Les prodromes des rechutes schizophréniques : étude descriptive et comparative

Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities

Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.

Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort

BACKGROUND

Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.

OBJECTIVES

To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach.

METHODS

Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal.

RESULTS

Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration <2 years, low Buss&Perry hostility score, high CTQ score, low premorbid IQ and low medication adherence (BARS) score were found to be the best predictors of follow-up withdrawal with a percentage of correct prediction of 52.4%, sensitivity 62%, specificity 38.7%.

CONCLUSION

Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.

Claims-based proxies of patient instability among commercially insured adults with schizophrenia

Objective

Schizophrenia (Sz) patients are among the highest utilizers of hospital-based services. Prevention of relapse is in part a treatment goal in order to reduce hospital admissions. However, predicting relapse is a challenge, particularly for payers and disease management firms with only access to claims data. Understandably, such organizations have had little success predicting relapse. A tool that allows payers to identify patients at elevated risk of relapse could facilitate targeted interventions prior to relapse and avoid rehospitalization. In this study, a series of proxy measures of patient instability, calculated from claims data were examined for their utility in identifying Sz patients at elevated risk of relapse.

Methods

Aetna claims were used to assess the relationship between instability of Sz patients and valence and magnitude of antipsychotic (AP) medication change during a 2-year period. Six proxies of instability including hospital admissions, emergency department visits, medication utilization patterns, and use of outpatient services were identified. Results were replicated using claims data from Truven MarketScan^®.

Results

Patients who switched AP ingredient had the highest overall instability at the point of switch and the second steepest decline in instability following switch. Those who changed to a long-acting injectable AP showed the second highest level of instability and the steepest decrease in instability following the change. Patients augmented with a second AP showed the smallest increase in instability, up to the switch. Results were directionally consistent between the two data sets.

Conclusion

Using claims-based proxy measures to estimate instability may provide a viable method to better understand Sz patient markers of change in disease severity. Also, such proxies could be used to identify those individuals with the greatest need for treatment modification preventing relapse, improving patient outcomes, and reducing the burden of illness.

[Positive changes in the severity of residual psychotic symptoms in schizophrenic patients switched to treatment with risperidone]

AIM

To evaluate the effect of changing therapy from typical antipsychotics to the atypical antipsychotic risperidone in the treatment of difficult-to-treat residual psychotic symptoms.

MATERIAL AND METHODS

The study included 15 patients, 8 men and 7 women, mean age 49.1±10.25 years, diagnosed with paranoid schizophrenia, partial remission (ICD-10 F20.04). At the beginning all participants received regular maintenance antipsychotic therapy with typical antipsychotics. The patient assessment with the PANSS, CGI scale and GAF scale was performed at the beginning (before the change of antipsychotics to risperidone) and in the end of the study. The primary efficacy endpoint was a reduction in scores on the PANSS items P1 'delusions' and P3 'hallucinatory behavior' to 1 (no such symptom) or 2 (minimal residual symptom). Secondary criteria were positive changes in the severity of other psychopathological symptoms and an increase in the social functioning level. The average dose of risperidone was 4.62±1.35 mg. The duration of treatment was 2 month.

RESULTS

After switching from typical antipsychotics to risperidone and two months monotherapy, there were significant positive changes in the total PANSS score as well as in positive subscale score and CGI-S score. The small, but statistically significant, changes were detected in the overall functioning of the patients (the increase in the GAF score). The dynamics of residual psychotic symptoms was unequal: the severity of hallucinatory symptoms decreased significantly while the delusional symptoms remained unchanged.

CONCLUSION

The authors suggest that excessive dopaminergic blockade might play a significant role in the pathogenesis of residual symptoms. This fact may explain the positive effect of treatment in the cases with the less degree of dopaminergic blockade. If it is true, the treatment strategy in the maintenance phase should not be a direct continuation of acute phase therapy and switching to other drugs and changing of the dose are needed.

Reduced Responsiveness to Social Provocation in Autism Spectrum Disorder

Deficits in emotion processing and social interaction are prominent symptoms of autism spectrum disorder (ASD). ASD has also been associated with aggressive tendencies towards self and others. The prevalence of aggressive behavior in this disorder, its etiology and its impact on social life are still unclear. This study investigated behavioral and physiological effects of social provocation in patients with ASD and healthy controls. We used a modified Taylor Aggression Paradigm in 24 high-functioning patients with ASD and 24 healthy controls. Participants were instructed to play against a fictitious human opponent. Money withdrawals toward the participant represented provocation and money deduction by the participant denoted aggressive behavior. Throughout the measurement, electrodermal activity (EDA) was recorded. Healthy controls showed higher aggressive responses to high provocation compared to low provocation, which demonstrated the effectiveness of the used procedure in eliciting aggression. Patients' responses were not influenced by the level of social provocation, although in both groups aggression was higher after lost compared to won trials. Physiologically, controls showed fewer but higher EDA amplitudes when responding aggressively, whereas patients displayed the opposite pattern of more but lower EDA amplitudes. The modified Taylor Aggression Paradigm successfully elicited aggression and revealed different behavioral and neurophysiological responses in patients and healthy controls. Patients' aggressive behavior as well as their physiological responses were less modulated by level of provocation compared to controls. Therapeutic attempts for patients might concentrate on improving empathic abilities and the understanding of social situations, including provocation and aggressive behavior.