Physical health comorbidities in women with personality disorder: data from the Geelong osteoporosis study

BACKGROUND: Associations between common psychiatric disorders, psychotic disorders and physical health comorbidities are frequently investigated. The complex relationship between personality disorders (PDs) and physical health is less understood, and findings to date are varied. This study aims to investigate associations between PDs with a number of prevalent physical health conditions. METHODS: This study examined data collected from women (n=765;≥25years) participating in a population-based study located in south-eastern Australia. Lifetime history of psychiatric disorders was assessed using the semi-structured clinical interviews (SCID-I/NP and SCID-II). The presence of physical health conditions (lifetime) were identified via a combination of self-report, medical records, medication use and clinical data. Socioeconomic status, and information regarding medication use, lifestyle behaviors, and sociodemographic information was collected via questionnaires. Logistic regression models were used to investigate associations. RESULTS: After adjustment for sociodemographic variables (age, socioeconomic status) and health-related factors (body mass index, physical activity, smoking, psychotropic medication use), PDs were consistently associated with a range of physical health conditions. Novel associations were observed between Cluster A PDs and gastro-oesophageal reflux disease (GORD); Cluster B PDs with syncope and seizures, as well as arthritis; and Cluster C PDs with GORD and recurrent headaches. CONCLUSIONS: PDs were associated with physical comorbidity. The current data contribute to a growing evidence base demonstrating associations between PDs and a number of physical health conditions independent of psychiatric comorbidity, sociodemographic and lifestyle factors. Longitudinal studies are now required to investigate causal pathways, as are studies determining pathological mechanisms.

Emerging drugs for partial-onset epilepsy: a review of brivaracetam

There are more than 12 new antiepileptic drugs approved in the last 2 decades. Even with these newer agents, seizure remission is still unachievable in around 30% of patients with partial-onset seizures (POS). Brivaracetam (BRV) is chemically related to levetiracetam (LEV) and possesses a strong binding affinity for the synaptic vesicle protein 2A tenfold above that of LEV, and other possible modes of antiepileptic actions. BRV is now under Phase III development for POS, but data from one Phase III trial also suggested its potential efficacy for primary generalized seizures. The purpose of this review is to provide updated information on the mechanisms of action of the available antiepileptic drugs, with a focus on BRV to assess its pharmacology, pharmacokinetics, clinical efficacy, safety, and tolerability in patients with uncontrolled POS. To date, six Phase IIb and III clinical trials have been performed to investigate the efficacy, safety, and tolerability of BRV as an adjunctive treatment for patients with POS. Generally, BRV was well tolerated and did not show significant difference in safety profile, compared to placebo. The efficacy outcomes of BRV, although not consistent across trials, did indicate that BRV was a promising add-on therapy for patients with POS. In conclusion, the many favorable attributes of BRV, like its high oral efficacy, good tolerability, dosing regimen, and minimal drug interaction, make it a promising antiepileptic therapy for patients with uncontrolled partial-onset epilepsy.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure-sample entropy (SampEn)-and a more recently proposed complexity measure-distribution entropy (DistEn)-were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.