ArticleWorld journal of gastrointestinal surgery2022
Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy.
Article in World journal of gastrointestinal surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of perioperative immunonutrition on postoperative outcomes in pancreaticoduodenectomy: a systematic review and meta-analysis of randomized controlled trials.BMC gastroenterology · 2024Pooled it
- Perioperative changes in systemic inflammatory markers predict major complications after distal pancreatectomy.Updates in surgery · 2026Article
- Risk stratification of postoperative pancreatic fistula and other complications following pancreatoduodenectomy. How far are we? A scoping review.Langenbeck's archives of surgery · 2025Article
- Comparison of Controlling Nutritional Status Score with Bedside Index for Severity in Acute Pancreatitis Score and Atlanta Classification for Mortality in Patients with Acute Pancreatitis.Journal of clinical medicine · 2024Article
- Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives.World journal of gastroenterology · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification.
aimTo develop an ML-aided model for PF risk stratification.
methodsWe retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model.
resultsA total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370-1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191-1.261) and 0.882 (95%CI: 0.321-1.443).
conclusionFluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.
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