T Scores, FRAX, Frailty Phenotype, Falls, and Its Relationship to Fractures in Patients on Maintenance Hemodialysis
Bibliographic record
Abstract
Background: Despite the magnitude of fracture and the consequences in patients receiving hemodialysis, optimal risk assessment tools in this population are not well explored. Frailty and falls—known risk factors for fracture in chronic kidney disease (CKD) and non-CKD populations—are common in patients receiving hemodialysis (HD) therapy. While the relationship between T scores in relation to fractures in patients receiving HD is recognized, there is a paucity of data to the additional contributions of fracture assessment tool (FRAX), frailty status, and falls in its relationship with fracture. Objectives: To evaluate the clinical utility of adding FRAX, frailty status, and falls to T scores at the femoral neck to determine whether it enhances fracture discrimination in patients on maintenance HD. Design: A cross-sectional observational study. Setting: Two main dialysis units in Regina, Saskatchewan, Canada. Patients: A total of 109 patients on maintenance HD at two dialysis units from January 1, 2017, to December 31, 2018, were included in the study. Measurements: Fracture (the main outcome) was documented based on the review of medical charts, self-recall, and additionally vertebral fractures were identified by an x-ray. Areal bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry (DXA). FRAX score was calculated using an online algorithm based on 11 clinical risk factors. We calculated the FRAX score for hip fracture and major osteoprotoic fracture with and without the inclusion of BMD. Frailty was assessed using the Fried criteria, which included assessments of unintentional weight loss, weakness (handgrip strength), slowness (walking speed), and questionnaires for physical activity and self-perceived exhaustion. Patients were enquired about the history and frequency of falls. Methods: A total of 131 patients underwent frailty assessments at the two dialysis units during the dialysis treatment. Following frailty assessments, they were referred for DXA scans and upon receipt of the results undertook FRAX questionnaires. They were additionally sent for lumbar x-rays and contacted for a history of falls. Association between the BMD-T score, FRAX, frailty status, falls, with fracture were examined with sequential multivariable logistic regression models. Differences were considered statistically significant at P values <.05. Results: A total of 109 patients were included in the data analysis. The composite of fracture occurred in 37.6% of patients. About 59.3% were identified as frail, and 29% of the participants had at least one fall in the last year. On multivariate regression analysis, each lower standard deviation (SD) in femoral neck T score was associated with 48% higher odds of fracture (odds ratio [OR] = 1.48; 95% confidence interval [CI] 1.20-1.68, P = .005). With the inclusion for FRAX scores (hip), the OR for fracture remained significant at 1.38 (OR = 1.38, 95% CI 1.04-1.63, P = .043). The addition of frailty status and history of falls did not further improve the model. Low T score and FRAX were both independent risk factors in patients on HD therapy. Limitations: This is a single-center study with a small sample size which limits the generalizability of the findings. Due to the cross-sectional study, associations identified may be difficult to interpret. Conclusions: Both BMD measurements by DXA and FRAX are useful tools to assess fracture in patients receiving HD. The addition of frailty status and history of falls is not associated with fractures in this population. Larger prospective studies are needed to determine whether the inclusion of frailty and falls to the conventional models will improve fracture assessment in the population receiving HD. Trial Registration: The study was not registered on a publicly accessible registry as it did not involve health care intervention on human participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".