Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455 [DOI: 10.5498/wjp.119455]
Corresponding Author of This Article
Chen-Xiao Zheng, PhD, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, No. 3 Kangxin Road, West District, Zhongshan 528400, Guangdong Province, China. cuokoo1973@163.com
Research Domain of This Article
Psychology
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research-article
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Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455 [DOI: 10.5498/wjp.119455]
Fei-Fan Luan, Jia-Yi Chen, Yu-Zhong Zheng, Min-Hua Hu, Chen-Xiao Zheng, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, Zhongshan 528400, Guangdong Province, China
Fei-Fan Luan, Feng Huang, The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong Province, China
Co-corresponding authors: Feng Huang and Chen-Xiao Zheng.
Author contributions: Luan FF designed the research and wrote the first manuscript; Luan FF, Chen JY, and Zheng YZ contributed to conceiving the research and analyzing data; Luan FF and Hu MH conducted the analysis; Huang F and Zheng CX provided guidance for the research; they contributed equally to this manuscript and are co-corresponding authors; all authors reviewed and approved the final manuscript.
AI contribution statement: The authors declare that no AI tools were used in the development or writing of this manuscript and take full responsibility for its integrity, accuracy, and originality.
Institutional review board statement: This study was approved by the Ethics Committee of Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, No. 2026ZSZY-LL-KY-033.
Informed consent statement: The requirement for patients’ informed consent for this study was waived due to its retrospective nature.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Corresponding author: Chen-Xiao Zheng, PhD, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, No. 3 Kangxin Road, West District, Zhongshan 528400, Guangdong Province, China. cuokoo1973@163.com
Received: April 17, 2026 Revised: May 22, 2026 Accepted: June 15, 2026 Published online: September 19, 2026 Processing time: 128 Days and 20.8 Hours
Abstract
BACKGROUND
Traumatic long bone fractures with segmental bone defects represent a major challenge in orthopedics. Nonunion after bone grafting can result in persistent pain, dysfunction, and repeated operations. Currently, traditional clinical index-based predictive models show limited discriminative power and do not systematically integrate psychosocial factors into risk assessment.
AIM
To develop and validate a machine learning model integrating clinical and psychosocial factors to predict the risk of nonunion in patients with traumatic bone defects after bone transport.
METHODS
In this study, the development cohort included patients (n = 145) treated between January 2016 and November 2021, while an independent external validation cohort included patients (n = 53) treated between February 2022 and December 2024. Demographic data, injury- and treatment-related variables, and psychosocial scale scores (9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, and Multidimensional Scale of Perceived Social Support) were collected. The Extreme Gradient Boosting algorithm was used to construct a traditional clinical indicator-based model and an integrated model integrating clinical and psychosocial factors. Model performance was evaluated through internal cross-validation and independent external validation using the area under the receiver operating characteristic curve, F1 score, and decision curve analysis.
RESULTS
During external validation, the integrated model demonstrated superior discriminative performance compared with the traditional clinical model (area under the receiver operating characteristic curve: 0.888 vs 0.823; P < 0.05). SHapley Additive exPlanations analysis showed that anxiety and depression contributed substantially to integrated model predictions. Decision curve analysis further demonstrated that the integrated model provided greater clinical net benefit across a broader decision-threshold range (20%-80%).
CONCLUSION
The machine learning model integrating clinical and psychosocial factors demonstrated superior discriminative performance and potential clinical applicability compared with models based solely on clinical indicators in predicting post-bone transport nonunion risk. Psychosocial factors constitute an important component of nonunion risk prediction.
Core Tip: In summary, the integrated model developed in this study demonstrates favorable discriminative ability (operating characteristic curve = 0.888 in external validation) and potential clinical utility for nonunion risk prediction, although further validation is needed. Importantly, our findings highlight the significant contribution of psychosocial factors - particularly anxiety, depression, and social support - to bone healing. These results provide strong evidence for promoting the deep integration of the “bio‑psycho‑social” medical model into routine orthopedic clinical practice, thereby facilitating more individualized and holistic patient management.
