang Gu, Zuojie Luo, Li Chen, Qiang Li, Zhen Ye, Yinfei Zhang, Chao Liu, Youmin Wang, Shengli Wu, Tao Yang, Huacong Deng, Lulu Chen, Tianshu Zeng, Jiajun Zhao, Yiming Mu, Weiqing Wang, Guang Ning, Yufang Bi, Yuhong Chen, Jieli Lu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Gut and Liver, Vol 18, Iss 5, Pp 926-927 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1976-2283 |
| DOI: |
10.5009/gnl230220.e |
| Popis: |
Background/Aims: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection. Methods: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. Results: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP. Conclusions: We have developed a prediction nomogram of NP that is of great value in guiding clinical decision. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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