MO546: Association Between T Scores, FRAX, Frailty, Falls and Fractures in Patients Undergoing Maintenance Hemodialysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».