Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

AbstractDeficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.</jats:p

Treatment-Resistant Schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus Guidelines on Diagnosis and Terminology

OBJECTIVE: Research and clinical translation in schizophrenia is limited by inconsistent definitions of treatment resistance and response. To address this issue, the authors evaluated current approaches and then developed consensus criteria and guidelines. METHODS: A systematic review of randomized antipsychotic clinical trials in treatment-resistant schizophrenia was performed, and definitions of treatment resistance were extracted. Subsequently, consensus operationalized criteria were developed through 1) a multiphase, mixed methods approach, 2) identification of key criteria via an online survey, and 3) meetings to achieve consensus. RESULTS: Of 2,808 studies identified, 42 met inclusion criteria. Of these, 21 studies (50%) did not provide operationalized criteria. In the remaining studies, criteria varied considerably, particularly regarding symptom severity, prior treatment duration, and antipsychotic dosage thresholds; only two studies (5%) utilized the same criteria. The consensus group identified minimum and optimal criteria, employing the following principles: 1) current symptoms of a minimum duration and severity determined by a standardized rating scale; 2) moderate or worse functional impairment; 3) prior treatment consisting of at least two different antipsychotic trials, each for a minimum duration and dosage; 4) systematic monitoring of adherence and meeting of minimum adherence criteria; 5) ideally at least one prospective treatment trial; and 6) criteria that clearly separate responsive from treatment-resistant patients. CONCLUSIONS: There is considerable variation in current approaches to defining treatment resistance in schizophrenia. The authors present consensus guidelines that operationalize criteria for determining and reporting treatment resistance, adequate treatment, and treatment response, providing a benchmark for research and clinical translation

Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model

Background Disturbances in presynaptic dopamine activity and levels of gamma-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve first episode psychotic patients from healthy controls. Methods We included 23 patients (mean age 22.3 years, nine males) and 20 controls (mean age 22.4 years, eight males). We determined dopamine metabolism in nucleus accumbens (NAcc) and striatum from 18F-FDOPA positron emission tomography. We measured GABA levels in anterior cingulate cortex (ACC) and Glx levels in ACC and left thalamus with 3 Tesla 1H-MRS. We used binominal logistic regression for unimodal prediction when we modelled neurotransmitters individually, and for multimodal prediction when we combined the three neurotransmitters. We selected the best combination based on Akaike information criterion. Results Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p=0.003) and included dopamine synthesis capacity (Ki 4p) in NAcc (p=0.664), GABA levels in ACC (p=0.019), Glx levels in thalamus (p=0.678), and the interaction-term Ki 4pxGABA (p=0.016). Conclusion Our multimodal approach proved superior classification accuracy implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between Ki 4p in NAcc and GABA values in ACC appeared to contribute diagnostic information.Background: Disturbances in presynaptic dopamine activity and levels of GABA (gamma-aminobutyric acid) and glutamate plus glutamine collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve patients with first-episode psychosis from healthy control subjects. Methods: We included 23 patients (mean age 22.3 years, 9 male) and 20 control subjects (mean age 22.4 years, 8 male). We determined dopamine metabolism in the nucleus accumbens and striatum from 18F-fluorodopa ( 18F-FDOPA) positron emission tomography. We measured GABA levels in the anterior cingulate cortex (ACC) and glutamate plus glutamine levels in the ACC and left thalamus with 3T proton magnetic resonance spectroscopy. We used binominal logistic regression for unimodal prediction when we modeled neurotransmitters individually and for multimodal prediction when we combined the 3 neurotransmitters. We selected the best combination based on Akaike information criterion. Results: Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p = .003) and included dopamine synthesis capacity (K i 4p) in the nucleus accumbens (p = .664), GABA levels in the ACC (p = .019), glutamate plus glutamine levels in the thalamus (p = .678), and the interaction term K i 4p × GABA (p = .016). Conclusions: Our multimodal approach proved superior classification accuracy, implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between K i 4p in the nucleus accumbens and GABA values in the ACC appeared to contribute diagnostic information.</p

Mortality and HRQoL in ICU patients with delirium : Protocol for 1-year follow-up of AID-ICU trial

Background Intensive care unit (ICU)-acquired delirium is frequent and associated with poor short- and long-term outcomes for patients in ICUs. It therefore constitutes a major healthcare problem. Despite limited evidence, haloperidol is the most frequently used pharmacological intervention against ICU-acquired delirium. Agents intervening against Delirium in the ICU (AID-ICU) is an international, multicentre, randomised, blinded, placebo-controlled trial investigates benefits and harms of treatment with haloperidol in patients with ICU-acquired delirium. The current pre-planned one-year follow-up study of the AID-ICU trial population aims to explore the effects of haloperidol on one-year mortality and health related quality of life (HRQoL). Methods The AID-ICU trial will include 1000 participants. One-year mortality will be obtained from the trial sites; we will validate the vital status of Danish participants using the Danish National Health Data Registers. Mortality will be analysed by Cox-regression and visualized by Kaplan-Meier curves tested for significance using the log-rank test. We will obtain HRQoL data using the EQ-5D instrument. HRQoL analysis will be performed using a general linear model adjusted for stratification variables. Deceased participants will be designated the worst possible value. Results We expect to publish results of this study in 2022. Conclusion We expect that this one-year follow-up study of participants with ICU-acquired delirium allocated to haloperidol vs. placebo will provide important information on the long-term consequences of delirium including the effects of haloperidol. We expect that our results will improve the care of this vulnerable patient group.Peer reviewe

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4–6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication

Agents intervening against delirium in the intensive care unit (AID-ICU) - Protocol for a randomised placebo-controlled trial of haloperidol in patients with delirium in the ICU

Background Delirium among patients in the intensive care unit (ICU) is a common condition associated with increased morbidity and mortality. Haloperidol is the most frequently used pharmacologic intervention, but its use is not supported by firm evidence. Therefore, we are conducting Agents Intervening against Delirium in the Intensive Care Unit (AID‐ICU) trial to assess the benefits and harms of haloperidol for the treatment of ICU‐acquired delirium. Methods AID‐ICU is an investigator‐initiated, pragmatic, international, randomised, blinded, parallel‐group, trial allocating adult ICU patients with manifest delirium 1:1 to haloperidol or placebo. Trial participants will receive intravenous 2.5 mg haloperidol three times daily or matching placebo (isotonic saline 0.9%) if they are delirious. If needed, a maximum of 20 mg/daily haloperidol/placebo is given. An escape protocol, not including haloperidol, is part of the trial protocol. The primary outcome is days alive out of the hospital within 90 days post‐randomisation. Secondary outcomes are number of days without delirium or coma, serious adverse reactions to haloperidol, usage of escape medication, number of days alive without mechanical ventilation; mortality, health‐related quality‐of‐life and cognitive function at 1‐year follow‐up. A sample size of 1000 patients is required to detect a 7‐day improvement or worsening of the mean days alive out of the hospital, type 1 error risk of 5% and power 90%. Perspective The AID‐ICU trial is based on gold standard methodology applied to a large sample of clinically representative patients and will provide pivotal high‐quality data on the benefits and harms of haloperidol for the treatment ICU‐acquired delirium

PSYSCAN multi-centre study: baseline characteristics and clinical outcomes of the clinical high risk for psychosis sample

Predicting outcomes in individuals at clinical high risk (CHR) of developing psychosis remains challenging using clinical metrics alone. The PSYSCAN project aimed to enhance predictive value by integrating data across clinical, environmental, neuroimaging, cognitive, and peripheral blood biomarkers. PSYSCAN employed a naturalistic, prospective design across 12 sites (Europe, Australia, Asia, Americas). Assessments were conducted at baseline, 3, 6, and 12 months, with follow-ups at 18 and 24 months to evaluate clinical and functional outcomes. The study included 238 CHR individuals and 134 healthy controls (HC). At baseline, CHR and HC groups differed significantly in age, education, IQ, and vocational and relationship status. Cannabis and tobacco use did not significantly differ between groups, however CHR individuals had higher proportion of moderate to high risk of tobacco abuse. A substantial portion of the CHR sample met DSM criteria for anxiety (53.4%) and/or mood disorders (52.9%), with some prescribed antidepressants (38.7%), antipsychotics (13.9%), or benzodiazepines (16.4%). Over the follow-up period, 25 CHR individuals (10.5%) transitioned to psychosis. However, the CHR group as a whole showed improvements in functioning and attenuated psychotic symptoms. Similar to other recent multi-centre studies, the CHR cohort exhibits high comorbidity rates and relatively low psychosis transition rates. These findings highlight the clinical heterogeneity within CHR populations and suggest that outcomes extend beyond psychosis onset, reinforcing the need for broader prognostic models that consider functional and transdiagnostic outcomes

Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one’s children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines

Symptom Remission and Brain Cortical Networks at First Clinical Presentation of Psychosis: The OPTiMiSE Study

Individuals with psychoses have brain alterations, particularly in frontal and temporal cortices, that may be particularly prominent, already at illness onset, in those more likely to have poorer symptom remission following treatment with the first antipsychotic. The identification of strong neuroanatomical markers of symptom remission could thus facilitate stratification and individualized treatment of patients with schizophrenia. We used magnetic resonance imaging at baseline to examine brain regional and network correlates of subsequent symptomatic remission in 167 medication-naïve or minimally treated patients with first-episode schizophrenia, schizophreniform disorder, or schizoaffective disorder entering a three-phase trial, at seven sites. Patients in remission at the end of each phase were randomized to treatment as usual, with or without an adjunctive psycho-social intervention for medication adherence. The final follow-up visit was at 74 weeks. A total of 108 patients (70%) were in remission at Week 4, 85 (55%) at Week 22, and 97 (63%) at Week 74. We found no baseline regional differences in volumes, cortical thickness, surface area, or local gyrification between patients who did or did not achieved remission at any time point. However, patients not in remission at Week 74, at baseline showed reduced structural connectivity across frontal, anterior cingulate, and insular cortices. A similar pattern was evident in patients not in remission at Week 4 and Week 22, although not significantly. Lack of symptom remission in first-episode psychosis is not associated with regional brain alterations at illness onset. Instead, when the illness becomes a stable entity, its association with the altered organization of cortical gyrification becomes more defined