Defining Treatment Response, Remission, Relapse, and Recovery in First-Episode Psychosis: A Survey among Korean Experts

Drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics

Background

The present study aimed to investigate the drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics.

Research design and methods

A total of 119 patients with schizophrenia treated with aripiprazole were included to build an aripiprazole population pharmacokinetic model using nonlinear mixed effects.

Results

The weight and concomitant medication of fluoxetine influenced aripiprazole clearance. Under the same weight, the aripiprazole clearance rates were 0.714:1 in patients with or without fluoxetine, respectively. In addition, without fluoxetine, for the once-daily aripiprazole regimen, dosages of 0.3 and 0.2 mg kg-1 day-1 were recommended for patients with schizophrenia weighing 40-95 and 95-120 kg, respectively, while for the twice-daily aripiprazole regimen, 0.3 mg kg-1 day-1 was recommended for those weighing 40-120 kg. With fluoxetine, for the once-daily aripiprazole regimen, a dosage of 0.2 mg kg-1 day-1 was recommended for patients with schizophrenia weighing 40-120 kg, while for the twice-daily aripiprazole regimen, 0.3 and 0.2 mg kg-1 day-1 were recommended for those weighing 40-60 and 60-120 kg, respectively.

Conclusion

This is the first investigation of the effects of fluoxetine on aripiprazole via drug-drug interaction. The optimal aripiprazole initial dosage is recommended in patients with schizophrenia.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality

BACKGROUND

Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility.

OBJECTIVE

This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence.

METHODS

We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months.

RESULTS

A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets.

CONCLUSIONS

Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Predictors of full recovery in patients with early stage schizophrenia spectrum disorders

To promote recovery in psychosis, targeting modifiable factors related to recovery is critical. Using more strict definition of full recovery, we examined predictors for recovery in patients with early stage schizophrenia spectrum disorders (SSD) followed up to 6.5 years. The target subjects were 375 patients with early stage SSD who had been over at least 1-year after registration and evaluated. The criteria for full recovery were having the score of the Positive and Negative Syndrome Scale (PANSS) 8-item ≤ 2 and adequate functional recovery for at least 1-year. We performed univariate Cox and stepwise Cox regression in both total and acute patients. In stepwise Cox regression, several independent predictors for recovery, i.e., negative symptoms of the PANSS, duration of untreated psychosis (DUP) and non-professional job were identified in patients with early stage SSD. In acute patients, other factors such as professional job and subjective well-being under neuroleptics were more important. The present study identified independent predictors for recovery modifiable by various psychosocial intervention and early intervention services. Moreover, it highlights the need of providing different treatment strategies depending on clinical status.

Aripiprazole for the treatment of schizophrenia: Recommendations of a panel of Spanish experts on its use in clinical practice

OBJECTIVES

Aripiprazole is an antipsychotic with a partial agonism of dopamine D₂ and D₃ receptors. This differential mechanism implies a rigorous appraisal of the appropriate therapeutic strategies in certain situations. To answer currently unsolved clinical questions about the use of oral and long-acting injectable (LAI) aripiprazole, we present here an expert consensus from 12 Spanish psychiatrists and a pharmacologist with extensive experience in the use of this antipsychotic.

METHODS

Through one face-to-face session and online collaboration, we reached consensus and established practical recommendations based on scientific evidence and clinical experience. We classified the available scientific literature according to SIGN system and attributed a level of evidence to each reviewed article.

RESULTS

The recommendations were divided according to (i) chronological dimension (based on previous treatments, including patients naïve or not to antipsychotic treatment and maintenance regimen), and (ii) dimension related to therapeutic options, comprising switches to aripiprazole and the most used combinations with this antipsychotic.

CONCLUSIONS

We recommend considering aripiprazole as first treatment option in the early stages of schizophrenia and in patients with affective symptoms and contemplating a switch to aripiprazole LAI in all candidate patients. Importantly, switches from other antipsychotics should consider previous antipsychotic history and exposure to aripiprazole. KEYPOINTSAripiprazole can be considered as first treatment option in early stages of schizophrenia and in patients with significant affective symptoms.Aripiprazole LAI shows better adherence than oral aripiprazole and could be considered in all candidate patients.Before switching to aripiprazole, detailed information about previous antipsychotic history should be gathered.Switch to aripiprazole should be managed differently for aripiprazole naïve and non-naïve patients.Rigorous and controlled studies on antipsychotics in real clinical practice should be carried out.

Guidelines for Discontinuation of Antipsychotics in Patients Who Recover From First-Episode Schizophrenia Spectrum Disorders: Derived From the Aggregated Opinions of Asian Network of Early Psychosis Experts and Literature Review

OBJECTIVE

Antipsychotic discontinuation has been a long-standing clinical and medicolegal issue. The Asian Network of Early Psychosis developed guidelines for antipsychotic discontinuation in patients who recover from first-episode non-affective psychosis. We reviewed the existing studies and guidelines on antipsychotic discontinuation to develop guidelines for antipsychotic discontinuation in such patients.

METHODS

We reviewed the relevant studies, reviews, guidelines, and ongoing trials related to antipsychotic discontinuation in patients with first-episode psychosis or schizophrenia. The quality of randomized controlled trials was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach.

RESULTS

Most studies had low to very low quality, and 2 had moderate quality. All studies, except 1, advised against antipsychotic discontinuation because of higher relapse rates in the antipsychotic discontinuation group (19%-82% at 1-year follow-up) than the treatment maintenance group compared with the maintenance group. Based on expert opinion and Grading of Recommendations Assessment, Development, and Evaluation evidence of trials, guidelines have been recommended for future discontinuation studies on patients with first-episode schizophrenia spectrum disorders.

CONCLUSIONS

Currently, there are no recommendations for antipsychotic discontinuation in patients with first-episode schizophrenia spectrum disorders. However, there is a pressing need to conduct more rigorous research in remitted patients using more stringent criteria of full recovery, which can form the basis of guidelines on when and how antipsychotics should be tapered and discontinued. Studies that evaluate the patient characteristics and biomarkers that predict successful antipsychotic discontinuation are also needed.

A Prospective Study for Prediction of Psychotic Relapse Using the Korean Early Signs Scale in Patients With Schizophrenia

INTRODUCTION

A psychotic relapse of schizophrenia is commonly preceded by nonpsychotic behavioral symptoms and signs, and detection of these early signs may enable prevention of relapse of schizophrenia. This study aimed to test the predictive validity of a Korean version of Early Signs Scale (K-ESS) for psychotic relapse for detecting the early signs.

MATERIALS AND METHODS

In this multicenter noninterventional 52-week prospective study, outpatients diagnosed as having schizophrenia within 5 years were recruited. The K-ESS and Clinical Global Impression-Severity (CGI-S) scale were administered monthly until the end of the study or the relapse. The primary objective was to determine an optimal cutoff point of K-ESS score for prediction of psychotic relapse. The secondary objective was to assess the concurrent validity of the K-ESS using CGI-S scale.

RESULTS

Among the 162 included patients, 14 (8.6%) relapsed during the 52-week study period. The optimal cutoff score of K-ESS was 15 with a sensitivity of 71.43% and a specificity of 52.70%, indicating poor predictive accuracy of K-ESS. A lower cutoff K-ESS score of 3 and a higher cutoff score of 28 were found in the subgroups with milder (CGI-S = 1-2) and severer (CGI-S = 3-4) symptom severity, respectively, with fair to good predictive accuracy. The K-ESS showed acceptable concurrent validity with CGI-S and concordance rate between self-rated and informant-rated scores.

DISCUSSION

The predictive accuracy of K-ESS was limited by evaluation interval of a month. At least fortnightly follow-up would be needed for detection of early signs to prevent a psychotic relapse in schizophrenia.

Temporal Dynamics in Degree Centrality of Brain Functional Connectome in First-Episode Schizophrenia with Different Short-Term Treatment Responses: A Longitudinal Study

PURPOSE

This study investigated temporal dynamics in degree centrality (DC) of the brain functional connectome in first-episode schizophrenia with different short-term treatment responses.

METHODS

A total of 127 first-episode patients (FEPs) with schizophrenia and 133 healthy controls (HCs) were recruited in this study. All subjects underwent resting-state functional magnetic resonance imaging. FEPs were scanned at baseline (pretreatment) and at follow-up (posttreatment), while HCs were scanned only at baseline. The patients were exposed to naturalistic antipsychotic treatment for 12 weeks, and classified as schizophrenia responders (SRs) or nonresponders (NRs). Voxel-wise dynamic DC analyses were conducted among the SRs (n=75), NRs (n=52), and HCs (n=133) to assess temporal variability in functional connectivity across the entire neuronal network.

RESULTS

The SRs and NRs showed dissimilar dynamic DC at baseline, with differences mainly involving the temporal lobe. Different DC alteration was observed in the left fusiform gyrus, right fusiform gyrus, left middle cingulate cortex, and left superior parietal gyrus in the SRs and NRs pre- and posttreatment. SRs group and NRs presented opposite changing patterns of dynamic DC in particular regions of the brain.

CONCLUSION

These findings indicate that dynamic DC abnormalities exist in unmedicated patients with schizophrenia. The NRs differed from the SRs in dynamic DC not only at baseline but in the characteristics of changes before and after treatment as well. Our study may contribute to understanding pathophysiology in schizophrenia with different treatment responses.

Digital Health Around Clinical High Risk and First-Episode Psychosis

PURPOSE OF REVIEW

This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling.

RECENT FINDINGS

There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.