Quantitative Assessment of Gait and Balance Following Deep Brain Stimulation in Patients with Parkinson’s Disease
Notice bibliographique
Résumé
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremor, rigidity, bradykinesia and postural instability. Deep Brain Stimulation (DBS) targeting either the subthalamic nucleus (STN) or globus pallidus interna (GPi) is highly effective for treating the cardinal motor symptoms of PD and motor complications of levodopa (L-DOPA) therapy, but its impact on gait and balance symptoms is not well established. In the advanced stages of PD, gait and balance impairments can limit patient mobility, increase the risk of falls and fall-related injuries, and reduce quality of life. The objective of our study was to investigate the precise impact of DBS on the mechanisms of gait (pace, rhythm, variability, asymmetry and postural control) using a quantitative gait analysis. Eight participants awaiting DBS (prospectively implanted participants) were recruited for our study, as well as five PD participants who had previously received DBS (already implanted participants). Prospectively implanted participants were evaluated pre-operatively and post-operatively at four weeks, three months and six months after the initial DBS programming session. Already implanted participants were evaluated after programming was optimized. All participants were tested in four standard treatment conditions: OFF-medication/OFF-DBS, OFF-medication/ON-DBS, ON-medication/OFF-DBS, and ON-medication/ON-DBS. Participants were instructed to walk on a computerized walkway (GaitRite), which was used to collect objective spatial and temporal parameters. To investigate changes in the five domains of gait, our study measured gait velocity (cm/s), step length (cm), stance time (ms), swing time (ms), stance time ratio (|L/R|), step length ratio (|L/R|), and step length variability (% coefficient of variation). Additional standard tests and clinical scales, including the Timed-Up and Go (TUG), Unified Parkinson’s Disease Rating Scale-III (UPDRS-III), Montreal Cognitive Assessment (MoCA) and Freezing of Gait Questionnaire (FOG-Q), were also analyzed. The ON-medication/ON-DBS condition, otherwise known as the best treatment condition (BTC), produced a significant improvement in UPDRS-III, TUG, gait velocity and step length at four weeks and three months post-programming relative to the OFF-medication/OFF-DBS condition (P<0.05). There was a trend towards further improvement in these parameters at six months post-programming in the BTC, but statistical significance was not achieved, likely due to smaller sample size at this time point. Step length variability was significantly reduced in the BTC at four weeks post-programming (P=0.008) and during the OFF-medication/ON-DBS condition at three months (P=0.02), once DBS programming approached optimization. Step length asymmetry improved in the BTC at three months post-programming (P=0.004). Swing time improved at four weeks during the OFF-medication/ON-DBS state (P=0.002) and at three months during the ON-medication/OFF-DBS state (P<0.05). Stance time, stance time ratio, and stride width did not significantly change for prospectively implanted participants. No statistically significant changes were observed in FOG-Q scores before and after DBS. For already implanted participants, performance during the TUG, along with gait velocity, step length and stride width were significantly improved in the BTC relative to OFF-medication/OFF-DBS (P<0.05). Gait velocity and TUG times were also significantly better in the BTC compared to the ON-medication/OFF-DBS condition (P<0.05). We also compared gait and balance outcomes between STN- and GPi-DBS. Our preliminary findings show GPi-DBS to have a slight advantage for improving pace, select gait asymmetry parameters, and balance-related parameters in the BTC. Taken together, our study shows STN- and GPi-DBS does not seem to worsen axial gait and balance in PD patients without pre-existing FOG. However, further analyses with more participants should be conducted to verify our preliminary findings.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».