Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial

Description of schizophrenia treatment outcomes in Saudi Arabia: A preliminary pilot investigation

ObjectivesSchizophrenia is a debilitating psychiatric illness that is often understudied due to stigma and cultural barriers. This report presents a clinical descriptive evaluation of patients with schizophrenia in Saudi Arabia, focusing on demographics, clinical information, and treatment efficacy and safety.MethodsThis single-center cross-sectional study included 42 patients on antipsychotics for at least 6 months, with a male-to-female ratio of 1:1 and a mean age of 36 years.ResultsMost patients were single (64.3%) and unemployed (51.2%), with 52.4% having only a high school education level. The median duration of antipsychotic treatment was 21.2  (1.7-227.5) months. Antipsychotic combination therapy was the most common intervention (59.5%). Efficacy results from the Positive and Negative Syndrome Scale showed complete remission in 4.8%, partial remission in 11.9%, moderate remission in 33.3%, and no improvement in 50% of the patients. Safety evaluation via the modified Simpson-Angus Scale indicated that 71.4% of the patients had normal scores, 26.2% experienced minimal movement disorders, and only 2.4% had clinically significant movement symptoms.ConclusionThis preliminary investigation examined the demographics and clinical features of patients with schizophrenia as well as efficacy and tolerability of relevant treatments. Larger studies are needed to understand this population comprehensively.

Towards Enhancing Drug Development Methodology to Treat Cognitive Impairment Associated With Schizophrenia and Other Neuropsychiatric Conditions: Insights From 2 Decades of Clinical Trials

Cognitive impairment is a core feature and leading cause of functional disability in schizophrenia and other neuropsychiatric disorders. The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative in the early 2000s marked a pivotal moment for drug development, establishing consensus on methodology for treatment studies, including assessment strategies and trial designs, for cognitive impairment associated with schizophrenia (CIAS). Despite extensive industry-sponsored and academic drug development efforts over the last 2 decades using these strategies no pharmacological treatments have been approved for CIAS. Drawing on pharmaceutical industry experience and scientific developments since the MATRICS initiative, we review lessons learned about the practical and operational complexities of conducting large-scale CIAS clinical trials. Based on this collective experience, we identify elements of the MATRICS guidelines that may warrant reconsideration and suggest some new approaches to streamline the drug development pathway, without weakening standards for evidence. Our goal is to initiate an open exchange among all stakeholders about possible enhancements to drug development methodology that optimize our ability to develop new treatments for cognitive impairment in schizophrenia and other neuropsychiatric disorders.

Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model

BACKGROUND

Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).

METHODS

Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.

RESULTS

The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).

CONCLUSIONS

Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

When to hold and when to fold: Early prediction of nonresponse to transcranial magnetic stimulation in major depressive disorder

BACKGROUND

Determining when to recommend a change in treatment regimen due to insufficient improvement is a common challenge in therapeutics.

METHODS

In a sample of 7215 patients with major depressive disorder treated with transcranial magnetic stimulation (TMS) and with PHQ-9 scores before, during and after the course, 3 groups were identified based on number of acute course sessions: exactly 36 sessions (N = 3591), more than 36 sessions (N = 975), and less than 36 sessions (N = 2649). Two techniques were used to determine thresholds for percentage change in PHQ-9 scores at assessments after 10, 20, and 30 sessions that optimized prediction of endpoint response status: the Youden index and fixing the false positive rate at 10%. Positive and negative predictive values were calculated to assess the accuracy of identifying final nonresponders and responders, respectively.

RESULTS

There was greater accuracy in predicting final response than nonresponse, especially in the groups that had at least 36 sessions. Substantial proportions of patients with low levels of early improvement were classified as responders at the end of treatment.

LIMITATIONS

The findings should be validated with clinician ratings using a more comprehensive depression severity scale.

CONCLUSIONS

Manifesting clinical improvement early in the TMS course is strongly predictive of final status as a responder, while lack of early improvement is a relatively poor indicator of final nonresponse status. The predictive value of lack of early symptomatic improvement is too low to make reliable recommendations regarding changes in treatment regimen.