Français
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
International Journal of Spine Surgery 2019-Jun

Suboptimal Age-Adjusted Lumbo-Pelvic Mismatch Predicts Negative Cervical-Thoracic Compensation in Obese Patients.

Seuls les utilisateurs enregistrés peuvent traduire des articles
Se connecter S'inscrire
Le lien est enregistré dans le presse-papiers
Samantha Horn
Cole Bortz
Subaraman Ramachandran
Gregory Poorman
Frank Segreto
Matt Siow
Akhila Sure
Dennis Vasquez-Montes
Bassel Diebo
Jared Tishelman

Mots clés

Abstrait

Given the paucity of literature regarding compensatory mechanisms used by obese patients with sagittal malalignment, it is necessary to gain a better understanding of the effects of obesity on compensation after comparing the degree of malalignment to age-adjusted ideals. This study aims to compare baseline alignment of obese and nonobese patients using age-adjusted spino-pelvic alignment parameters, describing associated spinal changes.

Methods
Patients ≥ 18 years with full-body stereoradiographs were propensity-score matched for sex, baseline pelvic incidence (PI), and categorized as nonobese (body mass index < 30kg/m2) or obese (body mass index ≥ 30). Age-adjusted ideals were calculated for sagittal vertical axis, spino-pelvic mismatch (PI-LL), pelvic tilt, and T1 pelvic angle using established formulas. Patients were stratified as meeting alignment ideals, being above ideal, or being below. Spinal alignment parameters included C0-C2, C2-C7, C2-T3, cervical thoracic pelvic angle, cervical sagittal vertical axis SVA, thoracic kyphosis, T1 pelvic angle, T1 slope, sagittal vertical axis, lumbar lordosis (LL), PI, PI-LL, pelvic tilt. Lower-extremity parameters included sacrofemoral angle, knee flexion (KA), ankle flexion (AA), pelvic shift (PS), and global sagittal angle (GSA). Independent t tests compared parameters between cohorts.

Results
Included: 800 obese, 800 nonobese patients. Both groups recruited lower-extremity compensation: sacrofemoral angle (P = .004), KA, AA, PS, GSA (all P < .001). Obese patients meeting age-adjusted PI-LL had greater lower-extremity compensation than nonobese patients: lower sacrofemoral angle (P = .002), higher KA (P = .008), PS (P = .002), and GSA (P = .02). Obese patients with PI-LL mismatch higher than age-adjusted ideal recruited greater lower-extremity compensation than nonobese patients: higher KA, AA, PS, GSA (all P < .001). Obese patients showed compensation through the cervical spine: increased C0-C2, C2-C7, C2-T3, and cervical sagittal vertical axis (all P < .001), high T1 pelvic angle (P < .001), cervical thoracic pelvic angle (P = .03), and T1 slope (P < .001), with increased thoracic kyphosis (P = .015) and decreased LL (P < .001) compared to nonobese patients with PI-LL larger than age-adjusted ideal.

Regardless of malalignment severity, obese patients recruited lower-limb compensation more than nonobese patients. Obese patients with PI-LL mismatch larger than age-adjusted ideal also develop upper-cervical and cervicothoracic compensation for malalignment.III.Clinical evaluation should extend to the cervical spine in obese patients not meeting age-adjusted sagittal alignment ideals.

Rejoignez notre
page facebook

La base de données d'herbes médicinales la plus complète soutenue par la science

  • Fonctionne en 55 langues
  • Cures à base de plantes soutenues par la science
  • Reconnaissance des herbes par image
  • Carte GPS interactive - étiquetez les herbes sur place (à venir)
  • Lisez les publications scientifiques liées à votre recherche
  • Rechercher les herbes médicinales par leurs effets
  • Organisez vos intérêts et restez à jour avec les nouvelles recherches, essais cliniques et brevets

Tapez un symptôme ou une maladie et lisez des informations sur les herbes qui pourraient aider, tapez une herbe et voyez les maladies et symptômes contre lesquels elle est utilisée.
* Toutes les informations sont basées sur des recherches scientifiques publiées

Google Play badgeApp Store badge