ArticleAnnals of surgical treatment and research2022
External validation of risk prediction platforms for pancreatic fistula after pancreatoduodenectomy using nomograms and artificial intelligence.
Article in Annals of surgical treatment and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Postoperative pancreatic fistula: a bibliometric analysis of research trends and a meta-analysis on the association with intra pancreatic fat deposition.Scientific reports · 2025Pooled it
- CCN2/CTGF-Driven Myocardial Fibrosis and NT-proBNP Synergy as Predictors of Mortality in Maintenance Hemodialysis.International journal of molecular sciences · 2025Article
- Risk stratification of postoperative pancreatic fistula and other complications following pancreatoduodenectomy. How far are we? A scoping review.Langenbeck's archives of surgery · 2025Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). We previously developed nomogram- and artificial intelligence (AI)-based risk prediction platforms for POPF after PD. This study aims to externally validate these platforms. Methods: Between January 2007 and December 2016, a total of 1,576 patients who underwent PD in Seoul National University Hospital, Ilsan Paik Hospital, and Boramae Medical Center were retrospectively reviewed. The individual risk scores for POPF were calculated using each platform by Samsung Medical Center. The predictive ability was evaluated using a receiver operating characteristic curve and the area under the curve (AUC). The optimal predictive value was obtained via backward elimination in accordance with the results from the AI development process. Results: The AUC of the nomogram after external validation was 0.679 (P < 0.001). The values of AUC after backward elimination in the AI model varied from 0.585 to 0.672. A total of 13 risk factors represented the maximal AUC of 0.672 (P < 0.001). Conclusion: We performed external validation of previously developed platforms for predicting POPF. Further research is needed to investigate other potential risk factors and thereby improve the predictability of the platform.
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