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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 21, 2022; 28(7): 715-731
Published online Feb 21, 2022. doi: 10.3748/wjg.v28.i7.715
Published online Feb 21, 2022. doi: 10.3748/wjg.v28.i7.715
Clinical online nomogram for predicting prognosis in recurrent hepatolithiasis after biliary surgery: A multicenter, retrospective study
Tian Pu, Jiang-Ming Chen, Zi-Han Li, Dong Jiang, Qi Guo, Ang-Qing Li, Zi-Xiang Chen, Kun Xie, Yi-Jun Zhao, Xiao-Ping Geng, Fu-Bao Liu, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Ming Cai, Cheng Wang, Department of General Surgery, The First Affiliated Hospital of the University of Science and Technology of China, Hefei 230001, Anhui Province, China
Hui Hou, Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Zheng Lu, Department of General Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu 233030, Anhui Province, China
Author contributions: Pu T, Chen JM, Li ZH, and Jiang D contributed to the data analysis and participated in drafting the article; Guo Q, Li AQ, and Cai M extracted the clinical data and calculated the clinical correlations; Chen ZX, Xie K and Zhao YJ interpreted the results and revised the manuscript; Liu FB, Hou H, Lu Z, Wang C, and Geng XP gave final approval of the version to be published; and all authors contributed to the design and interpretation of the study and to further drafts and approved the final version to be published.
Supported by the Key Research and Development Plan of Anhui Province , No. 1804h08020239 .
Institutional review board statement: The study was reviewed and approved by the Institutional ethics committees of the First Affiliated Hospital of Anhui Medical University (Approval No: Quick-PJ2021-08-19).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: No additional data are available.
STROBE statement: We have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Fu-Bao Liu, MD, PhD, Chief Doctor, Professor, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Hefei 230022, Anhui Province, China. lancetlfb@126.com
Received: September 14, 2021
Peer-review started: September 14, 2021
First decision: November 7, 2021
Revised: November 20, 2021
Accepted: January 19, 2022
Article in press: January 19, 2022
Published online: February 21, 2022
Processing time: 156 Days and 4.1 Hours
Peer-review started: September 14, 2021
First decision: November 7, 2021
Revised: November 20, 2021
Accepted: January 19, 2022
Article in press: January 19, 2022
Published online: February 21, 2022
Processing time: 156 Days and 4.1 Hours
Core Tip
Core Tip: This is a multicenter, retrospective study to establish a nomogram to predict the prognosis of patients with recurrent hepatolithiasis after biliary surgery. After this, an online calculator was developed, and the nomograms are freely available on the internet. We used the concordance index, calibration, area under curve, decision curve analysis, clinical impact curves, and visual fit indices to evaluate the accuracy of the nomogram. Compared with predictions using traditional classification models, those using our nomogram showed better agreement with actual observations.