Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 28, 2022; 28(32): 4681-4697
Published online Aug 28, 2022. doi: 10.3748/wjg.v28.i32.4681
Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models
Jian Li, Qi-Qi Wu, Rong-Hua Zhu, Xing Lv, Wen-Qiang Wang, Jin-Lin Wang, Bin-Yong Liang, Zhi-Yong Huang, Er-Lei Zhang
Jian Li, Rong-Hua Zhu, Xing Lv, Wen-Qiang Wang, Jin-Lin Wang, Bin-Yong Liang, Zhi-Yong Huang, Er-Lei Zhang, Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Qi-Qi Wu, Department of Trauma Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Author contributions: All authors contributed to the study; Li J wrote the manuscript, and collected and analysed the data; Wu QQ collected the data, and contributed to the follow-up results; Zhu RH, Lv X, and Wang WQ collected the data and performed the analysis; Wang JL contributed to the data; Liang BY and Huang ZY provided the resources and supervision; Zhang EL contributed to writing the manuscript, and drafting the conception and design; all authors read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 81902839; and Hubei Provincial Special Grants for Scientific and Technical Innovation, No. 2021BCA115.
Institutional review board statement: The study was approved by the Medical Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (No. 2022-LSZ (S066)).
Informed consent statement: Written informed consent from the patients was waived due to the retrospective nature of this study.
Conflict-of-interest statement: All authors declare no conflicts of interest related to this article.
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: Er-Lei Zhang, MD, Doctor, Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Qiaokou District, Wuhan 430030, Hubei Province, China. baiyu19861104@163.com
Received: April 16, 2022
Peer-review started: April 16, 2022
First decision: May 12, 2022
Revised: May 25, 2022
Accepted: July 31, 2022
Article in press: July 31, 2022
Published online: August 28, 2022
Processing time: 132 Days and 2.5 Hours
Abstract
BACKGROUND

For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.

AIM

To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.

METHODS

We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).

RESULTS

Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.

CONCLUSION

PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.

Keywords: Portal hypertension; Splenectomy; Portal vein thrombosis; Postoperative platelet elevation rate; Practical model; Machine learning

Core Tip: For patients with portal hypertension related to hepatitis B virus, postoperative platelet elevation rate (PPER) is an important predictor of the formation of portal vein thrombosis (PVT) after splenectomy. This study was the first to construct PPER-based practical models for predicting PVT, which would be helpful for clinicians to recognize individuals at high risk of PVT as soon as possible.