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1.
Mov Disord Clin Pract ; 11(7): 855-860, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38715209

RESUMO

BACKGROUND: Subthalamic deep brain stimulation (STN-DBS) reduces antiparkinsonian medications in Parkinson's disease (PD) compared with the preoperative state. Longitudinal and comparative studies on this effect are lacking. OBJECTIVE: To compare longitudinal trajectories of antiparkinsonian medication in STN-DBS treated patients to non-surgically treated control patients. METHODS: We collected retrospective information on antiparkinsonian medication from PD patients that underwent subthalamic DBS between 1999 and 2010 and control PD patients similar in age at onset and baseline, sex-distribution, and comorbidities. RESULTS: In 74 DBS patients levodopa-equivalent daily dose (LEDD) were reduced by 33.9-56.0% in relation to the preoperative baseline over the 14-year observational period. In 61 control patients LEDDs increased over approximately 10 years, causing a significant divergence between groups. The largest difference amongst single drug-classes was observed for dopamine agonists. CONCLUSION: In PD patients, chronic STN-DBS was associated with a lower LEDD compared with control patients over 14 years.


Assuntos
Antiparkinsonianos , Estimulação Encefálica Profunda , Doença de Parkinson , Núcleo Subtalâmico , Humanos , Doença de Parkinson/terapia , Doença de Parkinson/tratamento farmacológico , Estimulação Encefálica Profunda/métodos , Masculino , Feminino , Pessoa de Meia-Idade , Antiparkinsonianos/uso terapêutico , Antiparkinsonianos/administração & dosagem , Idoso , Estudos Retrospectivos , Levodopa/administração & dosagem , Levodopa/uso terapêutico , Estudos Longitudinais , Resultado do Tratamento
2.
NPJ Digit Med ; 7(1): 160, 2024 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-38890413

RESUMO

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.

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