Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Construction and validation of a predictive model for the risk of emergence delirium in older adult patients
Yi Xin, Bin He, Xiao-Hui Wei, Ya-Ling Yan, Chen Huang, Chun-Yan Gao, Shuo Wang, Guang-Ming Zhang, Rui Li, Ying Wu
Yi Xin, Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Bin He, Department of Cardiovascular Surgery, Peking University Shenzhen Hospital, Shenzhen 518036, Guangdong Province, China
Xiao-Hui Wei, Rui Li, Department of Nursing, Shanghai Pulmonary Hospital, Shanghai 200082, China
Ya-Ling Yan, Department of Vascular Surgery, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
Chen Huang, Chun-Yan Gao, Shuo Wang, Guang-Ming Zhang, Department of Anesthesiology, Tongren Hospital Shanghai Jiao Tong University School of Medicine, Shanghai 200335, China
Ying Wu, Department of Nursing, Tongren Hospital Shanghai Jiao Tong University School of Medicine, Shanghai 200335, China
Co-first authors: Yi Xin and Bin He.
Co-corresponding authors: Rui Li and Ying Wu.
Author contributions: Xin Y, He B, Wu Y, and Li R contributed to the research design and manuscript writing; Xin Y and He B jointly contributed to experimental design, data collection, and analysis, playing a crucial role in ensuring the reliability and validity of the research, and collaborated in writing and revising the manuscript, thereby enhancing the overall quality of the manuscript as co-first authors; Xin Y, He B, Wei XH, Yan YL, Huang C, Gao CY, Wang S, and Zhang GM contributed to the data collection; Wu Y and Li R possess expertise in the fields of anesthesiology and nursing, providing crucial professional support and advice for the research, and they serve as leaders and mentors within the research team, playing significant organizational and guidance roles throughout the entire research project as co-corresponding authors. All authors contributed to the article and approved the submitted version.
Supported by the 2024 Shanghai Jiao Tong University School of Medicine Nursing Research Top Priority Project, No. Jyhz2410; and Advanced Anesthesia Specialty Nursing Training Base, No. 2022zkh1jd.
Institutional review board statement: The study was reviewed and approved by the Shanghai Tongren Hospital’s Ethics Committee (approval No. Tong Ren Lun Audit 2024-002-02).
Clinical trial registration statement: The research did not involve any clinical trials or interventional procedures and is an observational cohort study. As such, it does not meet the criteria for clinical trial registration.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: sharing statement: No additional data are available.
Corresponding author: Ying Wu, Chief Nurse, Department of Nursing, Tongren Hospital Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200335, China.
1285042230@qq.com
Received: October 27, 2025
Revised: December 24, 2025
Accepted: February 12, 2026
Published online: June 19, 2026
Processing time: 213 Days and 21.2 Hours
BACKGROUND
Emergence delirium (ED) is a common postoperative complication in older adult patients, posing a significant burden on both patients and medical staff. Despite its prevalence, there is a notable lack of research focused on identifying predictive factors and constructing models for ED in the post-anesthesia care unit. Therefore, developing a risk prediction model for ED in older adult patients is imperative. We anticipate that such a model would demonstrate strong predictive efficacy and be applicable in clinical settings.
AIM
To develop and validate an ED risk-prediction model for early intervention in older adults.
METHODS
This study enrolled 705 older surgical patients (January 2024 to October 2024) for modeling and 115 (November 2024 to December 2024) for validation. Using least absolute shrinkage and selection operator and multivariable logistic regression, we developed a predictive model with an online dynamic nomogram. Internal (10-fold cross-validation) and external validation demonstrated strong discrimination, calibration, and clinical utility.
RESULTS
The incidence of ED in older adult patients postoperatively was found to be 17.16%. Independent risk factors for postoperative ED included preoperative Mini-Mental State Examination score, preoperative albumin level, surgical duration, surgical risk score, number of indwelling catheters, and extubation time (all P < 0.05). The model demonstrated the area under curve (AUC) of 0.924 [95% confidence interval (CI): 0.897-0.951], with the calibration curve closely aligning with the ideal curve. The Hosmer-Lemeshow test yielded χ2 = 7.934, P = 0.541, indicating good clinical utility. Internal validation resulted in an AUC of 0.920 (95%CI: 0.571-0.959), while external validation showed an AUC of 0.931 (95%CI: 0.866-0.997). The calibration curve for the validation cohort closely matched the ideal curve, with the Hosmer-Lemeshow test showing χ2 = 5.772, P = 0.763, further supporting its clinical applicability.
CONCLUSION
The dynamic nomogram accurately predicts ED risk in older adults, aiding early identification and clinical intervention.
Core Tip: In this study, 705 older adult surgical patients were selected as the modeling cohort, while 115 older adult surgical patients from different time periods were chosen for external validation. The results indicated that the incidence of postoperative emergence delirium in older adult patients was 17.16%. Preoperative Mini-Mental State Examination score, preoperative albumin level, surgical risk score, the number of indwelling catheters, and extubation time were identified as independent factors influencing the risk of emergence delirium. The risk nomogram model developed based on these factors demonstrated robust predictive efficacy.
