Evaluation of scoring systems and hematological parameters in the severity stratification of early-phase acute pancreatitis

评估评分系统和血液学参数在早期急性胰腺炎严重程度分层中的作用

阅读:1

Abstract

BACKGROUND: Acute pancreatitis (AP) is an emergency gastrointestinal disease that requires immediate diagnosis and urgent clinical treatment. An accurate assessment and precise staging of severity are essential in initial intensive therapy. AIM: To explore the prognostic value of inflammatory markers and several scoring systems [Acute Physiology and Chronic Health Evaluation II, the bedside index of severity in AP (BISAP), Ranson's score, the computed tomography severity index (CTSI) and sequential organ failure assessment] in severity stratification of early-phase AP. METHODS: A total of 463 patients with AP admitted to our hospital between 1 January 2021 and 30 June 2024 were retrospectively enrolled in this study. Inflammation marker and scoring system levels were calculated and compared between different severity groups. Relationships between severity and several predictors were evaluated using univariate and multivariate logistic regression models. Predictive ability was estimated using receiver operating characteristic curves. RESULTS: Of the 463 patients, 50 (10.80%) were classified as having severe AP (SAP). The results revealed that the white cell count significantly increased, whereas the prognostic nutritional index measured within 48 hours (PNI(48)) and calcium (Ca(2+)) were decreased as the severity of AP increased (P < 0.001). According to multivariate logistic regression, C-reactive protein measured within 48 hours (CRP(48)), Ca(2+) levels, and PNI(48) were independent risk factors for predicting SAP. The area under the curve (AUC) values for the CRP(48), Ca(2+), PNI(48), Acute Physiology and Chronic Health Evaluation II, sequential organ failure assessment, BISAP, CTSI, and Ranson scores for the prediction of SAP were 0.802, 0.736, 0.871, 0.799, 0.783, 0.895, 0.931 and 0.914, respectively. The AUC for the combined CRP(48) + Ca(2+) + PNI(48) model was 0.892. The combination of PNI(48) and Ranson achieved an AUC of 0.936. CONCLUSION: Independent risk factors for developing SAP include CRP(48), Ca(2+), and PNI(48). CTSI, BISAP, and the combination of PNI(48) and the Ranson score can act as reliable predictors of SAP.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。