A simple nomogram for early postoperative risk prediction of clinically relevant pancreatic fistula after pancreatoduodenectomy

Author:

Honselmann K. C.ORCID,Antoine C.,Frohneberg L.,Deichmann S.,Bolm L.,Braun R.,Lapshyn H.,Petrova E.,Keck T.,Wellner U.,Bausch D.

Abstract

Abstract Purpose Postoperative pancreatic fistulae (POPF) present a serious and life-threatening complication after pancreatic head resections (PD). Therefore, reliable risk stratification to identify those at risk is urgently needed. The aim of this study was to identify postoperative laboratory parameters for the prediction of POPF in the early postoperative period. Methods One hundred eighty-two patients who underwent PD from 2012 until 2017 were retrospectively analyzed. Multivariate logistic regression was performed using the GLM (general linear model) method for model building. Two nomograms were created based on the GLM models of postoperative day one and postoperative day one to five. A cohort of 48 patients operated between 2018 and 2019 served as internal validation. Results Clinically relevant pancreatic fistulae (CR-POPF) were present in 16% (n = 29) of patients. Patients with CR-POPF experienced significantly more insufficiencies of gastroenterostomies, delayed gastric emptying, and more extraluminal bleeding than patients without CR-POPF. Multivariate analysis revealed multiple postoperative predictive models, the best one including ASA, main pancreatic duct diameter, operation time, and serum lipase as well as leucocytes on day one. This model was able to predict CR-POPF with an accuracy of 90% and an AUC of 0.903. Two nomograms were created for easier use. Conclusion Clinically relevant fistula can be predicted using simple laboratory and clinical parameters. Not serum amylase, but serum lipase is an independent predictor of CR-POPF. Our simple nomograms may help in the identification of patients for early postoperative interventions.

Funder

Universität zu Lübeck

Publisher

Springer Science and Business Media LLC

Subject

Surgery

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3