MO546: Association Between T Scores, FRAX, Frailty, Falls and Fractures in Patients Undergoing Maintenance Hemodialysis
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Despite the negative impact of fracture in hemodialysis (HD) patients, optimal risk assessment tools in this population are not well explored. Frailty (a syndrome of diminished capacity to respond to health stressors) and falls—known risk factors for fracture—are common in HD patients. While the relationship between T scores in relation to fractures in these patients is recognized, there is a paucity of data to the additional contributions of fracture risk assessment tool (FRAX), frailty and falls in relation to fractures. The clinical, societal and economic impact of fractures necessitates an active response from the HD programs. Therefore, we intended to evaluate the clinical utility of adding these factors to T scores at the femoral neck to determine whether it enhances fracture discrimination in HD patients. METHOD We conducted a cross-sectional study on 131 adult patients receiving HD at two dialysis units in Regina, Saskatchewan, Canada (January 2017–December 2018). After undergoing frailty assessments, patients were referred for dual-energy X-ray absorptiometry (DXA) scans and FRAX questionnaires. They were additionally sent for lumbar X-rays and contacted for a history of falls. The presence of fracture (hips, femur, pelvis, knee, foot, leg, toe, shoulder, elbow, ankle, arm, wrist, spine and lumbar) was documented based on the review of medical charts, self-recall and additionally vertebral fractures were identified by an X-ray. Bone mineral density (BMD) was measured by DXA. FRAX score (the 10-year probability of hip and major osteoporotic fracture) was calculated using an online algorithm based on 11 clinical risk factors with and without the inclusion of BMD. Frailty was assessed using the FRIED criteria (Figure 1). Patients were enquired about the history and frequency of falls. Association between the BMD-T score, FRAX score, frailty status, falls, with fracture were examined with sequential multivariable logistic regression models. The area under the receiver operating characteristic curve analysis was conducted for each model to assess its discrimination ability for fracture outcome (α = 0.05). RESULTS A total of 109 HD patients were included in the data analysis. The composite of fracture occurred in 38% of patients. About 60% were identified as frail, and 29% had at least one fall in the last year. Low T score and FRAX were both independent risk factors in patients on HD therapy. On multivariate analysis, each lower standard deviation in T score was associated with 48% higher odds of fracture [odds ratio (OR) = 1.48; 95% confidence interval (95% CI) 1.20–1.68, P = 0.005]. With the inclusion of FRAX score for hip fracture, the OR for fracture remained significant at 1.38 (OR = 1.38, 95% CI 1.04–1.63, P = 0.04). The addition of frailty status and history of falls did not further improve the model. Model 2 showed better discrimination ability and goodness-of-fit for fracture compared to Model 1 (P = 0.004) (Table). CONCLUSION This is the first study to our knowledge that looked at the independent contribution of frailty and falls in addition to T scores and FRAX in assessing for fractures in patients on HD. Our study showed both BMD measurements by DXA scans 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. Results of this study should lead to prospective studies with larger sample sizes prior to DXA scans, FRAX and falls assessment being recommended as standards of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".