120 research outputs found
Spanish expert consensus on the use of safinamide in Parkinson's disease
La safinamida es un nuevo fármaco para el tratamiento de pacientes con enfermedad de Parkinson (EP) con fluctuaciones como tratamiento complementario a levodopa. Dado que por el momento aún no existen estudios de fase IV postautorización debido a la reciente incorporación de la safinamida a la práctica clínica habitual, el interés de este proyecto radica en el desarrollo de una guía de manejo clínico de la safinamida basada en las opiniones de expertos de trastornos del movimiento. Este proyecto se desarrolló en 2 fases: una primera fase que constó de 16 reuniones locales y una segunda fase que consistió en una reunión nacional. Dichas reuniones siguieron un guion de trabajo preestablecido. Tras la reunión nacional se recopilaron las principales conclusiones de los expertos, que han supuesto la base para redactar la presente guía clínica. Se concluyó que la safinamida es eficaz en la reducción de las fluctuaciones motoras y no motoras. Los pacientes con EP con fluctuaciones leves-moderadas son los que más se benefician del tratamiento, si bien el fármaco puede contribuir a mejorar diversos problemas clínicos en pacientes con EP avanzada. Se ha destacado la posibilidad de reducir la dosis de otros fármacos dopaminérgicos tras la introducción de la safinamida, lo cual contribuiría a reducir efectos adversos como el trastorno de control de impulsos. Se hipotetizó sobre el posible efecto de la safinamida sobre la mejoría de las discinesias a dosis más altas de las habitualmente utilizadas. Se ha consensuado que la safinamida es bien tolerada y presenta un perfil de efectos adversos favorable frente a placebo.Safinamide is a new add-on drug to levodopa for the treatment of Parkinson's disease (PD) with motor fluctuations. Due to the recent incorporation of safinamide into routine clinical practice, no post-authorisation phase IV studies on the safety of safinamide have been conducted to date. This study provides clinical management guidelines for safinamide based on the opinion of a group of experts in movement disorders. This project was developed in 2 phases: 16 local meetings in phase 1 and a national meeting in phase 2. The meetings followed a pre-established agenda. The present clinical practice guidelines are based on the main conclusions reached during the national meeting. The group concluded that safinamide is effective in reducing motor and non-motor fluctuations. PD patients with mild-to-moderate fluctuations benefit most from treatment, although the drug may also improve the clinical status of patients with advanced PD. The dose of other dopaminergic drugs may be reduced after introducing safinamide, which would contribute to reducing such adverse reactions as impulse control disorder. At doses higher than those usually prescribed, safinamide may also improve dyskinesia. The experts agreed that safinamide is well tolerated and causes few adverse reactions when compared with placebo
Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning.
Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission
Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission
Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p
Present and Future of Parkinson’s Disease in Spain: PARKINSON-2030 Delphi Project
Parkinson's disease (PD) is a chronic progressive and irreversible disease and the second most common neurodegenerative disease worldwide. In Spain, it affects around 120.000-150.000 individuals, and its prevalence is estimated to increase in the future. PD has a great impact on patients' and caregivers' lives and also entails a substantial socioeconomic burden. The aim of the present study was to examine the current situation and the 10-year PD forecast for Spain in order to optimize and design future management strategies. This study was performed using the modified Delphi method to try to obtain a consensus among a panel of movement disorders experts. According to the panel, future PD management will improve diagnostic capacity and follow-up, it will include multidisciplinary teams, and innovative treatments will be developed. The expansion of new technologies and studies on biomarkers will have an impact on future PD management, leading to more accurate diagnoses, prognoses, and individualized therapies. However, the socio-economic impact of the disease will continue to be significant by 2030, especially for patients in advanced stages. This study highlighted the unmet needs in diagnosis and treatment and how crucial it is to establish recommendations for future diagnostic and therapeutic management of PD
Long-term efficacy of botulinum toxin A for treatment of blepharospasm,hemifacial spasm, and spastic entropion: a multicentre study using two drug-dose escalation indexes
PURPOSE: To investigate the long-term effectiveness and safety of botulinum
neurotoxin A (BoNT-A) treatment in patients with blepharospasm (BEB), hemifacial
spasm (HFS), and entropion (EN) and to use for the first time two modified indexes, 'botulin toxin escalation index-U' (BEI-U) and 'botulin toxin escalation
index percentage' (BEI-%), in the dose-escalation evaluation. METHODS: All
patients in this multicentre study were followed for at least 10 years and main
outcomes were clinical efficacy, duration of relief, BEI-U and BEI-%, and
frequency of adverse events. RESULTS: BEB, HFS, and EN patients received a mean
BoNT-A dose with a significant inter-group difference (P<0.0005, respectively).
The mean (+/-SD) effect duration was statistically different (P=0.009) among
three patient groups. Regarding the BoNT-A escalation indexes, the mean (+/-SD)
values of BEI-U and BEI-% were statistically different (P=0.035 and 0.047,
respectively) among the three groups. In BEB patients, the BEI-% was
significantly increased in younger compared with older patients (P=0.008). The
most frequent adverse events were upper lid ptosis, diplopia, ecchymosis, and
localized bruising. CONCLUSIONS: This long-term multicentre study supports a high
efficacy and good safety profile of BoNT-A for treatment of BEB, HFS, and EN. The
BEI indexes indicate a significantly greater BoNT-A-dose escalation for BEB
patients compared with HFS or EN patients and a significantly greater BEI-% in
younger vsolder BEB patients. These results confirm a greater efficacy in the
elderly and provide a framework for long-term studies with a more flexible and
reliable evaluation of drug-dose escalation
High-throughput mutational analysis of TOR1A in primary dystonia
<p>Abstract</p> <p>Background</p> <p>Although the c.904_906delGAG mutation in Exon 5 of <it>TOR1A </it>typically manifests as early-onset generalized dystonia, DYT1 dystonia is genetically and clinically heterogeneous. Recently, another Exon 5 mutation (c.863G>A) has been associated with early-onset generalized dystonia and some ΔGAG mutation carriers present with late-onset focal dystonia. The aim of this study was to identify <it>TOR1A </it>Exon 5 mutations in a large cohort of subjects with mainly non-generalized primary dystonia.</p> <p>Methods</p> <p>High resolution melting (HRM) was used to examine the entire <it>TOR1A </it>Exon 5 coding sequence in 1014 subjects with primary dystonia (422 spasmodic dysphonia, 285 cervical dystonia, 67 blepharospasm, 41 writer's cramp, 16 oromandibular dystonia, 38 other primary focal dystonia, 112 segmental dystonia, 16 multifocal dystonia, and 17 generalized dystonia) and 250 controls (150 neurologically normal and 100 with other movement disorders). Diagnostic sensitivity and specificity were evaluated in an additional 8 subjects with known ΔGAG DYT1 dystonia and 88 subjects with ΔGAG-negative dystonia.</p> <p>Results</p> <p>HRM of <it>TOR1A </it>Exon 5 showed high (100%) diagnostic sensitivity and specificity. HRM was rapid and economical. HRM reliably differentiated the <it>TOR1A </it>ΔGAG and c.863G>A mutations. Melting curves were normal in 250/250 controls and 1012/1014 subjects with primary dystonia. The two subjects with shifted melting curves were found to harbor the classic ΔGAG deletion: 1) a non-Jewish Caucasian female with childhood-onset multifocal dystonia and 2) an Ashkenazi Jewish female with adolescent-onset spasmodic dysphonia.</p> <p>Conclusion</p> <p>First, HRM is an inexpensive, diagnostically sensitive and specific, high-throughput method for mutation discovery. Second, Exon 5 mutations in <it>TOR1A </it>are rarely associated with non-generalized primary dystonia.</p
Identification of wearing-off manifestations (reduction of levodopa effect) in Parkinson's disease using specific questionnaire and comparison of the results with routine ambulatory evaluations
Pharmacological Strategies for the Management of Levodopa-Induced Dyskinesia in Patients with Parkinson’s Disease
Prevalence and correlates of sleep disorders in Parkinson’s disease: a polysomnographic study
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