Sarcopenia prevalence and relationship to frailty by measurement method in patients with adult spinal deformity
Notice bibliographique
Résumé
OBJECTIVE: There is no consensus on how to diagnose sarcopenia in patients with spine disorders, limiting one's understanding of the relationship among sarcopenia, frailty, and surgical outcomes. The authors characterized the baseline prevalence of sarcopenia in patients with adult spinal deformity (ASD) according to previously established methods. They then examined the intersection between sarcopenia and frailty. METHODS: This is a retrospective cross-sectional study of preoperative patients with ASD at a single tertiary care center. Muscle function was assessed via hand grip strength, gait speed, and the Timed Up and Go (TUG) test. Bioelectrical impedance analysis was used to determine the skeletal muscle index. Muscle imaging included both CT to determine the psoas muscle index and MRI to assess myosteatosis. Diagnostic thresholds for sarcopenia were taken from the Sarcopenia Definitions and Outcomes Consortium (SDOC) and European Working Group on Sarcopenia in Older People 2 (EWGSOP2) consensus guidelines. Frailty was assessed using the Edmonton Frail Scale (EFS) and the adult spinal deformity frailty index (ASD-FI). Fisher's exact test, Spearman's rank correlation, and UpSet plot analyses were used to compare sarcopenia rates between measurement methods. Correlations between sarcopenia measures and frailty scores were also tested. RESULTS: Between 2023 and 2024, 101 patients with ASD were evaluated for sarcopenia. The mean age was 66.0 years, and 66 patients (65.3%) were female. The percentage of patients meeting SDOC or EWGSOP2 cutoff criteria for sarcopenia based on grip strength, age-normalized grip strength, grip strength/BMI, gait speed, TUG test, or skeletal muscle index ranged from 0% to 74.2%. The distribution of patients meeting each criterion differed significantly for male and female patients (p < 0.0001 for both). Despite this, all functional and imaging-based measures of sarcopenia, except for gait speed, were significantly correlated with each other. Interestingly, many sarcopenic patients were not frail (69.8% per the EFS, 47.5% per the ASD-FI), but nearly all frail patients were sarcopenic (100% per the EFS, 82.2% per the ASD-FI). Sarcopenia measures were not significantly correlated with frailty scores with a few exceptions. CONCLUSIONS: The baseline prevalence of sarcopenia in patients with ASD varied widely according to the measurement method (0%-74.2%). Despite this, nearly all measures of sarcopenia were significantly correlated with each other. However, many sarcopenic patients were not frail, and sarcopenia measures largely did not correlate with frailty scores. These results highlight the need for a consensus criterion for sarcopenia in patients with spine disorders.
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 | 0,004 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».