The role of short-term video electroencephalogram monitoring for epilepsy and psychogenic seizures: Experience from a Latin American referral center.

Short-term VEEG represents an affordable option in limited resources environments. There are few reports on its use. Its diagnostic yield is variable (7-57%) and can be related to the differences in recording time. The present study analyzes possible predictive factors to support the indication of a short-term VEEG. We analyzed short-term VEEG studies (<24 h) throughout a period of 5 years (2013-2017). The patients were clustered according to the date of last epileptic seizure and the frequency of epileptic events per month and subcategorized depending on the frequency found. Chi square univariate analysis was performed looking for predictive variables to obtain an epileptic short-term EEG. A multivariate logistic regression analysis was performed with statistically significant variables. A total of 1092 VEEG were analyzed from 832 patients. 34.5% were reported as epileptic VEEG. In the multivariate analysis, 3 predictors of epileptic short-term VEEG were identified: The use of 2 or more antiepileptic drugs (AEDs) (OR 1.67, CI 1.23-2.25, p = 0.001), the presence of an epileptic event in the last month (OR 1.53, CI 1.07-2.17, p = 0.018) and daily seizures (OR 1.84, CI 1.21-2.78, p = 0.004). Six-month seizure free subjects predict a non-epileptic VEEG (OR 0.58, CI 0.30-0.89, p = 0.013).

A Polysomnographic Study of Parkinson's Disease Sleep Architecture.

Sleep disturbance is a common nonmotor phenomenon in Parkinson's disease (PD) affecting patient's quality of life. In this study, we examined the association between clinical characteristics with sleep disorders and sleep architecture patterns in a PD cohort. Patients underwent a standardized polysomnography study (PSG) in their "on medication" state. We observed that male gender and disease duration were independently associated with obstructive sleep apnea (OSA). Only lower levodopa equivalent dose (LED) was associated with periodic limb movement disorders (PLMD). REM sleep behavior disorder (RBD) was more common among older patients, with higher MDS-UPDRS III scores, and LED. None of the investigated variables were associated with the awakenings/arousals (A/A). Sleep efficiency was predicted by amantadine usage and age, while sleep stage 1 was predicted by dopamine agonists and Hoehn & Yahr severity. The use of MAO-B inhibitors and MDS-UPDRS part III were predictors of sleep stages 2 and 3. Age was the only predictor of REM sleep stage and gender for total sleep time. We conclude that sleep disorders and architecture are poorly predictable by clinical PD characteristics and other disease related factors must also be contributing to these sleep disturbances.

Prevalence and correlates of sleep disorders in Parkinson's disease: a polysomnographic study.

OBJECTIVE: Sleep disorders in Parkinson's disease are very common. Polysomnography (PSG) is considered the gold standard for diagnosis. The aim of the present study is to assess the prevalence of nocturnal sleep disorders diagnosed by polysomnography and to determine the associated clinical factors. METHOD: A total of 120 patients with Parkinson's disease were included. All patients underwent a standardized overnight, single night polysomnography. RESULTS: Ninety-four (78.3%) patients had an abnormal PSG. Half of the patients fulfilled criteria for sleep apnea-hypopnea syndrome (SAHS); rapid eye movement behavior disorder (RBD) was present in 37.5%. Characteristics associated with SAHS were age (p = 0.049) and body mass index (p = 0.016). Regarding RBD, age (p < 0.001), left motor onset (p = 0.047) and levodopa equivalent dose (p = 0.002) were the main predictors. CONCLUSION: SAHS and RBD were the most frequent sleep disorders. Higher levodopa equivalent dose and body mass index appear to be risk factors for RBD and SAHS, respectively.

Prevalence and correlates of sleep disorders in Parkinson’s disease: a polysomnographic study / Prevalência e correlatos de distúrbios do sono na doença de Parkinson: estudo polissonográfico

Objective Sleep disorders in Parkinson’s disease are very common. Polysomnography (PSG) is considered the gold standard for diagnosis. The aim of the present study is to assess the prevalence of nocturnal sleep disorders diagnosed by polysomnography and to determine the associated clinical factors. Method A total of 120 patients with Parkinson’s disease were included. All patients underwent a standardized overnight, single night polysomnography. Results Ninety-four (78.3%) patients had an abnormal PSG. Half of the patients fulfilled criteria for sleep apnea-hypopnea syndrome (SAHS); rapid eye movement behavior disorder (RBD) was present in 37.5%. Characteristics associated with SAHS were age (p = 0.049) and body mass index (p = 0.016). Regarding RBD, age (p < 0.001), left motor onset (p = 0.047) and levodopa equivalent dose (p = 0.002) were the main predictors. Conclusion SAHS and RBD were the most frequent sleep disorders. Higher levodopa equivalent dose and body mass index appear to be risk factors for RBD and SAHS, respectively. .

Objetivo Os distúrbios do sono na doença de Parkinson são muito comuns. A polissonografia é considerada o padrão-ouro para o diagnóstico. O objetivo do presente estudo é avaliar a prevalência de distúrbios de sono noturno diagnosticados por polissonografia. Método 120 pacientes com doença de Parkinson foram incluídos. Todos os pacientes foram submetidos a uma única noite, polissonografia de noite. Resultados 94 (78,3%) pacientes tiveram uma polissonografia anormal e 50% preencheram a síndrome da apneia e hipopneia do sono (SAHOS); distúrbio de comportamento do movimento rápido dos olhos (RBD) esteve presente em 37,5%. As características associadas com SAHOS foram idade (p = 0,049) e índice de massa corporal (p = 0,016). Quanto RBD, idade (p < 0,001), deixou início motor (p = 0,047) e levodopa dose equivalente (p = 0,002) foram os preditores. Conclusão SAHOS e RBD foram os distúrbios do sono mais frequente. Dose superior equivalente de levodopa e índice de massa corporal parecem ser fatores de risco, respectivamente. .