Risk prediction models for pancreatic fistula after pancreaticoduodenectomy： A systematic review and a Meta-analysis
Journal Title: Journal of Clinical Hepatology - Year 2024, Vol 40, Issue 11
Abstract
[Objective] To systematically review the risk prediction models for postoperative pancreatic fistula （POPF） after pancreaticoduodenectomy （PD）， and to provide a reference for the clinical screening and application of POPF-related risk models. [Methods] This study was conducted according to the PRISMA guidelines， with a PROSPERO registration number of CRD42023437672. PubMed， Scopus， Embase， Web of Science， the Cochrane Library， CNKI， VIP， Wanfang Data， China Medical Journal Full-text Database， and CBM were searched for studies on establishing risk prediction models for POPF after PD published up to April 26， 2024. The PROBAST tool was used to assess the quality of articles， and RevMan 5.4 and MedCalc were used to perform the Meta-analysis. [Results] A total of 36 studies were included， involving 20 119 in total， and the incidence rate of POPF after PD was 7.4% — 47.8%. A total of 55 risk prediction models were established in the 36 articles， with an area under the receiver operating characteristic curve （AUC） of 0.690 — 0.952， among which 52 models had an AUC of >0.7. The quality assessment of the articles showed high risk of bias and good applicability. MedCalc was used to perform a statistical analysis of AUC values， and the results showed a pooled AUC of 0.833 （95% confidence interval： 0.808 — 0.857）. The Meta-analysis showed that body mass index， amylase in drainage fluid on the first day after surgery， preoperative serum albumin， pancreatic duct diameter， pancreatic texture， fat score， tumor location， blood loss， sex， time of operation， main pancreatic duct index， and pancreatic CT value were predictive factors for POPF （all P<0.05）. [Conclusion] The risk prediction models for POPF after PD is still in the exploratory stage. There is a lack of calibration methods and internal validation for most prediction models， and only the univariate analysis is used to for the screening of variables， which leads to the high risk of bias. In the future， it is necessary to improve the methods for model establishment， so as to develop risk prediction models with a higher prediction accuracy.
Authors and Affiliations
Zaichun PU, Ping JIA, Juan LIU, Yushuang SU, Li WANG, Qin ZHANG, Danyang GUO
Keywords
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- EP ID EP753506
- DOI 10.12449/JCH241121
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