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Choi H, Shin J, Jung JH, Han K, Choi W, Lee HR, Yoo JE, Yeo Y, Lee H, Shin DW. Tuberculosis and osteoporotic fracture risk: development of individualized fracture risk estimation prediction model using a nationwide cohort study. Front Public Health 2024; 12:1358010. [PMID: 38721534 PMCID: PMC11076769 DOI: 10.3389/fpubh.2024.1358010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/08/2024] [Indexed: 05/15/2024] Open
Abstract
Purpose Tuberculosis (TB) is linked to sustained inflammation even after treatment, and fracture risk is higher in TB survivors than in the general population. However, no individualized fracture risk prediction model exists for TB survivors. We aimed to estimate fracture risk, identify fracture-related factors, and develop an individualized risk prediction model for TB survivors. Methods TB survivors (n = 44,453) between 2010 and 2017 and 1:1 age- and sex-matched controls were enrolled. One year after TB diagnosis, the participants were followed-up until the date of fracture, death, or end of the study period (December 2018). Cox proportional hazard regression analyses were performed to compare the fracture risk between TB survivors and controls and to identify fracture-related factors among TB survivors. Results During median 3.4 (interquartile range, 1.6-5.3) follow-up years, the incident fracture rate was significantly higher in TB survivors than in the matched controls (19.3 vs. 14.6 per 1,000 person-years, p < 0.001). Even after adjusting for potential confounders, TB survivors had a higher risk for all fractures (adjusted hazard ratio 1.27 [95% confidence interval 1.20-1.34]), including hip (1.65 [1.39-1.96]) and vertebral (1.35 [1.25-1.46]) fractures, than matched controls. Fracture-related factors included pulmonary TB, female sex, older age, heavy alcohol consumption, reduced exercise, and a higher Charlson Comorbidity Index (p < 0.05). The individualized fracture risk model showed good discrimination (concordance statistic = 0.678). Conclusion TB survivors have a higher fracture risk than matched controls. An individualized prediction model may help prevent fractures in TB survivors, especially in high-risk groups.
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Affiliation(s)
- Hayoung Choi
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
| | - Jungeun Shin
- International Healthcare Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jin-Hyung Jung
- Department of Biostatistics, College of Medicine, Catholic University of Korea, Seoul, Republic of Korea
| | - Kyungdo Han
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea
| | - Han Rim Lee
- International Healthcare Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Family Medicine and Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jung Eun Yoo
- Department of Family Medicine, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yohwan Yeo
- Department of Family Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Hyun Lee
- Division of Pulmonary Medicine and Allergy, Department of Internal Medicine, Hanyang Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Dong Wook Shin
- Department of Family Medicine, Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Science and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
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