Investigation of electrophysiological markers to predict clinical and functional outcome of schizophrenia using sparse partial least square regression

Introduction

Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

Objectives

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Methods

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

Results

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

Conclusions

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

Multivariate approach to identify electrophysiological markers for diagnosis and prognosis of schizophrenia

Introduction

Different electrophysiological indices have been investigated to identify diagnostic and prognostic markers of schizophrenia (SCZ). However, these indices have limited use in clinical practice, since both specificity and association with illness outcome remain unclear. In recent years, machine learning techniques, through the combination of multidimensional data, have been used to better characterize SCZ and to predict illness course.

Objectives

The aim of the present study is to identify multimodal electrophysiological biomarkers that could be used in clinical practice in order to improve precision in diagnosis and prognosis of SCZ.

Methods

Illness-related and functioning-related variables were measured at baseline in 113 subjects with SCZ and 57 healthy controls (HC), and after four-year follow-up in 61 SCZ. EEGs were recorded at baseline in resting-state condition and during two auditory tasks (MMN-P3a and N100-P3b). Through a Linear Support Vector Machine, using EEG data as predictors, four models were generated in order to classify SCZ and HC. Then, we combined unimodal classifiers’ scores through a stacking procedure. Pearson’s correlations between classifiers score with illness-related and functioning-related variables, at baseline and follow-up, were performed.

Results

Each EEG model produced significant classification (p < 0.05). Global classifier discriminated SCZ from HC with accuracy of 75.4% (p < 0.01). A significant correlation (r=0.40, p=0.002) between the global classifier scores with negative symptoms at follow-up was found. Within negative symptoms, blunted affect showed the strongest correlation.

Conclusions

Abnormalities in electrophysiological indices might be considered trait markers of schizophrenia. Our results suggest that multimodal electrophysiological markers might have prognostic value for negative symptoms.

COVID-19 pandemic and eating disorders: What impact on specific and general psychopathology?

Introduction

The coronavirus disease 2019 (COVID-19) pandemic and the resulting containment measures, such as “lockdown” and “social distancing”, have had important consequences on people’s mental and physical health.

Objectives

We aimed to study the effect of social isolation and subsequent re- exposure and eventual changes in general and ED-specific psychopathology in people with Eating Disorders (EDs).

Methods

Three-hundred twelve Italian people with EDs (179 Anorexia Nervosa, 83 Bulimia Nervosa, 48 Binge Eating Disorder and 22 Other Specific Feeding Eating Disorder) were asked to fill-in an online survey to explore several dimensions such as: anxiety, depression, panic, insomnia, suicide ideation, stress, post-traumatic stress and obsessive-compulsive symptoms. Differences in ED specific and general symptoms among the 3 investigated time periods (before, during and after the end of lockdown) were assessed with a one-way ANOVA with repeated measures. Subsequently, ED diagnosis was introduced as covariate in the analysis in order to investigate the possible contribution on psychopathological changes.

Results

ED core symptoms increased during the lockdown but most of them returned to pre-COVID19 levels at re-opening. The severity of general psychopathology also increased during the lockdown and persisted high in the following phase, except for depression and suicide ideation. None of this symptoms was affected by ED diagnosis, participants’age and illness duration.

Conclusions

People with EDs showed worsening of both general and specific psychopathology; moreover, changes in general psychopathology persisted in the re-opening period suggesting a higher stress vulnerability in this kind of patients.

Disclosure

No significant relationships.

Keyword

COVID19 and Eating Disorders

Real-world effectiveness of long acting aripiprazole: Treatment persistence and its correlates in the Italian clinical practice

OBJECTIVES

To identify the variables that are associated with persistence to Aripiprazole-Long Acting (A-LAI), in adult patients with schizophrenia.

METHODS

Observational, retrospective, non-interventional study involving 261 patients with schizophrenia.

RESULTS

Eighty-six percent of study subjects were persistent for at least 6 months. All subjects with baseline CGI-S of 1 or 2, 95% of subjects with CGI-S of 3, 86% with CGI-S of 4, 82% of subjects with CGI-S of 5, 73% of subjects with CGI of 6 and 90% of subjects with CGI of 7 were persistent. A-LAI treatment continuation rate was higher in patients with: 1) baseline CGI score ≤ 4; 2) schizophrenia dimension (LDPS) mania score ≤ 5; 3) psychotic spectrum schizoid score ≤ 11.

CONCLUSIONS

A relatively high number of patients (n = 225, 86%) were persistent to A-LAI for at least 6 months. Not surprisingly, very severe patients were more unlikely to be persistent. However, it is noteworthy that a large number of subjects with high CGI score at the time when A-LAI was started (82% of subjects with CGI-S of 5, 73% of subjects with CGI of 6 and 90% of subjects with CGI of 7) were persistent. Larger, controlled, prospective and longer studies are warranted.

Paliperidone palmitate in non-acute patients with schizophrenia previously unsuccessfully treated with risperidone long-acting therapy or frequently used conventional depot antipsychotics

PALMFlexS, a prospective multicentre, open-label, 6-month, phase IIIb interventional study, explored tolerability, safety and treatment response in adults (n = 231) with non-acute but symptomatic schizophrenia switching to flexibly dosed paliperidone palmitate (PP) after unsuccessful treatment with risperidone long-acting injectable therapy (RLAT) or conventional depot antipsychotics (APs). Treatment response was measured by change in Positive and Negative Syndrome Scale (PANSS) total score from baseline (BL) to last-observation-carried-forward (LOCF) endpoint (EP). Safety and tolerability assessments included Extrapyramidal Symptom Rating Scale (ESRS) total score and treatment-emergent adverse events. Significant reductions in mean PANSS total score were observed for all groups (-7.5 to -10.6; p ⩽ 0.01 [BL to LOCF EP]). After switching to PP, more than 50% of all patients achieved ⩾20% and one-third of RLAT-treated patients even achieved ⩾50% improvement in PANSS total score. Across groups, there were significant improvements (p < 0.05) in symptom severity as measured by Clinical Global Impression-Severity (CGI-S; trend for improvement with RLAT; p = 0.0568), subjective well-being, medication satisfaction, and patient functioning with PP. PP was generally well tolerated. Clinically relevant benefits were observed in non-acute patients with schizophrenia switched from RLAT or conventional depot APs to PP.