Fracture risk assessment in the presence of competing risk of death.
- Publisher:
- SPRINGER LONDON LTD
- Publication Type:
- Journal Article
- Citation:
- Osteoporos Int, 2024, 35, (11), pp. 1989-1998
- Issue Date:
- 2024-11
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Full metadata record
| Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tran, TS | |
| dc.contributor.author | Bliuc, D | |
| dc.contributor.author | Blank, RD | |
| dc.contributor.author | Center, JR | |
| dc.contributor.author | Nguyen, TV | |
| dc.date.accessioned | 2024-11-16T21:24:56Z | |
| dc.date.available | 2024-08-02 | |
| dc.date.available | 2024-11-16T21:24:56Z | |
| dc.date.issued | 2024-11 | |
| dc.identifier.citation | Osteoporos Int, 2024, 35, (11), pp. 1989-1998 | |
| dc.identifier.issn | 0937-941X | |
| dc.identifier.issn | 1433-2965 | |
| dc.identifier.uri | http://hdl.handle.net/10453/181932 | |
| dc.description.abstract | PURPOSE: To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. METHODS: We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. RESULTS: During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. CONCLUSION: Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly. | |
| dc.format | Print-Electronic | |
| dc.language | eng | |
| dc.publisher | SPRINGER LONDON LTD | |
| dc.relation | http://purl.org/au-research/grants/nhmrc/1070187 | |
| dc.relation | http://purl.org/au-research/grants/nhmrc/1195305 | |
| dc.relation.ispartof | Osteoporos Int | |
| dc.relation.isbasedon | 10.1007/s00198-024-07224-z | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | 0903 Biomedical Engineering, 1103 Clinical Sciences, 1117 Public Health and Health Services | |
| dc.subject.classification | Endocrinology & Metabolism | |
| dc.subject.classification | 3202 Clinical sciences | |
| dc.subject.classification | 4202 Epidemiology | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Osteoporotic Fractures | |
| dc.subject.mesh | Risk Assessment | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | Models, Statistical | |
| dc.subject.mesh | Follow-Up Studies | |
| dc.subject.mesh | Cause of Death | |
| dc.subject.mesh | Proportional Hazards Models | |
| dc.subject.mesh | Osteoporosis | |
| dc.subject.mesh | Middle Aged | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Osteoporosis | |
| dc.subject.mesh | Cause of Death | |
| dc.subject.mesh | Models, Statistical | |
| dc.subject.mesh | Proportional Hazards Models | |
| dc.subject.mesh | Risk Assessment | |
| dc.subject.mesh | Follow-Up Studies | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | Middle Aged | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | Osteoporotic Fractures | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Osteoporotic Fractures | |
| dc.subject.mesh | Risk Assessment | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | Models, Statistical | |
| dc.subject.mesh | Follow-Up Studies | |
| dc.subject.mesh | Cause of Death | |
| dc.subject.mesh | Proportional Hazards Models | |
| dc.subject.mesh | Osteoporosis | |
| dc.subject.mesh | Middle Aged | |
| dc.title | Fracture risk assessment in the presence of competing risk of death. | |
| dc.type | Journal Article | |
| utslib.citation.volume | 35 | |
| utslib.location.activity | England | |
| utslib.for | 0903 Biomedical Engineering | |
| utslib.for | 1103 Clinical Sciences | |
| utslib.for | 1117 Public Health and Health Services | |
| pubs.organisational-group | University of Technology Sydney | |
| pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology | |
| pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering | |
| pubs.organisational-group | University of Technology Sydney/UTS Groups | |
| pubs.organisational-group | University of Technology Sydney/UTS Groups/UTS Ageing Research Collaborative (UARC) | |
| pubs.organisational-group | University of Technology Sydney/UTS Groups/Centre for Health Technologies (CHT) | |
| utslib.copyright.status | open_access | * |
| dc.rights.license | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.date.updated | 2024-11-16T21:24:55Z | |
| pubs.issue | 11 | |
| pubs.publication-status | Published | |
| pubs.volume | 35 | |
| utslib.citation.issue | 11 |
Abstract:
PURPOSE: To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. METHODS: We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. RESULTS: During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. CONCLUSION: Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.
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