Li L, Sheng WW, Song LJ, Cheng S, Cui EG, Zhang YB, Yu XZ, Liu YL. Developing a nomogram for postoperative delirium in elderly patients with hip fractures. World J Psychiatry 2025; 15(3): 102117 [DOI: 10.5498/wjp.v15.i3.102117]
Corresponding Author of This Article
Yan-Li Liu, Assistant Professor, PhD, Department of Health Care, Dongying People’s Hospital, No. 317 Dongcheng South 1st Road, Dongying 257091, Shandong Province, China. dyliuyanli@163.com
Research Domain of This Article
Orthopedics
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
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/
Liang Li, Shuai Cheng, Department of Orthopaedics and Traumatology, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Wei-Wei Sheng, Li-Juan Song, Yan-Li Liu, Department of Health Care, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
En-Gang Cui, Department of Medical Imaging, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Yong-Bing Zhang, Department of Joint Surgery, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Xue-Zhong Yu, Department of Spine Surgery, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Co-first authors: Liang Li and Wei-Wei Sheng.
Author contributions: Li L and Sheng WW contributed equally to this work and are co-first authors; Li L, Sheng WW, and Liu YL designed the study, collected and analyzed data, and wrote the manuscript; Li L, Sheng WW, Song LJ, Cheng S, Cui EG, Zhang YB, Yu XZ, and Liu YL participated in the study’s conception and data collection; and all authors read and approved the final version.
Supported by Wang Zhengguo Foundation for Traumatic Medicine “Sequential Medical Research Special Foundation”, No 2024-XG-M05.
Institutional review board statement: This study was approved by the Ethic Committee of Dongying People’s Hospital.
Informed consent statement: The written informed consent was waived owing to the retrospective and deidentified nature of this study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for 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: Yan-Li Liu, Assistant Professor, PhD, Department of Health Care, Dongying People’s Hospital, No. 317 Dongcheng South 1st Road, Dongying 257091, Shandong Province, China. dyliuyanli@163.com
Received: November 21, 2024 Revised: December 30, 2024 Accepted: January 21, 2025 Published online: March 19, 2025 Processing time: 96 Days and 20.1 Hours
Abstract
BACKGROUND
Postoperative delirium (POD) is a prevalent complication, particularly in elderly patients with hip fractures (HFs). It significantly affects recovery, length of hospital stay, healthcare costs, and long-term outcomes. Existing studies have investigated risk factors for POD, but most are limited by single-factor analyses or small sample sizes. This study systematically determines independent risk factors using large-scale data and machine learning techniques and develops a validated nomogram model to support early prediction and management of POD.
AIM
To investigate POD incidence in elderly patients with HF and the independent risk factors, according to which a nomogram prediction model was developed and validated.
METHODS
This retrospective study included elderly patients with HF who were surgically treated in Dongying People’s Hospital from April 2018 to April 2022. The endpoint event includes POD. They were categorized into the modeling and validation cohorts in a 7:3 ratio by randomization. Both cohorts were further classified into the delirium and normal (non-delirium) groups according to the presence or absence of the endpoint event. The incidence of POD was calculated, and logistic multivariate analysis was conducted to determine the independent risk factors. The calibration curve and the Hosmer-Lemeshow test as well as the net benefit threshold probability interval by the decision curve were utilized to statistically validate the accuracy of the nomogram prediction model, developed according to each factor’s influence intensity.
RESULTS
This study included 532 elderly patients with HF, with an overall POD incidence of 14.85%. The comparison of baseline data with perioperative indicators revealed statistical differences in age (P < 0.001), number of comorbidities (P = 0.042), American Society of Anesthesiologists grading (P = 0.004), preoperative red blood cell (RBC) count (P < 0.001), preoperative albumin (P < 0.001), preoperative hemoglobin (P < 0.001), preoperative platelet count (P < 0.001), intraoperative blood loss (P < 0.001), RBC transfusion of ≥ 2 units (P = 0.001), and postoperative intensive care unit care (P < 0.001) between the delirium and non-delirium groups. The participants were randomized to a training group (n = 372) and a validation group (n = 160). A score-risk nomogram prediction model was developed after screening key POD features using Lasso regression, support vector machine, and the random forest method. The nomogram showed excellent discriminatory capacity with area under the curve of 0.833 [95% confidence interval (CI) interval: 0.774-0.888] in the training group and 0.850 (95%CI: 0.718-0.982) in the validation group. Calibration curves demonstrated good agreement between predicted and actual probabilities, and decision curve analysis confirmed clinical net benefits within risk thresholds of 0%-30% and 0%-36%, respectively. The model has strong accuracy and clinical utility for predicting the risk of POD.
CONCLUSION
This study reveals cognitive impairment history, American Society of Anesthesiologists grade of > 2, RBC transfusion of ≥ 2 units, postoperative intensive care unit care, and preoperative hemoglobin level as independent risk factors for POD in elderly patients with HF. The developed nomogram model demonstrates excellent accuracy and stability in predicting the risk of POD, which is recommended to be applied in clinical practice to optimize postoperative management and reduce delirium incidence.
Core Tip: This study integrates advanced machine learning techniques, including Lasso regression, support vector machine, and random forest, to determine independent risk factors for postoperative delirium. A nomogram prediction model was developed and demonstrated high accuracy and stability in both training and validation cohorts. The decision curve analysis confirmed its clinical use within a risk threshold range of 8%-35%. This tool provides valuable guidance for the early determination of patients at high-risk and personalized postoperative management to reduce postoperative delirium incidence.
