120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
利用常规人口学和生化数据预测髋部骨质疏松：Shao HipOsteoRisk模型
Authors Dai X, Lu W, Xu F , Deng Z, Xiao G, Li Z, Zhang Y, Liu A , Guo W, Huang K, Zhu W, Tan J, Zhou B, Lou C, Wu Q
Received 8 September 2025
Accepted for publication 12 January 2026
Published 20 January 2026 Volume 2026:21 566021
DOI https://doi.org/10.2147/CIA.S566021
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Prof. Dr. Nandu Goswami
Xiangheng Dai,1,* Weiqi Lu,2,* Fuzhou Xu,3 Zongping Deng,1 Guorong Xiao,4 Zeping Li,5 Yilv Zhang,3 Ao Liu,3 Weipeng Guo,6 Kunhua Huang,7 Wengang Zhu,8 Junhao Tan,8 Beidi Zhou,9 Chao Lou,10 Qiang Wu1
1Department of Spinal Surgery, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 2Department of Chinese traditional traumatology, Dongguan Shijie Hospital, Dongguan, Guangdong, People’s Republic of China; 3The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, People’s Republic of China; 4Department of orthopedics, Wengyuan Hospital of Traditional Chinese Medicine, Shaoguan, Guangdong, People’s Republic of China; 5Department of Pain, the Fourth People’s Hospital of Nanhai District, Foshan, Guangdong, People’s Republic of China; 6Department of Information, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 7Department of Laboratory, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 8Department of Osteoarthritis, Yuebei People’s Hospital, Shaoguan, Guangdong, People’s Republic of China; 9Department of Prevention and Health Care, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 10Department of Trauma Surgery and Orthopedics, AGAPLESION EV. Klinikum Schaumburg, Hannover Medical School, Obernkirchen, Germany
*These authors contributed equally to this work
Correspondence: Chao Lou, Email chaolou213@gmail.com Qiang Wu, Email sgwuqiang@sgu.edu.cn
Objective: To develop and externally validate a simple, accessible prediction model for identifying individuals at risk of hip osteoporosis using routine demographic and laboratory data.
Methods: This retrospective study included 7686 adult patients who underwent hip dual-energy X-ray absorptiometry (DXA) at two medical centers in northern Guangdong, China. A total of 4638 patients were used for model development and 3048 for external validation. Predictors were selected using appropriate imputation and regularized regression techniques to ensure stability across datasets. Model performance was evaluated using discrimination, calibration, and clinical utility metrics.
Results: Four routinely available variables—age, sex, body mass index, and the serum albumin-to-alkaline phosphatase ratio—were identified as the key predictors. The final logistic regression model demonstrated strong discrimination, with an area under the curve of 0.9107 in the development cohort and 0.8286 in the external validation cohort. Sensitivity and specificity were both favorable, and calibration showed good agreement between predicted and observed risk across most probability ranges. Decision curve analysis indicated meaningful net clinical benefit across a wide range of threshold probabilities, supporting the model’s potential to improve risk stratification in practice.
Conclusion: We developed and validated a practical predictive model for hip osteoporosis based entirely on information commonly obtained during routine clinical care. Because it requires no specialized testing beyond standard laboratory panels, the model offers a low-cost, scalable screening tool—particularly valuable in settings where DXA access is limited. Its strong performance and ease of application suggest that it may help clinicians identify high-risk patients earlier, guide referral for confirmatory DXA scanning, and support more proactive osteoporosis prevention strategies.
Keywords: osteoporosis, risk assessment, predictive model
1Department of Spinal Surgery, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 2Department of Chinese traditional traumatology, Dongguan Shijie Hospital, Dongguan, Guangdong, People’s Republic of China; 3The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, Guangdong, People’s Republic of China; 4Department of orthopedics, Wengyuan Hospital of Traditional Chinese Medicine, Shaoguan, Guangdong, People’s Republic of China; 5Department of Pain, the Fourth People’s Hospital of Nanhai District, Foshan, Guangdong, People’s Republic of China; 6Department of Information, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 7Department of Laboratory, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 8Department of Osteoarthritis, Yuebei People’s Hospital, Shaoguan, Guangdong, People’s Republic of China; 9Department of Prevention and Health Care, Shaoguan First People’s Hospital, Affiliated Hospital of Shaoguan University, Shaoguan, Guangdong, People’s Republic of China; 10Department of Trauma Surgery and Orthopedics, AGAPLESION EV. Klinikum Schaumburg, Hannover Medical School, Obernkirchen, Germany
*These authors contributed equally to this work
Correspondence: Chao Lou, Email chaolou213@gmail.com Qiang Wu, Email sgwuqiang@sgu.edu.cn
Objective: To develop and externally validate a simple, accessible prediction model for identifying individuals at risk of hip osteoporosis using routine demographic and laboratory data.
Methods: This retrospective study included 7686 adult patients who underwent hip dual-energy X-ray absorptiometry (DXA) at two medical centers in northern Guangdong, China. A total of 4638 patients were used for model development and 3048 for external validation. Predictors were selected using appropriate imputation and regularized regression techniques to ensure stability across datasets. Model performance was evaluated using discrimination, calibration, and clinical utility metrics.
Results: Four routinely available variables—age, sex, body mass index, and the serum albumin-to-alkaline phosphatase ratio—were identified as the key predictors. The final logistic regression model demonstrated strong discrimination, with an area under the curve of 0.9107 in the development cohort and 0.8286 in the external validation cohort. Sensitivity and specificity were both favorable, and calibration showed good agreement between predicted and observed risk across most probability ranges. Decision curve analysis indicated meaningful net clinical benefit across a wide range of threshold probabilities, supporting the model’s potential to improve risk stratification in practice.
Conclusion: We developed and validated a practical predictive model for hip osteoporosis based entirely on information commonly obtained during routine clinical care. Because it requires no specialized testing beyond standard laboratory panels, the model offers a low-cost, scalable screening tool—particularly valuable in settings where DXA access is limited. Its strong performance and ease of application suggest that it may help clinicians identify high-risk patients earlier, guide referral for confirmatory DXA scanning, and support more proactive osteoporosis prevention strategies.
Keywords: osteoporosis, risk assessment, predictive model