Evidence map›Paper›PMID 40307833›Full record

ArticleBMC surgery2025

Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

Abstract read
In one paragraph

Article in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qianchang WangShandong First Medical University, Jinan, Shandong, China.
Zhe WangShandong First Medical University, Jinan, Shandong, China.
Fangfeng LiuShandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital, Jinan, Shandong, China. liufangfeng@sdfmu.edu.cn.
Zhengjian WangShandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital, Jinan, Shandong, China.
Qingqiang NiShandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital, Jinan, Shandong, China.
Hong ChangShandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital, Jinan, Shandong, China.

Funding

Shandong Provincial Health Commission General Program Grant No. 202304010282
6 · The paper itself

Abstract

backgroundClinically relevant postoperative pancreatic fistula (CR-POPF) following laparoscopic pancreaticoduodenectomy (LPD) is a critical complication that significantly worsens patient outcomes. However, the heterogeneity of its risk factors and the clinical utility of predictive models remain to be fully elucidated. This study aims to systematically analyze the risk factors for CR-POPF and develop an optimized predictive model using machine learning algorithms, providing an evidence-based approach for individualized risk assessment in patients undergoing LPD.

methodsA retrospective study was conducted, including 210 patients with periampullary cancer who underwent laparoscopic pancreaticoduodenectomy (LPD) at the Hepatobiliary Surgery Center, Olympic Stadium Campus, Shandong Provincial Hospital Affiliated to Shandong First Medical University, from January 2017 to January 2024. Patients were classified into the clinically relevant pancreatic fistula (CR-POPF) group (n = 34) and the non-clinically relevant pancreatic fistula (non-CR-POPF) group (n = 176) according to the 2016 criteria of the International Study Group of Pancreatic Surgery (ISGPS). Potential risk factors were identified through intergroup comparisons, and independent risk factors were determined using univariate and multivariate logistic regression analyses. Based on these findings, a predictive model for CR-POPF was developed using machine learning algorithms.

resultsCR-POPF was associated with higher BMI, monocyte levels, platelet count, total bilirubin, AST, ALT, and lower albumin. Pathological diagnosis of ampullary carcinoma and soft pancreatic texture were significantly more common in the CR-POPF group. Multivariate analysis identified soft pancreatic texture as an independent predictor (OR = 4.99, 95% CI: 1.93-12.86). Among all models, the random forest model showed the best performance (AUC = 0.747, sensitivity = 0.917, specificity = 0.574), using only preoperative variables such as age, gender, BMI, hypertension, diabetes, hemoglobin, platelets, AST, and ALT.

conclusionSoft pancreatic texture was identified as an independent risk factor for postoperative pancreatic fistula following laparoscopic pancreaticoduodenectomy (LPD). The random forest model based on preoperative clinical variables enables individualized risk prediction, offering value for preoperative planning and postoperative care.

Indexed as

LaparoscopyMachine LearningPancreatic FistulaPancreaticoduodenectomyPostoperative ComplicationsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsClinically relevant postoperative pancreatic fistulaLaparoscopic pancreaticoduodenectomyMachine learningPostoperative complicationsPredictive model

Identifiers

PMID40307833
PMCPMC12045007

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.