Li WJ, Mi N, Huang X, Liu CS, Zhang ST, Liao Y, Yu Y. Predicting unplanned extubation risk in patients with endoscopic nasobiliary drainage. World J Gastrointest Surg 2025; 17(12): 112175 [DOI: 10.4240/wjgs.v17.i12.112175]
Corresponding Author of This Article
Yan Yu, MD, Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Army Medical University, No. 83 Xinqiao Street, Shapingba District, Chongqing 400037, China. yuyan2025@tmmu.edu.cn
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Gastroenterology & Hepatology
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Retrospective Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Dec 27, 2025 (publication date) through Dec 25, 2025
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World Journal of Gastrointestinal Surgery
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1948-9366
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Li WJ, Mi N, Huang X, Liu CS, Zhang ST, Liao Y, Yu Y. Predicting unplanned extubation risk in patients with endoscopic nasobiliary drainage. World J Gastrointest Surg 2025; 17(12): 112175 [DOI: 10.4240/wjgs.v17.i12.112175]
World J Gastrointest Surg. Dec 27, 2025; 17(12): 112175 Published online Dec 27, 2025. doi: 10.4240/wjgs.v17.i12.112175
Predicting unplanned extubation risk in patients with endoscopic nasobiliary drainage
Wen-Jing Li, Na Mi, Xi Huang, Chang-Sha Liu, Shu-Ting Zhang, Yu Liao, Yan Yu
Wen-Jing Li, Na Mi, Xi Huang, Chang-Sha Liu, Shu-Ting Zhang, Yu Liao, Yan Yu, Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Army Medical University, Chongqing 400037, China
Author contributions: Li WJ and Mi N contributed equally as co-first authors for study design, data analysis, and manuscript writing; Huang X, Liu CS, Zhang ST, and Liao Y participated in data collection and manuscript revision; Yu Y served as corresponding author, providing study supervision and final approval; All authors read and approved the final version of the manuscript.
Supported by the Chongqing Municipal Health Commission Project, No. 2024WSJK017.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital, Army Medical University (Approval No. 2024-Research-135-02).
Informed consent statement: As this was a retrospective study, the requirement for informed consent was waived by the Ethics Committee.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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: Yan Yu, MD, Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Army Medical University, No. 83 Xinqiao Street, Shapingba District, Chongqing 400037, China. yuyan2025@tmmu.edu.cn
Received: August 6, 2025 Revised: September 3, 2025 Accepted: October 13, 2025 Published online: December 27, 2025 Processing time: 140 Days and 17.8 Hours
Abstract
BACKGROUND
Unplanned extubation (UE) after endoscopic retrograde cholangiopancreatography plus endoscopic nasobiliary drainage (ENBD) increases patient morbidity and prolongs hospitalization duration.
AIM
To construct a risk prediction model for UE in patients undergoing ENBD to provide evidence for clinical nursing.
METHODS
A multicenter retrospective study was conducted, collecting data from 981 patients undergoing ENBD from three hospitals in Chongqing from January 2018 to June 2024, randomly allocated to modeling and validation groups in a 7:3 ratio. Logistic regression analysis was used to screen independent risk factors, construct prediction models, and draw nomograms.
RESULTS
The overall incidence of UE was 6.12% (60/981). The majority (70.00%) of extubations occurred within 24-72 h postoperatively. Multivariate logistic regression analysis identified age ≥ 61 years [odds ratio (OR) = 2.341, 95% confidence interval (CI): 1.28-4.27], smoking history (OR = 2.876, 95%CI: 1.54-5.37), prolonged fasting time (OR = 1.124, 95%CI: 1.05-1.20), prolonged catheter duration (OR = 1.286, 95%CI: 1.09-1.52), and consciousness changes (OR = 3.152, 95%CI: 1.69-5.89) were independent risk factors while serum albumin was a protective factor (OR = 0.912, 95%CI: 0.87-0.95). The model receiver operating characteristic area under curve was 0.881 with accuracy of 80.36%, sensitivity of 83.59%, and specificity of 74.88%. A nomogram total score ≥ 199 points corresponded to a high-risk threshold.
CONCLUSION
The six-factor risk prediction model had good discrimination and accuracy, which can provide clinical nursing staff with scientific evidence to identify patients at high risk and help reduce the incidence of UE.
Core Tip: This multicenter retrospective study developed and validated a six-factor risk prediction model for unplanned nasobiliary tube extubation after endoscopic retrograde cholangiopancreatography plus endoscopic nasobiliary drainage. Independent predictors included age ≥ 61 years, smoking history, prolonged fasting, catheter duration, consciousness change, and low serum albumin. The nomogram demonstrated excellent discrimination (area under the curve = 0.881) and clinical utility. A total score ≥ 199 indicated high risk. This tool enables early identification of patients at high risk and supports targeted nursing interventions to reduce complications, hospitalization time, and healthcare costs.
