Predictors of treatment resistant schizophrenia: a systematic review of prospective observational studies

Overcoming the barriers to identifying and managing treatment-resistant schizophrenia and to improving access to clozapine: A narrative review and recommendation for clinical practice

Clozapine is the only approved antipsychotic for treatment-resistant schizophrenia (TRS). Although a large body of evidence supports its efficacy and favorable risk-benefit ratio in individuals who have failed two or more antipsychotics, clozapine remains underused. However, variations in clozapine utilization across geographic and clinical settings suggest that it could be possible to improve its use. In this narrative review and expert opinion, we summarized information available in the literature on the mechanisms of action, effectiveness, and potential adverse events of clozapine. We identified barriers leading to discouragement in clozapine prescription internationally, and we proposed practical solutions to overcome each barrier. One of the main obstacles identified to the use of clozapine is the lack of appropriate training for physicians: we highlighted the need to develop specific professional programs to train clinicians, both practicing and in residency, on the relevance and efficacy of clozapine in TRS treatment, initiation, maintenance, and management of potential adverse events. This approach would facilitate physicians to identify eligible patients and offer clozapine as a treatment option in the early stage of the disease. We also noted that increasing awareness of the benefits of clozapine among healthcare professionals, people with TRS, and their caregivers can help promote the use of clozapine. Educational material, such as leaflets or videos, could be developed and distributed to achieve this goal. The information provided in this article may be useful to improve disease burden and support healthcare professionals, patients, and caregivers navigating the complex pathways to TRS management.

Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features

Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS.

[Management of treatment resistance-Treatment-resistant schizophrenia]

Despite a very high prevalence and substantial impairments among affected individuals, treatment-resistant schizophrenia (TRS) has not been sufficiently researched in clinical research in the field of psychiatric disorders and the pathophysiology is still poorly understood. A better clinical and pathophysiological understanding of this heterogeneous and severely affected population of people with persistent symptoms in different domains is necessary in order not only to be able to intervene early but also to develop novel therapeutic strategies or individualized treatment approaches. This review article presents the state of the art criteria of the pharmacological TRS, neurobiological disease models and predictive factors for TRS as well as the phenomenon of pseudo-treatment resistance and the clinical management of TRS. In the future, not only the use of operationalized criteria and definitions of TRS in longitudinal studies and randomized-controlled trials (RCTs) are paramount, but also the observation of trajectories with the integration of multimodal longitudinal phenotyping and the longitudinal collection of clinical routine data in academic research, which will be possible in the newly created German Center for Mental Health (DZPG).

Identification and treatment of individuals with childhood-onset and early-onset schizophrenia

Approximately 8 % of patients with schizophrenia are diagnosed before age 18, and 18 % experience their first symptoms before age 18. This narrative review explores the management of patients with early-onset schizophrenia (EOS) and childhood-onset schizophrenia (COS) from diagnosis to their transition to adult care settings. Early diagnosis of schizophrenia in children and adolescents is essential for improving outcomes, but delays are common due to overlapping of symptoms with developmental phenomena and other psychiatric conditions, including substance use, and lack of clinicians' awareness. Once diagnosed, antipsychotic treatment is key, with specific second-generation agents generally being preferred due to better tolerability and their broader efficacy evidence-base in youth. Dosing should be carefully individualized, considering age-related differences in drug metabolism and side effect liability. Clinicians must be vigilant in detecting early non-response and consider switching or dose escalation when appropriate. Since early age of illness onset is a consistent risk factor for treatment-resistant schizophrenia (TRS), clinicians need to be competent in diagnosing TRS and using clozapine. Since COS and EOS are associated with cognitive deficits and impaired functioning, psychosocial interventions should be considered to improve overall functioning and quality of life. Good long-term outcomes depend on continuous treatment engagement, and successful transitioning from pediatric to adult care requires careful planning, early preparation, and collaboration between pediatric and adult clinicians. Targeting functional outcomes and quality of life in addition to symptom remission can improve overall patient well-being. Comprehensive evaluations, age-specific assessments, and targeted interventions are needed to address the unique challenges of EOS and COS.

Variations to plasma H2O2 levels and TAC in chronical medicated and treatment-resistant male schizophrenia patients: Correlations with psychopathology

Accumulating evidence suggests that imbalanced oxidative stress (OS) may contribute to the mechanism of schizophrenia. The aim of the present study was to evaluate the associations of OS parameters with psychopathological symptoms in male chronically medicated schizophrenia (CMS) and treatment-resistant schizophrenia (TRS) patients. Levels of hydrogen peroxide (H2O2), hydroxyl radical (·OH), peroxidase (POD), α-tocopherol (α-toc), total antioxidant capacity (TAC), matrix metalloproteinase-9 (MMP-9), and tissue inhibitor of metalloproteinases-1 (TIMP-1) were assayed in males with CMS and TRS, and matched healthy controls. Schizophrenia symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS). The results demonstrated significant differences in the variables H2O2 (F = 5.068, p = 0.008), ·OH (F = 31.856, p < 0.001), POD (F = 14.043, p < 0.001), α-toc (F = 3.711, p = 0.027), TAC (F = 24.098, p < 0.001), and MMP-9 (F = 3.219, p = 0.043) between TRS and CMS patients and healthy controls. For TRS patients, H2O2 levels were correlated to the PANSS positive subscale (r = 0.386, p = 0.032) and smoking (r = -0,412, p = 0.021), while TAC was significantly negatively correlated to the PANSS total score (r = -0.578, p = 0.001) and POD and TAC levels were positively correlated to body mass index (r = 0.412 and 0.357, p = 0.021 and 0.049, respectively). For patients with CMS, ·OH levels and TAC were positively correlated to the PANSS general subscale (r = 0.308, p = 0.031) and negatively correlated to the PANSS total score (r = -0.543, p < 0.001). Furthermore, H2O2, α-toc, and ·OH may be protective factors against TRS, and POD was a risk factor. Patients with CMS and TRS exhibit an imbalance in OS, thus warranting future investigations.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

Improving outcome of treatment-resistant schizophrenia: effects of cognitive remediation therapy

Treatment-Resistant Schizophrenia (TRS) represents a main clinical issue, associated with worse psychopathological outcomes, a more disrupted neurobiological substrate, and poorer neurocognitive performance across several domains, especially in verbal abilities. If cognitive impairment is a major determinant of patients' functional outcomes and quality of life, targeting cognitive dysfunction becomes even more crucial in TRS patients in order to minimize cognitive and functional deterioration. However, although Cognitive Remediation Therapy (CRT) represents the best available tool to treat cognitive dysfunction in schizophrenia, specific evidence of its efficacy in TRS is lacking. Based on these premises, our study aimed at investigating possible differences in CRT outcomes in a sample of 150 patients with schizophrenia, stratified according to antipsychotic response (TRS vs. non-TRS). Subjects were assessed for neurocognition through Brief Assessment of Cognition in Schizophrenia (BACS) and the Wisconsin Card Sorting Test (WCST) at baseline and after CRT. As expected, we observed greater baseline impairment among TRS patients in BACS-Verbal Memory and WCST-Executive Functions. Repeated measures ANCOVAs showed significant within-group pre-/post-CRT differences in the above-mentioned domains, both among non-TRS and TRS subjects. However, after CRT, no differences were observed between groups. This is the first study to indicate that CRT represents a highly valuable resource for TRS patients, since it may be able to fill the cognitive gap between treatment response groups. Our finding further highlights the importance of early implementation of CRT in addition to pharmacotherapy to reduce the cognitive and functional burden associated with the disease, especially for TRS patients.

The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach

BACKGROUND

Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future.

STUDY DESIGN

Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction.

STUDY RESULTS

Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69).

CONCLUSIONS

We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

20-year trajectories of positive and negative symptoms after the first psychotic episode in patients with schizophrenia spectrum disorder: results from the OPUS study

This study aimed to identify the 20-year trajectories of positive and negative symptoms after the first psychotic episode in a sample of patients with an ICD-10 diagnosis of schizophrenia spectrum disorder, and to investigate the baseline characteristics and long-term outcomes associated with these trajectories. A total of 373 participants in the OPUS trial were included in the study. Symptoms were assessed at baseline and after 1, 2, 5, 10 and 20 years using the Scales for the Assessment of Positive and Negative Symptoms. We used latent class growth mixture modelling to identify trajectories, and multinominal regression analyses to investigate predictors of membership to identified trajectories. Five trajectories of positive symptoms were identified: early continuous remission (50.9% of the sample), stable improvement (18.0%), intermittent symptoms (10.2%), relapse with moderate symptoms (11.9%), and continuous severe symptoms (9.1%). Substance use disorder (odds ratio, OR: 2.83, 95% CI: 1.09-7.38, p=0.033), longer duration of untreated psychosis (OR: 1.02, 95% CI: 1.00-1.03, p=0.007) and higher level of negative symptoms (OR: 1.60, 95% CI: 1.07-2.39, p=0.021) were predictors of the relapse with moderate symptoms trajectory, while only longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.030) predicted membership to the continuous severe symptoms trajectory. Two trajectories of negative symptoms were identified: symptom remission (51.0%) and continuous symptoms (49.0%). Predictors of the continuous symptoms trajectory were male sex (OR: 3.03, 95% CI: 1.48-6.02, p=0.002) and longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.034). Trajectories displaying continuous positive and negative symptoms were linked to lower neurocognition, as measured by the Brief Assessment of Cognition in Schizophrenia (BACS) (z-score: -0.78, CI: -1.39 to -0.17, for continuous positive symptoms; z-score: -0.33, CI: -0.53 to -0.13, for continuous negative symptoms). The same trajectories were also linked to higher use of antipsychotic medication at 20-year follow-up (continuous positive symptoms: 78%; continuous negative symptoms: 67%). These findings suggest that the majority of patients with first-episode schizophrenia spectrum disorder have a trajectory with early stable remission of positive symptoms. Long duration of untreated psychosis and comorbid substance abuse are modifiable predictors of poor trajectories for positive symptoms in these patients. In about half of patients, negative symptoms do not improve over time. These symptoms, in addition to being associated with poor social and neurocognitive functioning, may prevent patients from seeking help.

The Involvement of PGRMC1 Signaling in Cognitive Impairment Induced by Long-Term Clozapine Treatment in Rats

INTRODUCTION

Progesterone receptor component 1 (PGRMC1) has been identified as a potential target in atypical antipsychotic drug-induced metabolic disturbances as well as neuroprotection in the central nervous system. In our study, we aimed to figure out the essential role of PGRMC1 signaling pathway underlying clozapine-induced cognitive impairment.

METHODS

In male SD rats, we utilized recombinant adeno-associated viruses (BBB 2.0) and the specific inhibitor of PGRMC1 (AG205) to regulate the expression of PGRMC1 in the brain, with a special focus on the hippocampus. Treatments of clozapine and AG205 were conducted for 28 days, and subsequent behavioral tests including modified elevated plus maze and Morris water maze were conducted to evaluate the cognitive performance. Hippocampal protein expressions were measured by Western blotting.

RESULTS

Our study showed that long-term clozapine administration led to cognitive impairment as confirmed by behavioral tests as well as histopathological examination in the hippocampus. Clozapine inhibited neural survival through the PGRMC1/EGFR/GLP1R-PI3K-Akt signaling pathway, leading to a decrease in the downstream survival factor, brain-derived neurotrophic factor (BDNF), and simultaneously promoted neural apoptosis in the rat hippocampus. Intriguingly, by targeting at the hippocampal PGRMC1, we found that inhibiting PGRMC1 mimics, while its upregulation notably mitigates clozapine-induced cognitive impairment through PGRMC1 and its downstream signaling pathways.

CONCLUSION

PGRMC1-overexpression could protect hippocampus-dependent cognitive impairment induced by clozapine. This effect appears to arise, in part, from the upregulated expression of PGRMC1/EGFR/GLP1R and the activation of downstream PI3K-Akt-BDNF and caspase-3 signaling pathways.

Increased Prevalence of Rare Copy Number Variants in Treatment-Resistant Psychosis

BACKGROUND

It remains unknown why ~30% of patients with psychotic disorders fail to respond to treatment. Previous genomic investigations of treatment-resistant psychosis have been inconclusive, but some evidence suggests a possible link between rare disease-associated copy number variants (CNVs) and worse clinical outcomes in schizophrenia. Here, we identified schizophrenia-associated CNVs in patients with treatment-resistant psychotic symptoms and then compared the prevalence of these CNVs to previously published schizophrenia cases not selected for treatment resistance.

METHODS

CNVs were identified using chromosomal microarray (CMA) and whole exome sequencing (WES) in 509 patients with treatment-resistant psychosis (a lack of clinical response to ≥3 adequate antipsychotic medication trials over at least 5 years of psychiatric hospitalization). Prevalence of schizophrenia-associated CNVs in this sample was compared to that in a previously published large schizophrenia cohort study.

RESULTS

Integrating CMA and WES data, we identified 47 cases (9.2%) with at least one CNV of known or possible neuropsychiatric risk. 4.7% (n = 24) carried a known neurodevelopmental risk CNV. The prevalence of well-replicated schizophrenia-associated CNVs was 4.1%, with duplications of the 16p11.2 and 15q11.2-q13.1 regions, and deletions of the 22q11.2 chromosomal region as the most frequent CNVs. Pairwise loci-based analysis identified duplications of 15q11.2-q13.1 to be independently associated with treatment resistance.

CONCLUSIONS

These findings suggest that CNVs may uniquely impact clinical phenotypes beyond increasing risk for schizophrenia and may potentially serve as biological entry points for studying treatment resistance. Further investigation will be necessary to elucidate the spectrum of phenotypic characteristics observed in adult psychiatric patients with disease-associated CNVs.

Developing prediction models for symptom severity around the time of discharge from a tertiary-care program for treatment-resistant psychosis

Antipsychotics are the only therapeutic class indicated in the symptomatic management of psychotic disorders. However, individuals diagnosed with schizophrenia or schizoaffective disorder may not always benefit from these first-line agents. This refractoriness to conventional treatment can be difficult to address in most clinical settings. Therefore, a referral to a tertiary-care program that is better able to deliver specialized care in excess of the needs of most individuals may be necessary. The average outcome following a period of treatment at these programs tends to be one of improvement. Nonetheless, accurate prognostication of individual-level responses may be useful in identifying those who are unlikely to improve despite receiving specialized care. Thus, the main objective of this study was to predict symptom severity around the time of discharge from the Refractory Psychosis Program in British Columbia, Canada using only clinicodemographic information and prescription drug data available at the time of admission. To this end, a different boosted beta regression model was trained to predict the total score on each of the five factors of the Positive and Negative Syndrome Scale (PANSS) using a data set composed of 320 hospital admissions. Internal validation of these prediction models was then accomplished by nested cross-validation. Insofar as it is possible to make comparisons of model performance across different outcomes, the correlation between predictions and observations tended to be higher for the negative and disorganized factors than the positive, excited, and depressed factors on internal validation. Past scores had the greatest effect on the prediction of future scores across all 5 factors. The results of this study serve as a proof of concept for the prediction of symptom severity using this specific approach.

Resting-state functional MRI in treatment-resistant schizophrenia

BACKGROUND

Abnormalities in brain regions involved in the pathophysiology of schizophrenia (SCZ) may present insight into individual clinical symptoms. Specifically, functional connectivity irregularities may provide potential biomarkers for treatment response or treatment resistance, as such changes can occur before any structural changes are visible. We reviewed resting-state functional magnetic resonance imaging (rs-fMRI) findings from the last decade to provide an overview of the current knowledge on brain functional connectivity abnormalities and their associations to symptoms in treatment-resistant schizophrenia (TRS) and ultra-treatment-resistant schizophrenia (UTRS) and to look for support for the dysconnection hypothesis.

METHODS

PubMed database was searched for articles published in the last 10 years applying rs-fMRI in TRS patients, i.e., who had not responded to at least two adequate treatment trials with different antipsychotic drugs.

RESULTS

Eighteen articles were selected for this review involving 648 participants (TRS and control cohorts). The studies showed frontal hypoconnectivity before the initiation of treatment with CLZ or riluzole, an increase in frontal connectivity after riluzole treatment, fronto-temporal hypoconnectivity that may be specific for non-responders, widespread abnormal connectivity during mixed treatments, and ECT-induced effects on the limbic system.

CONCLUSION

Probably due to the heterogeneity in the patient cohorts concerning antipsychotic treatment and other clinical variables (e.g., treatment response, lifetime antipsychotic drug exposure, duration of illness, treatment adherence), widespread abnormalities in connectivity were noted. However, irregularities in frontal brain regions, especially in the prefrontal cortex, were noted which are consistent with previous SCZ literature and the dysconnectivity hypothesis. There were major limitations, as most studies did not differentiate between TRS and UTRS (i.e., CLZ-resistant schizophrenia) and investigated heterogeneous cohorts treated with mixed treatments (with or without CLZ). This is critical as in different subtypes of the disorder an interplay between dopaminergic and glutamatergic pathways involving frontal, striatal, and hippocampal brain regions in separate ways is likely. Better definitions of TRS and UTRS are necessary in future longitudinal studies to correctly differentiate brain regions underlying the pathophysiology of SCZ, which could serve as potential functional biomarkers for treatment resistance.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Genome-wide methylation analysis of treatment resistant schizophrenia

Various studies have investigated the relationship between genetic polymorphisms of antipsychotic drug-metabolizing agents and drug response. DNA methylation is a form of epigenetic modification that regulates gene expression. Few studies have analyzed the relationship between genome-wide methylation patterns and treatment resistance schizophrenia. The primary aim of this pilot study is to investigate the association between treatment resistance status and genome-wide DNA methylation in schizophrenia patients. Treatment resistance status was determined for 109 patients with schizophrenia. Treatment resistance was the primary outcome variable in a model, including methylation status of white blood cells using the Illumina 450 array. The genome-wide DNA methylation levels in 109 Schizophrenia subjects did not show that DNA methylation sties were associated with resistance status. From our study, it is evident the importance of continuing to investigate the relationship between DNA methylation and antipsychotic response to personalize treatment in schizophrenia. Future studies require larger prescription databases to build on the results presented in this pilot study.

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

Clozapine research from India: A systematic review

BACKGROUND

Although clozapine is much researched in western literature, a review on Indian research on clozapine published in 2010 reported limited data and need for further research in this area.

AIM

We aimed to conduct a systematic review of research on clozapine from India from 2010 to mid-2022 and also compare the same with research output before 2010.

METHODOLOGY

A systematic various search engines, i.e., PUBMED, Medknow, Hinari and Google Scholar was done using the key words clozapine and India. Published articles with clozapine in the title and having an author from India, published during 2010 to July 2022 were included.

RESULTS

Initial Internet and hand searches yielded 280 articles, out of which 126 articles were excluded due to various reasons and 154 articles, were included for the review. This included 84 case reports, 49 original articles, 11 review articles and 10 letters to the editor as comments. We found an increase in the number of publications during the period of 2010-2022 compared to 1997-2009 in all types of publications. Over the years a significant proportion of the articles focused on various side effects of clozapine, factors associated with response and non-response to clozapine and evaluation of outcomes other than efficacy/effectiveness. However, all the studies were limited to a single centre with no multicentric studies on clozapine.

CONCLUSION

Over the last 12 years or so, there is increase in the number of publications on clozapine. However, there is lack of multicentric studies.

Catalase and interleukin-6 serum elevation in a prediction of treatment-resistance in male schizophrenia patients

BACKGROUND

Oxidative stress (OS) and neuroinflammatory pathways play an important role in the pathophysiology of schizophrenia. The present study investigated the relationship between OS, inflammatory cytokines, and clinical features in male patients with treatment-resistant schizophrenia (TRS).

METHOD

We measured plasma OS parameters, including manganese-superoxide dismutase (Mn-SOD), copper/zinc-containing SOD (CuZn-SOD), total-SOD (T-SOD), malondialdehyde (MDA), catalase (CAT), and glutathione peroxidase (GSH-Px); and serum inflammatory cytokines, including interleukin (IL)- 1α, IL-6, tumor necrosis factor-alpha (TNF-α), and interferon (IFN)-γ, from 80 male patients with chronic schizophrenia (31 had TRS and 49 had chronic stable schizophrenia (CSS)), and 42 healthy controls. The severity of psychotic symptoms was evaluated using the Positive and Negative Syndrome Scale (PANSS).

RESULTS

Compared with healthy controls, plasma Mn-SOD, CuZn-SOD, T-SOD, GSH-Px, and MDA levels were significantly lower, while CAT and serum IL-6 levels were higher in both TRS and CSS male patients (all P < 0.05). Significant differences in the activities of CAT (F = 6.068, P = 0.016) and IL-6 levels (F = 6.876, P = 0.011) were observed between TRS and CSS male patients after analysis of covariance. Moreover, a significant positive correlation was found between IL-6 levels and PANSS general psychopathology subscores (r = 0.485, P = 0.006) and between CAT activity and PANSS total scores (r = 0.409, P = 0.022) in TRS male patients. CAT and IL-6 levels were predictors for TRS. Additionally, in chronic schizophrenia patients, a significant positive correlation was observed between IL-6 and GSH-Px (r = 0.292, P = 0.012), and the interaction effect of IL-6 and GSH-Px was positively associated with PANSS general psychopathology scores (r = 0.287, P = 0.014).

CONCLUSION

This preliminary study indicated that variations in OS and inflammatory cytokines may be involved in psychopathology for patients with chronic schizophrenia, especially in male patients with TRS.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

Disorganization domain as a putative predictor of Treatment Resistant Schizophrenia (TRS) diagnosis: A machine learning approach

BACKGROUND

Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS.

METHODS

In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model.

RESULTS

according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.

Naturalistic study on the use of clozapine in the early phases of non-affective psychosis: A 10-year follow-up study in the PAFIP-10 cohort

Clozapine is seldom prescribed in treatment-resistant schizophrenia (TRS) patients during early phases of the illness. We aimed to examine the pathway and patterns and the impact of clozapine use in patients with TRS who were followed up for 10 years after the first outbreak of the illness. Data were obtained retrospectively from an epidemiological cohort of first episode schizophrenia patients (n = 218) who had been treated in a specialized intervention program (PAFIP). Out of 218, 35 (16%) individuals were on clozapine at 10-year assessment, while 183 (84%) were taking other antipsychotics. Among those 183 psychosis subjects who were not on clozapine, 13 (7.1%) met criteria for TRS. In the clozapine group, ten (28.6%) met criteria for early-TR and twenty-five (71.4%) met criteria for late-TR. Before clozapine treatment was initiated, the median number of days under other antipsychotic treatment was 1551 days (IQR = 1715) and the median time that subjects remained on clozapine was 6.3 years (IC95%: 5.49-7.20). At 10 years, we found that those individuals taking clozapine had higher CGI total scores (F = 12.0, p = 0.001) and SANS total scores (F = 9.27, p = 0.003) than subjects taking other antipsychotics after correcting for baseline values. Interestingly, when performing these analyses at 10 years between subjects taking clozapine (n = 35) and subjects who despite meeting TRS criteria were not taking clozapine (n = 13), we found that subjects taking clozapine had significantly lower total scores on all clinical scales compared with subjects who met TRS criteria and were not taking clozapine (p values < 0.05). TRS patients who took the longest time to start clozapine (third tertile) showed significantly higher CGI scores at 10-year follow-up compared to those who initiated clozapine earlier (first tertile) (t = 2.60; p = 0.043). Our findings reinforce the need of a timely assessment of treatment-resistant criteria in early schizophrenia patients and highlight the long-term benefits of an early introduction of clozapine on those patients meeting treatment-resistant criteria.

Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.

Treatment Approaches for First Episode and Early-Phase Schizophrenia in Adolescents and Young Adults: A Delphi Consensus Report from Europe

PURPOSE

Although first-episode psychosis (FEP) in youth, particularly early-onset schizophrenia (EOS), is managed similarly to adult-onset schizophrenia, few antipsychotics are approved for people aged 13-18 years. We aimed to explore areas of uncertainty in EOS management and provide evidence-based recommendations to mental health specialists. We used the Delphi methodology to gain knowledge in areas lacking evidence-based strategies. This standardized methodology consists of the development of a questionnaire by content experts, which is then submitted to a broader panel of professionals (panelists) to survey their level of agreement on the topics proposed.

MATERIALS AND METHODS

The developed questionnaire covered patient management from diagnosis to maintenance treatment and was administered to a broader panel of specialists across Europe. Based on an analysis of responses received in this first round, the items that needed further insight were submitted to the panel for a second round and then reanalysed.

RESULTS

An initial set of 90 items was developed; in round I, consensus was reached for 83/90 items (92%), while it was reached for 7/11 (64%) of the items sent out for rerating in round II. Feedback for rounds I and II was obtained from 54/92 and 48/54 approached experts, respectively. There was broad agreement on diagnostic standards, multimodal approaches and focus on adverse events, but uncertainty in terms of pharmacological strategies (including clozapine) in case of failure and antipsychotic dosing in younger patients.

CONCLUSION

Despite knowledge about diagnostic clues and integrated management of EOS, this study highlights the lack of standardization in treating EOS, with safety arguments having a major role in the decision-making process. Targeted clinical trials and systematic dissemination across Europe of current scientific evidence on the value of early intervention services is hoped to contribute to standardized and improved quality care for patients with early-phase psychosis and schizophrenia.

Cross-sectional study comparing cognitive function in treatment responsive versus treatment non-responsive schizophrenia: evidence from the STRATA study

BACKGROUND

70%-84% of individuals with antipsychotic treatment resistance show non-response from the first episode. Emerging cross-sectional evidence comparing cognitive profiles in treatment resistant schizophrenia to treatment-responsive schizophrenia has indicated that verbal memory and language functions may be more impaired in treatment resistance. We sought to confirm this finding by comparing cognitive performance between antipsychotic non-responders (NR) and responders (R) using a brief cognitive battery for schizophrenia, with a primary focus on verbal tasks compared against other measures of cognition.

DESIGN

Cross-sectional.

SETTING

This cross-sectional study recruited antipsychotic treatment R and antipsychotic NR across four UK sites. Cognitive performance was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS).

PARTICIPANTS

One hundred and six participants aged 18-65 years with a diagnosis of schizophrenia or schizophreniform disorder were recruited according to their treatment response, with 52 NR and 54 R cases.

OUTCOMES

Composite and subscale scores of cognitive performance on the BACS. Group (R vs NR) differences in cognitive scores were investigated using univariable and multivariable linear regressions adjusted for age, gender and illness duration.

RESULTS

Univariable regression models observed no significant differences between R and NR groups on any measure of the BACS, including verbal memory (ß=-1.99, 95% CI -6.63 to 2.66, p=0.398) and verbal fluency (ß=1.23, 95% CI -2.46 to 4.91, p=0.510). This pattern of findings was consistent in multivariable models.

CONCLUSIONS

The lack of group difference in cognition in our sample is likely due to a lack of clinical distinction between our groups. Future investigations should aim to use machine learning methods using longitudinal first episode samples to identify responder subtypes within schizophrenia, and how cognitive factors may interact within this.

TRAIL REGISTRATION NUMBER

REC: 15/LO/0038.

Structural Covariance of Cortical Gyrification at Illness Onset in Treatment Resistance: A Longitudinal Study of First-Episode Psychoses

Treatment resistance (TR) in patients with first-episode psychosis (FEP) is a major cause of disability and functional impairment, yet mechanisms underlying this severe disorder are poorly understood. As one view is that TR has neurodevelopmental roots, we investigated whether its emergence relates to disruptions in synchronized cortical maturation quantified using gyrification-based connectomes. Seventy patients with FEP evaluated at their first presentation to psychiatric services were followed up using clinical records for 4 years; of these, 17 (24.3%) met the definition of TR and 53 (75.7%) remained non-TR at 4 years. Structural MRI images were obtained within 5 weeks from first exposure to antipsychotics. Local gyrification indices were computed for 148 contiguous cortical regions using FreeSurfer; each subject's contribution to group-based structural covariance was quantified using a jack-knife procedure, providing a single deviation matrix for each subject. The latter was used to derive topological properties that were compared between TR and non-TR patients using a Functional Data Analysis approach. Compared to the non-TR patients, TR patients showed a significant reduction in small-worldness (Hedges's g = 2.09, P < .001) and a reduced clustering coefficient (Hedges's g = 1.07, P < .001) with increased length (Hedges's g = -2.17, P < .001), indicating a disruption in the organizing principles of cortical folding. The positive symptom burden was higher in patients with more pronounced small-worldness (r = .41, P = .001) across the entire sample. The trajectory of synchronized cortical development inferred from baseline MRI-based structural covariance highlights the possibility of identifying patients at high-risk of TR prospectively, based on individualized gyrification-based connectomes.

Associations of Clozapine Use With Psychosocial Functioning and Quality of Life in Patients With Schizophrenia: A Community-Based Cross-Sectional Study

OBJECTIVE

More attempts have been made recently to improve psychosocial functioning and quality of life in patients with schizophrenia, due to their crucial role in long-term outcomes. Previous studies on the effects of clozapine on psychosocial functioning have been limited in terms of generalizability and application to clinical practice. This study examined the relationship of clozapine use with psychosocial functioning and quality of life in patients with schizophrenia in a real-world setting.

METHODS

Data were obtained from a survey targeting community-dwelling patients with schizophrenia. The Behavior and Symptom Identification Scale (BASIS) and Satisfaction with Life Scale (SWLS) were administered to evaluate psychosocial functioning and quality of life, and patients were classified into Clozapine and Non-clozapine groups. Group differences were assessed using ANCOVA, with additional sensitivity analyses for participants on atypical antipsychotic medications only.

RESULTS

Of 292 patients, the Clozapine group (n=34) had significantly better psychosocial functioning and quality of life than the Nonclozapine group (n=258), as demonstrated by their low BASIS score (F=4.651, df=1, 290, p=0.032) and high SWLS score (F=14.637, df=1, 290, p<0.001). Similar findings for psychosocial outcomes were observed in the analyses of the atypical antipsychotic subgroup (n=195).

CONCLUSION

For optimal recovery in schizophrenia, restoration of impaired social functioning and enhanced satisfaction with life are essential. In this study, clozapine use was related to high levels of psychosocial functioning and quality of life in real-world settings. Further research on the causal relationship between clozapine use and psychosocial functioning is needed.

Prevalence and predictors of treatment-resistant schizophrenia in a tertiary hospital in Northeast Brazil

OBJECTIVE

To investigate epidemiological factors related to treatment-resistant schizophrenia (TRS) in Northeast Brazil, a region where data about mental health are still scarce.

METHODS

This retrospective cross-sectional study included all patients with schizophrenia currently receiving treatment at the outpatient psychiatric clinic of a tertiary hospital in Northeast Brazil. They were divided into TRS and treatment-responsive groups, and epidemiological characteristics of both groups were compared. A logistic regression model investigated factors related to treatment resistance.

RESULTS

Two hundred and five patients were included, 155 treatment-resistant and 50 treatment-responsive. The TRS group had higher use of benzodiazepines (36.1 vs. 18%, p = 0.017) and antiepileptics (36.8 vs. 8.0%, p < 0.001), antipsychotic polypharmacy (28.6 vs. 8%, p = 0.003) and suicide attempts (35.6 vs. 20%, p = 0.04). Age at onset was younger (19.7±7.3 vs. 24.6±8.6 years, p = 0.001) and CGI was higher in TRS (3.72±1.00 vs. 3.16±1.00, p = 0.001). In logistic regression, being married was a protector (odds ratio [OR] = 0.248, 95% confidence interval [95%CI] 0.091-0.679, p = 0.007) and younger age at onset was a predictor (OR = 1.076, 95%CI 1.034-1.120, p < 0.001) of treatment resistance.

CONCLUSION

Early onset of disease was associated with more treatment resistance, while being married with less resistance. Clinicians should identify early predictors of resistance in order to reduce unfavorable outcomes.

Prevalence and predictors of treatment-resistant schizophrenia in a tertiary hospital in Northeast Brazil

OBJECTIVE

To investigate epidemiological factors related to treatment-resistant schizophrenia (TRS) in Northeast Brazil, a region where data about mental health are still scarce.

METHODS

This retrospective cross-sectional study included all patients with schizophrenia currently receiving treatment at the outpatient psychiatric clinic of a tertiary hospital in Northeast Brazil. They were divided into TRS and treatment-responsive groups, and epidemiological characteristics of both groups were compared. A logistic regression model investigated factors related to treatment resistance.

RESULTS

Two hundred and five patients were included, 155 treatment-resistant and 50 treatment-responsive. The TRS group had higher use of benzodiazepines (36.1 vs. 18%, p = 0.017) and antiepileptics (36.8 vs. 8.0%, p < 0.001), antipsychotic polypharmacy (28.6 vs. 8%, p = 0.003) and suicide attempts (35.6 vs. 20%, p = 0.04). Age at onset was younger (19.7±7.3 vs. 24.6±8.6 years, p = 0.001) and CGI was higher in TRS (3.72±1.00 vs. 3.16±1.00, p = 0.001). In logistic regression, being married was a protector (odds ratio [OR] = 0.248, 95% confidence interval [95%CI] 0.091-0.679, p = 0.007) and younger age at onset was a predictor (OR = 1.076, 95%CI 1.034-1.120, p < 0.001) of treatment resistance.

CONCLUSION

Early onset of disease was associated with more treatment resistance, while being married with less resistance. Clinicians should identify early predictors of resistance in order to reduce unfavorable outcomes.

Gender disparities in clozapine prescription in a cohort of treatment-resistant schizophrenia in the South London and Maudsley case register

BACKGROUND

Gender disparities in treatment are apparent across many areas of healthcare. There has been little research into whether clozapine prescription, the first-line treatment for treatment-resistant schizophrenia (TRS), is affected by patient gender.

METHODS

This retrospective cohort study identified 2244 patients with TRS within the South London and Maudsley NHS Trust, by using a bespoke method validated against a gold-standard, manually coded, dataset of TRS cases. The outcome and exposures were identified from the free-text using natural language processing applications (including machine learning and rules-based approaches) and from information entered in structured fields. Multivariable logistic regression was carried out to calculate the odds ratios for clozapine prescription according to patients' gender, and adjusting for numerous potential confounders including sociodemographic, clinical (e.g., psychiatric comorbidities and substance use), neutropenia, functional factors (e.g., problems with occupation), and clinical monitoring.

RESULTS

Clozapine was prescribed to 77% of the women and 85% of the men with TRS. Women had reduced odds of being prescribed clozapine as compared to men after adjusting for all factors included in the present study (adjusted OR: 0.66; 95% CI 0.44-0.97; p = 0.037).

CONCLUSION

Women with TRS are less likely to be prescribed clozapine than men with TRS, even when considering the effects of multiple clinical and functional factors. This finding suggests there could be gender bias in clozapine prescription, which carries ramifications for the relatively poorer care of women with TRS regarding many outcomes such as increased hospitalisation, mortality, and poorer quality of life.

Novel ultra-rare exonic variants identified in a founder population implicate cadherins in schizophrenia

The identification of rare variants associated with schizophrenia has proven challenging due to genetic heterogeneity, which is reduced in founder populations. In samples from the Ashkenazi Jewish population, we report that schizophrenia cases had a greater frequency of novel missense or loss of function (MisLoF) ultra-rare variants (URVs) compared to controls, and the MisLoF URV burden was inversely correlated with polygenic risk scores in cases. Characterizing 141 "case-only" genes (MisLoF URVs in ≥3 cases with none in controls), the cadherin gene set was associated with schizophrenia. We report a recurrent case mutation in PCDHA3 that results in the formation of cytoplasmic aggregates and failure to engage in homophilic interactions on the plasma membrane in cultured cells. Modeling purifying selection, we demonstrate that deleterious URVs are greatly overrepresented in the Ashkenazi population, yielding enhanced power for association studies. Identification of the cadherin/protocadherin family as risk genes helps specify the synaptic abnormalities central to schizophrenia.

Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

Predicting onset of early- and late-treatment resistance in first-episode schizophrenia patients using advanced shrinkage statistical methods in a small sample

Evidence suggests there are two treatment-resistant schizophrenia subtypes (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR)). We aimed to develop prediction models for estimating individual risk for these outcomes by employing advanced statistical shrinkage methods. 239 first-episode schizophrenia (FES) patients were followed-up for approximately 5 years after first presentation to psychiatric services; of these, n=56 (25.2%) were defined as E-TR and n=24 (12.6%) were defined as L-TR. Using known risk factors for poor schizophrenia outcomes, we developed prediction models for E-TR and L-TR using LASSO and RIDGE logistic regression models. Models' internal validation was performed employing Harrell's optimism-correction with repeated cross-validation; their predictive accuracy was assessed through discrimination and calibration. Both LASSO and RIDGE models had high discrimination, good calibration. While LASSO had moderate sensitivity for estimating an individual risk for E-TR and L-TR, sensitivity estimated for RIDGE model for these outcomes was extremely low, which was due to having a very large estimated optimism. Although it was possible to discriminate with sufficient accuracy who would meet criteria for E-TR and L-TR during the 5-year follow-up after first contact with mental health services for schizophrenia, further work is necessary to improve sensitivity for these models.

Longitudinal trajectory analysis of antipsychotic response in patients with schizophrenia: 6-week, randomised, open-label, multicentre clinical trial

BACKGROUND

Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.

AIMS

This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).

METHOD

Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.

RESULTS

The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.

CONCLUSIONS

The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.

Predictors of Treatment-Resistant and Clozapine-Resistant Schizophrenia: A 12-Year Follow-up Study of First-Episode Schizophrenia-Spectrum Disorders

Studies on the long-term development and early predictors of treatment-resistant schizophrenia (TRS) and clozapine-resistant TRS (CR-TRS) in patients with first-episode schizophrenia-spectrum disorders (FES) are limited and have not considered the impact of early intervention services (EIS). This study aimed to explore the development of TRS and CR-TRS among patients with FES over 12 years of follow-up. Of the 1234 patients with FES, 15% developed TRS. A total of 450 patients with schizophrenia or schizoaffective disorder were included in a nested case-control study (157 TRS and 293 non-TRS). Younger age of onset, poorer premorbid social adjustment during adulthood, longer duration of first episode, a greater number of relapses, and a higher antipsychotic dose in the first 24 months were associated with earlier TRS. CR-TRS patients, constituting 25% of TRS patients, had a poorer premorbid social adjustment in late adolescence and longer delay before clozapine initiation compared with non-CR-TRS. CR-TRS had poorer clinical and functional outcomes at 12-year follow-up. However, TRS patients on clozapine had a lower mortality rate compared with non-TRS patients. EIS did not have a significant impact on the development of TRS, but patients in the EIS group had a shorter delay of clozapine initiation. Results suggested that neurodevelopmental factors, early clinical characteristics, and requirement for higher antipsychotic dose may be associated with TRS development, highlighting multiple pathways leading to this form of illness. Specific interventions including relapse prevention and early initiation of clozapine during the early course of illness may reduce the rate of TRS and improve patient outcomes.