Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM)

Möller, Sören; Skjødt, Michael Kriegbaum; Yan, Lin; Abrahamsen, Bo; Lix, Lisa M.; McCloskey, Eugène; Johansson, Helena; Harvey, Nicholas C.; Kanis, John А.; Rubin, Katrine Hass; Leslie, William D.

doi:10.1007/s00198-021-06165-1

Cited by 11 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clearly, there may be problems in applying these findings to other populations due to lack of generalisability of both the patient population and the available variables, and questions arise as to how these approaches can be implemented in other countries. The Danish FREM algorithm, for example, significantly overestimated hip fracture risk when applied to an independent clinical population from Manitoba, Canada, indicating the need for recalibration [ 32 ]. However, in health care systems where such rich data and machine learning technologies are available, this approach may indeed be a practicable future direction of travel, and could help with the identification of patients at very high risk of fracture, perhaps as a first population level healthcare provider “screen” for those who might warrant further assessment.…”

Section: Approaches To Risk Stratificationmentioning

confidence: 99%

Management of patients at very high risk of osteoporotic fractures through sequential treatments

et al. 2022

Self Cite

View full text Add to dashboard Cite

Osteoporosis care has evolved markedly over the last 50 years, such that there are now an established clinical definition, validated methods of fracture risk assessment and a range of effective pharmacological agents. Currently, bone-forming (anabolic) agents, in many countries, are used in those patients who have continued to lose bone mineral density (BMD), patients with multiple subsequent fractures or those who have fractured despite treatment with antiresorptive agents. However, head-to-head data suggest that anabolic agents have greater rapidity and efficacy for fracture risk reduction than do antiresorptive therapies. The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) convened an expert working group to discuss the tools available to identify patients at high risk of fracture, review the evidence for the use of anabolic agents as the initial intervention in patients at highest risk of fracture and consider the sequence of therapy following their use. This position paper sets out the findings of the group and the consequent recommendations. The key conclusion is that the current evidence base supports an “anabolic first” approach in patients found to be at very high risk of fracture, followed by maintenance therapy using an antiresorptive agent, and with the subsequent need for antiosteoporosis therapy addressed over a lifetime horizon.

show abstract

Section: Approaches To Risk Stratificationmentioning

confidence: 99%

Management of patients at very high risk of osteoporotic fractures through sequential treatments

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main strength of the planned study is building on top of an existing tool, which has already been validated in multiple settings [ 13 , 14 ], hence ensuring that the resulting enhanced version of FREM will potentially improve on top of an already applicable tool. The enhanced FREM has the potential to be integrated into the primary health care system as a decision support tool to optimize the identification of individuals at high risk of MOF.…”

Section: Discussionmentioning

confidence: 99%

“…A validation study of FREM was recently performed in an updated extraction of Danish registry data which still proved good accuracy of FREM in prediction of MOF and HF [ 13 ]. Additionally, FREM has been externally validated in Canadian hospitalization and physician claims data proving significant fracture risk stratification [ 14 ]. FREM has the potential to be integrated into the primary health care system and can—without any manual data entry required by the general practitioners—provide a single easy-to-interpret estimate for each patients’ risk of MOF and HF.…”

Section: Introductionmentioning

confidence: 99%

An enhanced version of FREM (Fracture Risk Evaluation Model) using national administrative health data: analysis protocol for development and validation of a multivariable prediction model

Kristensen,

Clausen,

Skjødt

et al. 2023

Diagn Progn Res

Self Cite

View full text Add to dashboard Cite

Background Osteoporosis poses a growing healthcare challenge owing to its rising prevalence and a significant treatment gap, as patients are widely underdiagnosed and consequently undertreated, leaving them at high risk of osteoporotic fracture. Several tools aim to improve case-finding in osteoporosis. One such tool is the Fracture Risk Evaluation Model (FREM), which in contrast to other tools focuses on imminent fracture risk and holds potential for automation as it relies solely on data that is routinely collected via the Danish healthcare registers. The present article is an analysis protocol for a prediction model that is to be used as a modified version of FREM, with the intention of improving the identification of subjects at high imminent risk of fracture by including pharmacological exposures and using more advanced statistical methods compared to the original FREM. Its main purposes are to document and motivate various aspects and choices of data management and statistical analyses. Methods The model will be developed by employing logistic regression with grouped LASSO regularization as the primary statistical approach and gradient-boosted classification trees as a secondary statistical modality. Hyperparameter choices as well as computational considerations on these two approaches are investigated by an unsupervised data review (i.e., blinded to the outcome), which also investigates and handles multicollinarity among the included exposures. Further, we present an unsupervised review of the data and testing of analysis code with respect to speed and robustness on a remote analysis environment. The data review and code tests are used to adjust the analysis plans in a blinded manner, so as not to increase the risk of overfitting in the proposed methods. Discussion This protocol specifies the planned tool development to ensure transparency in the modeling approach, hence improving the validity of the enhanced tool to be developed. Through an unsupervised data review, it is further documented that the planned statistical approaches are feasible and compatible with the data employed.

show abstract

Epidemiology and reporting of osteoporotic vertebral fractures in patients with long-term hospital records based on routine clinical CT imaging

et al. 2021

View full text Add to dashboard Cite

Osteoporotic vertebral fractures signify an increased risk of future fractures and mortality and can manifest the diagnosis of osteoporosis. We investigated the prevalence of vertebral fractures in routine CT of patients with long-term hospital records. Three out of ten patients showed osteoporotic vertebral fractures (VFs) corresponding to the highest rates reported in European population-based studies. Introduction VFs are a common manifestation of osteoporosis, which influences future fracture risk. Their epidemiology has been investigated in population-based studies. However, few studies report the prevalence of osteoporotic VF in patients seen in clinical routine and include all common fracture levels of the thoracolumbar spine. The purpose of this study was to investigate the prevalence of osteoporotic VF in patients with CT scans and long-term hospital records and identify clinical factors associated with prevalent VFs. Methods All patients aged 45 years and older with a CT scan and prior hospital record of at least 5 years that were seen in the study period between September 2008 and May 2017 were reviewed. Imaging requirements were a CT scan with sagittal reformations including at least T6-L4. Patients with multiple myeloma were excluded. Fracture reading was performed using the Genant semi-quantitative method. Medical notes were reviewed for established diagnoses of osteoporosis and clinical information. Clinical factors (e.g. drug intake, chemotherapy, and mobility level) associated with prevalent VF were identified in logistic regression. ResultsThe study population consisted of 718 patients (228 women and 490 men; mean age 69.3 ± 10.1 years) with mainly cancer staging and angiography CT imaging. The overall prevalence of VFs was 30.5%, with non-significantly more men showing a fracture (32.5%) compared to women (26.3%; p > 0.05). Intake of metamizole for ≥ 3 months was significantly associated with a prevalent VF. Medical records did not include information about bone health in 90% of all patients. CT reports did mention a VF in only 24.7% of patients with a prevalent VF on CT review. Conclusion Approximately 30% of elderly patients with CT imaging and long-term hospital records showed VFs. Only onequarter of these patients had VFs mentioned in CT reports. Osteoporosis management could be improved by consequent reporting of VFs in CT, opportunistic bone density measurements, and early involvement of fracture liaison services.

show abstract

Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM)

Cited by 11 publications

References 43 publications

Management of patients at very high risk of osteoporotic fractures through sequential treatments

Management of patients at very high risk of osteoporotic fractures through sequential treatments

An enhanced version of FREM (Fracture Risk Evaluation Model) using national administrative health data: analysis protocol for development and validation of a multivariable prediction model

Epidemiology and reporting of osteoporotic vertebral fractures in patients with long-term hospital records based on routine clinical CT imaging

Contact Info

Product

Resources

About