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
ARTICLE HIGHLIGHTS
Research background

We provide insights into the candidate predictive markers associated with a high risk of postoperative pancreatic fistula (PF) via serum inflammatory secretion. With the help of hemoglobin level × albumin level × lymphocyte count/platelet count ratio, neutrophil-to-albumin ratio, C-reactive protein, procalcitonin and platelet-to-lymphocyte ratio, we develop machine learning (ML)-based predictive models, and the predictive performance of these unsupervised integrated models was superior to that of traditional predictive models. We expect these findings to extend research to strengthen clinical decision-making and guide treatment.

Research motivation

Fluctuating serological inflammation markers and prognostic nutritional index can be detected in the early postoperative period, and clinically well established to predict postoperative PF; in particular, random forest classifier (RFC) performed best, which can guide optimal treatment, clinical management and prevent or mitigate adverse consequences.

Research objectives

A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was RFC, the area under the curve (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).

Research methods

As for descriptive variables (i.e., continuous or classified variables), the median (interquartile range) or frequency (percentage) were used for statistics in this study. The χ2 test or Mann–Whitney test was used to calculate the variables between groups to evaluate whether there was a statistical difference. Stepwise regression based on the minimum value of the Akaike information standard was used to select the variables. All data analysis was completed with the help of R language software (version 4.0.4, http://www.r-project.org/). All P values were double tailed, and P < 0.05 was statistically significant.

Research results

A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was RFC, the area under the curve (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).

Research conclusions

Fluctuating serological inflammatory markers and prognostic nutritional index (PNI) can be detected in the early postoperative period, which has been clinically proved to predict postoperative PF. In particular, RFC performed best, which can guide optimal treatment, clinical management, and prevent or mitigate adverse consequences.

Research perspectives

PD, also known as a Whipple procedure, is one of the most difficult and complex surgeries that carries a high rate of major complications. Postoperative PF, as one of the most difficult complications after PD, can seriously endanger the lives of patients, so it has become an area of continuous concern for pancreatic surgeons. Although the safety of PD has improved significantly in the past three decades, previous prospective studies have reported that postoperative PF has an incidence of > 10%. Understanding the potential complications and early warning of these complications is important for the care of these patients.