中文English
ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R

Risk prediction models for pancreatic fistula after pancreaticoduodenectomy: A systematic review and a Meta-analysis

DOI: 10.12449/JCH241121
Research funding:

2023 Science and Technology Plan Project of Sichuan Province (2023YFS0070)

More Information
  • Corresponding author: JIA Ping, aonejia@126.com (ORCID: 0000-0003-1584-9616)
  • Received Date: 2024-05-15
  • Accepted Date: 2024-06-03
  • Published Date: 2024-11-25
  •   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.

     

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