Improvement of Non-motor Symptoms and Quality of Life After Deep Brain Stimulation for Refractory Dystonia: A 1-Year Follow-Up

Listik, Clarice; Cury, Rubens Gisbert; Casagrande, Sara Carvalho Barbosa; Listik, Eduardo; Arnaut, Débora; Santiago, Natally; Silva, Valquíria Aparecida da; Galhardoni, Ricardo; Machado, Júlia de Lima Arantes; Almeida, Jessica Campelo de; Barbosa, Egberto Reis; Teixeira, Manoel Jacobsen; Andrade, Daniel Ciampi de

doi:10.3389/fneur.2021.717239

Cited by 6 publications

(23 citation statements)

References 20 publications

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“…Here too, pain intensity did not correlate with severity of motor symptoms. This further supports the view that motor symptoms and NMS such as pain are likely to not only depend on different mechanisms but also respond differently to treatment and have different prognoses 42,56 …”

Section: Discussionsupporting

confidence: 73%

“…12 Additionally, efficacious treatments to control dystonic movements may not wholly mitigate pain in PwD, which persists despite improvement in motor symptoms. 35,42 The main driving mechanism of dystonia is believed to be the reduction in cortical inhibition, impaired synaptic plasticity, and altered gain in somatosensory processing. [43][44][45][46][47][48][49] Our data show that CP's severity and impact did not correlate with dystonia's motor severity or disability.…”

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

“…This also supports the long-acknowledged lack of correlation between motor symptom severity and pain intensity in movement disorders such as PD and dystonia. 4,5,42,56,65 One important aspect is that the aforementioned potential bias refers to pain classification in step 2. The determination of pain chronicity (step 1) was based on a classic 3-month cutoff, which has been extensively validated and is recommended to classify pain as chronic.…”

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

“…We have evaluated PwD who have an established dystonia diagnosis and were under optimized treatment for their motor symptoms. It is known that dystonia's treatment may improve pain, 4,10,60,61 though it may persist even after DBS 42 or botulinum toxin injections. 35 Although our study was not designed to evaluate the frequency or prevalence of CP in PwD, it suggests that CP is present despite optimized motor control.…”

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

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Development and Validation of the Dystonia‐Pain Classification System: A Multicenter Study

Listik

Rolim

et al. 2023

Movement Disorders

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Background Dystonia is associated with disabling nonmotor symptoms like chronic pain (CP), which is prevalent in dystonia and significantly impacts the quality of life (QoL). There is no validated tool for assessing CP in dystonia, which substantially hampers pain management. Objective The aim was to develop a CP classification and scoring system for dystonia. Methods A multidisciplinary group was established to develop the Dystonia‐Pain Classification System (Dystonia‐PCS). The classification of CP as related or unrelated to dystonia was followed by the assessment of pain severity score, encompassing pain intensity, frequency, and impact on daily living. Then, consecutive patients with inherited/idiopathic dystonia of different spatial distribution were recruited in a cross‐sectional multicenter validation study. Dystonia‐PCS was compared to validated pain, mood, QoL, and dystonia scales (Brief Pain Inventory, Douleur Neuropathique‐4 questionnaire, European QoL‐5 Dimensions‐3 Level Version, and Burke–Fahn–Marsden Dystonia Rating Scale). Results CP was present in 81 of 123 recruited patients, being directly related to dystonia in 82.7%, aggravated by dystonia in 8.8%, and nonrelated to dystonia in 7.5%. Dystonia‐PCS had excellent intra‐rater (Intraclass Correlation Coefficient ‐ ICC: 0.941) and inter‐rater (ICC: 0.867) reliability. In addition, pain severity score correlated with European QoL‐5 Dimensions‐3 Level Version's pain subscore (r = 0.635, P < 0.001) and the Brief Pain Inventory's severity and interference scores (r = 0.553, P < 0.001 and r = 0.609, P < 0.001, respectively). Conclusions Dystonia‐PCS is a reliable tool to categorize and quantify CP impact in dystonia and will help improve clinical trial design and management of CP in patients affected by this disorder. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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Section: Discussionsupporting

confidence: 73%

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

Section: T H E D Y S T O N I a -P A I N C L A S S I F I C A T I O N S...mentioning

confidence: 99%

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Development and Validation of the Dystonia‐Pain Classification System: A Multicenter Study

Listik

Rolim

et al. 2023

Movement Disorders

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“…For DBS reporting of NMS, especially in adult-onset focal dystonia, is scarce. A review based on studies with limited cohort sizes showed positive effects on pain [21], and a prospective study in generalized dystonia demonstrated improvement in a wide array of NMS [22 ▪ ]. Nonetheless, there is a promising development with several recent publications stressing the importance of NMS in the treatment of patients with dystonia, advocating for a more holistic approach [23–25].…”

Section: Nonmotor Symptomsmentioning

confidence: 99%

New developments in diagnostics and treatment of adult-onset focal dystonia

Centen

Egmond

Tijssen

2023

Current Opinion in Neurology

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Purpose of review The aim of this review is to showcase the recent developments in the field of diagnosis and treatment of adult-onset focal dystonia. Recent findings Accurate phenotyping of focal dystonia is essential in the process of finding an underlying cause, including acquired, genetic, and idiopathic causes. Motor symptoms as well as the associated nonmotor symptoms and their detrimental impact on quality of life have received increased interest over the last years. The diagnostic process is complicated by the steadily increasing numbers of newly discovered genes associated with dystonia. Recent efforts have been aimed at further developing recommendations and algorithms to aid in diagnosis and in navigating the use of diagnostic tools. In terms of treatment, research on DBS is advancing towards a better understanding of the most effective stimulation locations within the globus pallidus. Moreover, with the introduction of the LFP-recording devices, the search continues for an accurate electrophysiological biomarker for dystonia. Summary Accurate phenotyping and (sub)classification of patients with dystonia is important for improving diagnosis, subsequent treatment effect and population-based study outcomes in research. Medical practitioners should be attentive to the presence of nonmotor symptoms in dystonia.

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A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease

Riasi,

Delrobaei,

Salari

2024

Sci Rep

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Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine. Recurrent neural networks have memory blocks that retain crucial information from previous patient visits. This feature is helpful for patients with PD, as the neurologist can refer to the patient's previous state and the prescribed medication to make informed decisions. We employed data from the Parkinson's Progression Markers Initiative. The dataset included information on the Unified Parkinson's Disease Rating Scale, Activities of Daily Living, Hoehn and Yahr scale, demographic details, and medication use logs for each patient. We evaluated several models, such as multi-layer perceptron (MLP), Simple-RNN, long short-term memory (LSTM), and gated recurrent units (GRU). Our analysis found that recurrent neural networks (LSTM and GRU) performed the best. More specifically, when using LSTM, we were able to predict levodopa and dopamine agonist dosage with a mean squared error of 0.009 and 0.003, mean absolute error of 0.062 and 0.030, root mean square error of 0.099 and 0.053, and R-squared of 0.514 and 0.711, respectively.

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Improvement of Non-motor Symptoms and Quality of Life After Deep Brain Stimulation for Refractory Dystonia: A 1-Year Follow-Up

Cited by 6 publications

References 20 publications

Development and Validation of the Dystonia‐Pain Classification System: A Multicenter Study

Development and Validation of the Dystonia‐Pain Classification System: A Multicenter Study

New developments in diagnostics and treatment of adult-onset focal dystonia

A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease

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