Risk prediction models for re-fractures following hip fracture in older adults: A methodological evaluation

doi:10.5312/wjo.v17.i5.116449

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World Journal of Orthopedics

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2218-5836

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Meta-Analysis

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.

World J Orthop. May 18, 2026; 17(5): 116449
Published online May 18, 2026. doi: 10.5312/wjo.v17.i5.116449

Risk prediction models for re-fractures following hip fracture in older adults: A methodological evaluation

Min Qian, Yi-Xiao Chen, Jia-Jia Liu, Hua-Jie Yang, Guo-Qing Li

Min Qian, Jia-Jia Liu, Hua-Jie Yang, Department of VIP/International Medical Service, National Center for Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China

Yi-Xiao Chen, Department of Traumatic Orthopaedics, National Center for Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China

Guo-Qing Li, Department of Pediatric Orthopaedics, National Center for Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China

Co-first authors: Min Qian and Yi-Xiao Chen.

Author contributions: Qian M and Chen YX were responsible for writing original draft, methodology, and funding acquisition as co-first authors; Liu JJ was responsible for methodology; Yang HJ was responsible for methodology; Li GQ was responsible for writing review and editing, supervision, and funding acquisition; all of the authors read and approved the final version of the manuscript to be published.

AI contribution statement: We did use AI tools for language polishing and writing assistance (e.g., Grammarly, ChatGPT, and Deepseek) to improve the readability and English expression of the manuscript. The entire main text (Abstract, Introduction, Methods, Results, Discussion, Conclusion) was drafted and critically revised by the authors themselves. AI was not used to generate the core scientific content, data, or conclusions. No AI tool was used in study design, data interpretation, figure/image generation, or statistical analysis.

Supported by National Natural Science Foundation of China, No. 82402789; Beijing Jishuitan Hospital Youcai Plan, No. KYYC202402; and Beijing JST Research Funding, No. HL202402.

Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.

PRISMA 2020 Checklist statement: The authors have read the PRISMA 2020 Checklist, and the manuscript was prepared and revised according to the PRISMA 2020 Checklist.

Corresponding author: Guo-Qing Li, MD, Principal Investigator, Senior Scientist, Department of Pediatric Orthopaedics, National Center for Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, No. 31 Xinjiekou East Street, Xicheng District, Beijing 100035, China. guoqingli@pku.edu.cn

Received: November 13, 2025
Revised: November 23, 2025
Accepted: February 27, 2026
Published online: May 18, 2026
Processing time: 188 Days and 0.7 Hours

Abstract

BACKGROUND

Hip fracture in elderly patients is often termed “the last fracture in life“. The incidence of hip re-fractures is high, but prognosis is poor, significantly impairing patients’ quality of life and imposing a substantial burden on healthcare systems. A reliable prediction model for re-fractures could play a crucial role in guiding preventive strategies.

AIM

To conduct a critical appraisal of existing prediction models for re-fractures in hip fracture patients.

METHODS

A systematic search was conducted across five databases, PubMed, EMBASE, the Cochrane Library, Web of Science, and the China National Knowledge Infrastructure, from inception to January 1, 2025. Eligible studies included those that developed prediction models for postoperative re-fractures following hip fractures. The Prediction Model Risk of Bias Assessment Tool was employed to evaluate the risk of bias and clinical applicability. A narrative synthesis summarised the characteristics, methodological quality, and predictive performance of the identified studies. Additionally, a meta-analysis was performed to quantitatively assess the performance of prediction models.

RESULTS

Of the 6056 studies retrieved, 8 studies with 28 predictive models were ultimately identified. Internal and external validation was performed for five (62.5%, internal), two (25.0%, external), and one (12.5%, internal and external) models. The number of predictors per model ranged from four to nineteen. The most frequently included predictors were age, rehabilitation exercise, osteoporosis, heart disease, and Alzheimer's disease. The models demonstrated area under the curve values of 0.69-0.98 in internal validation and 0.76-0.98 in external validation. Pooled analysis of the area under the curves yielded values of 0.970 (95%CI: 0.960-0.980) and 0.932 (95%CI: 0.907-0.959) for the model development and validation, respectively. All included models had a high risk of bias, while only two (25.0%) showed low concerns regarding applicability.

CONCLUSION

Current risk prediction models for postoperative re-fracture after hip fractures surgery lack robust validation and comprehensive evaluation. Future studies should prioritise refining model development, improving generalizability, and assessing clinical utility. Collaborative initiatives involving researchers, clinicians, and policymakers are crucial to transforming these models into effective tools for mitigating the burden of re-fractures in elderly populations.

Keywords: Older adults; Prediction model; Hip re-fracture; Meta-analysis; Systematic review

Core Tip: This study systematically evaluates existing models designed to predict re-fractures after hip fracture in older adults. By critically examining their development, validation, and methodological rigor, the review highlights substantial limitations in bias control, generalizability, and clinical applicability. Despite showing moderate to high predictive performance, most models lack robust external validation. The findings underscore the urgent need for improved model design and collaborative efforts to create reliable, clinically useful tools that can better guide prevention and reduce the burden of re-fractures in elderly populations.