Retrospective Cohort Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 16, 2023; 11(20): 4824-4832
Published online Jul 16, 2023. doi: 10.12998/wjcc.v11.i20.4824
Development and validation of a predictive model for spinal fracture risk in osteoporosis patients
Xu-Miao Lin, Zhi-Cai Shi
Xu-Miao Lin, Zhi-Cai Shi, Department of Orthopedics, Changhai Hospital, Shanghai 200433, China
Author contributions: Lin XM proposed the concept of this study; Shi ZC has made contributions to data collection; Shi ZC made contributions to formal analysis; Lin XM and Shi ZC participated in the investigation; Lin XM and Shi ZC contributed to these methods; Shi ZC guided research; Lin XM validated this study; Lin XM contributed to the visualization of this study; Lin XM and Shi ZC reviewed and edited the manuscript.
Institutional review board statement: This study has been approved by the Ethics Committee of the First Affiliated Hospital of the Naval Medical University.
Informed consent statement: Informed consent was unnecessary due to the anonymizing of patient records and the study’s retrospective nature.
Conflict-of-interest statement: The authors declare no conflicts of interest.
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: Zhi-Cai Shi, MD, Attending Doctor, Department of Orthopedics, Changhai Hospital, No. 168 Changhai Road, Shanghai 200433, China. szhicai@126.com
Received: June 6, 2023
Peer-review started: June 6, 2023
First decision: June 15, 2023
Revised: June 16, 2023
Accepted: June 19, 2023
Article in press: June 19, 2023
Published online: July 16, 2023
Processing time: 35 Days and 21 Hours
Abstract
BACKGROUND

Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine, increasing the risk of fractures. Given its high incidence, especially among older populations, it is critical to have accurate and effective predictive models for fracture risk. Traditionally, clinicians have relied on a combination of factors such as demographics, clinical attributes, and radiological characteristics to predict fracture risk in these patients. However, these models often lack precision and fail to include all potential risk factors. There is a need for a more comprehensive, statistically robust prediction model that can better identify high-risk individuals for early intervention.

AIM

To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.

METHODS

The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined. The patients were selected according to strict criteria and categorized into two groups: Those with fractures (n = 40) and those without fractures (n = 40). Demographics, clinical attributes, biochemical indicators, bone mineral density (BMD), and radiological characteristics were collected and compared. A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model’s performance.

RESULTS

Factors significantly associated with fracture risk included age, sex, body mass index (BMI), smoking history, BMD, vertebral trabecular alterations, and prior vertebral fractures. The final risk-prediction model was developed using the formula: (logit [P] = -3.75 + 0.04 × age - 1.15 × sex + 0.02 × BMI + 0.83 × smoking history + 2.25 × BMD - 1.12 × vertebral trabecular alterations + 1.83 × previous vertebral fractures). The AUROC of the model was 0.93 (95%CI: 0.88-0.96, P < 0.001), indicating strong discriminatory capabilities.

CONCLUSION

The fracture risk-prediction model, utilizing accessible clinical, biochemical, and radiological information, offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis. The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.

Keywords: Spinal osteoporosis; Fracture risk prediction; Bone mineral density; Vertebral trabecular alterations; Previous vertebral fractures

Core Tip: A fracture risk-prediction model was created and validated using the medical records of 80 patients with spinal osteoporosis. The model utilized accessible clinical, biochemical, and radiological information to accurately evaluate the patient's fracture risk. Factors significantly associated with fracture risk included age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures. The final model had strong discriminatory capabilities, as evidenced by an area under the receiver operating characteristic curve of 0.93. This model has potential in identifying high-risk individuals for early intervention and guiding appropriate preventive actions to reduce the impact of osteoporosis-related fractures.