T Scores, FRAX, Frailty Phenotype, Falls, and Its Relationship to Fractures in Patients on Maintenance Hemodialysis
Notice bibliographique
Résumé
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 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.
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 | 0,001 | 0,003 |
| 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 ».