Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Sep 27, 2022; 14(9): 963-975
Published online Sep 27, 2022. doi: 10.4240/wjgs.v14.i9.963
Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy
Zhi-Da Long, Chao Lu, Xi-Gang Xia, Bo Chen, Zhi-Xiang Xing, Lei Bie, Peng Zhou, Zhong-Lin Ma, Rui Wang
Zhi-Da Long, Chao Lu, Xi-Gang Xia, Bo Chen, Zhi-Xiang Xing, Lei Bie, Peng Zhou, Rui Wang, Department of Hepatobiliary and Pancreaticosplenic Surgery, Jingzhou Hospital, Yangtze University, Jingzhou 434020, Hubei Province, China
Zhong-Lin Ma, Department of Hepatobiliary Surgery, Lu’an Hospital of AnHui Medical University, Hefei 237006, Anhui Province, China
Author contributions: Long ZD, Lu C, Xia XG, Chen B, Xing ZX, Bie L, Zhou P, Ma ZL, and Wang R designed the research study; Long ZD, Lu C, Xia XG, and Chen B performed the research; Xia XG, Chen B, and Xing ZX contributed new reagents and analytic tools; Long ZD, Lu C, Xia XG, Chen B, Xing ZX, Bie L, Zhou P, Ma ZL, and Wang R analyzed the data and wrote the manuscript; all authors have read and approve the final manuscript.
Institutional review board statement: This retrospective study was following the declaration of Helsinki, and was ethically reviewed and approved by the Institutional Ethics Committee of Jingzhou Hospital, No. 2021-JH005.
Informed consent statement: Since the patient information contained in this study was anonymous, written informed consent was not obtained from all participants.
Conflict-of-interest statement: All authors declare that there is no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rui Wang, MD, Surgical Oncologist, Department of Hepatobiliary and Pancreaticosplenic Surgery, Jingzhou Hospital, Yangtze University, No. 60 Chuyuan Road, Jingzhou District, Jingzhou 434020, Hubei Province, China. wangrui_20222022@163.com
Received: May 3, 2022
Peer-review started: May 3, 2022
First decision: May 11, 2022
Revised: May 22, 2022
Accepted: July 27, 2022
Article in press: July 27, 2022
Published online: September 27, 2022
Processing time: 142 Days and 1 Hours
Abstract
BACKGROUND

Postoperative 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.

AIM

To develop an ML-aided model for PF risk stratification.

METHODS

We 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.

RESULTS

A 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).

CONCLUSION

Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

Keywords: Pancreatoduodenectomy; Pancreatic fistula; Machine learning algorithm; Systemic inflammatory biomarker; Risk prediction

Core tip: Our research is based on machine learning (ML) algorithms and integrates the correlation between serum inflammatory factors and high risk of postoperative pancreatic fistula (PF), and constructs early warning models that can predict postoperative PF, and the predictive efficiency of these ML-based models may be at the population-based level. In the future, we expect these findings to expand external research to strengthen valuable supporting information and guide treatment decisions.