Revealing the impact of psychiatric comorbidities on treatment outcome in early psychosis using counterfactual model explanation

INTRODUCTION: Psychiatric comorbidities have a significant impact on the course of illness in patients with schizophrenia spectrum disorders. To accurately predict outcomes for individual patients using computerized prognostic models, it is essential to consider these comorbidities and their influence. METHODS: In our study, we utilized a multi-modal deep learning architecture to forecast symptomatic remission, focusing on a multicenter sample of patients with first-episode psychosis from the OPTiMiSE study. Additionally, we introduced a counterfactual model explanation technique to examine how scores on the Mini International Neuropsychiatric Interview (MINI) affected the likelihood of remission, both at the group level and for individual patients. RESULTS: Our findings at the group level revealed that most comorbidities had a negative association with remission. Among them, current and recurrent depressive disorders consistently exerted the greatest negative impact on the probability of remission across patients. However, we made an interesting observation: current suicidality within the past month and substance abuse within the past 12 months were associated with an increased chance of remission in patients. We found a high degree of variability among patients at the individual level. Through hierarchical clustering analysis, we identified two subgroups: one in which comorbidities had a relatively limited effect on remission (approximately 45% of patients), and another in which comorbidities more strongly influenced remission. By incorporating comorbidities into individualized prognostic prediction models, we determined which specific comorbidities had the greatest impact on remission at both the group level and for individual patients. DISCUSSION: These results highlight the importance of identifying and including relevant comorbidities in prediction models, providing valuable insights for improving the treatment and prognosis of patients with psychotic disorders. Furthermore, they open avenues for further research into the efficacy of treating these comorbidities to enhance overall patient outcomes

The role of depression in the prediction of a "late" remission in first-episode psychosis:An analysis of the OPTiMiSE study

Objective: The identification of predictors of psychosis remission could guide early clinical decision-making for treatment of first-episode schizophrenia (FES). Methods: We analyzed two non-independent subsamples of patients with FES ages 18-40 years from the OPTiMiSE study dataset to investigate the demographic and clinical factors that might help to differentiate "late" re-mitters (i.e., not in remission at week 2 or 4, but achieving remission within a 10-week follow-up period) from non-remitters within the same period. Results: Subsample 1 included 216 individuals (55 females, mean age 25.9 years) treated with amisulpride in an open-label design who were not in remission at week 2. Early symptomatic response between baseline and week 2 (odds ratio (OR)- 4.186, 95% confidence interval (CI)- 2.082-8.416, p < 0.001) and older age (OR- 1.081, 95% = CI 1.026-1.138, p- 0.003) were the only variables significantly associated with a higher probability of psychosis remission at week 4. Subsample 2 was composed of the 72 participants (19 females, mean age 25.1 years) who were not in remission at week 4 and completed a 6-week double-blind randomized trial comparing continuation of amisulpride with switch to olanzapine. Depression at baseline (as measured with the Calgary Depression Scale for Schizophrenia) was significantly associated with a nearly 3-fold lower likelihood of psychosis remission during the 10-week follow-up (hazard ratio = 2.865, 95% CI = 1.187-6.916, p = 0.019). Conclusion: Our results reinforce the importance of assessing depressive symptoms in people with FES and support the relevance of an early response (as early as 2 weeks) as a predictor of clinical outcome in this population. (C) 2021 Elsevier B.V. All rights reserved

Digital behavioural signatures reveal trans-diagnostic clusters of schizophrenia and Alzheimer's disease patients

The current neuropsychiatric nosological categories underlie pragmatic treatment choice, regulation and clinical research but does not encompass biological rationale. However, subgroups of patients suffering from schizophrenia or Alzheimer's disease have more in common than the neuropsychiatric nature of their condition, such as the expression of social dysfunction. The PRISM project presents here initial quantitative biological insights allowing the first steps toward a novel trans-diagnostic classification of psychiatric and neurological symptomatology intended to reinvigorate drug discovery in this area. In this study, we applied spectral clustering on digital behavioural endpoints derived from passive smartphone monitoring data in a subgroup of Schizophrenia and Alzheimer's disease patients, as well as age matched healthy controls, as part of the PRISM clinical study. This analysis provided an objective social functioning characterization with three differential clusters that transcended initial diagnostic classification and was shown to be linked to quantitative neurobiological parameters assessed. This emerging quantitative framework will both offer new ways to classify individuals in biologically homogenous clusters irrespective of their initial diagnosis, and also offer insights into the pathophysiological mechanisms underlying these clusters.</p

Effect of disease related biases on the subjective assessment of social functioning in Alzheimer's disease and schizophrenia patients

Background: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity. Methods: We selected five items from the WHO Disability Assessment Schedule 2.0 (WHODAS) to assess social functioning in 53 AD and 61 SZ patients. Caregiver- and researcher-rated assessments of social functioning were used to calculate the discrepancies between self-rated and proxy-rated assessments. Furthermore, we used the number of communication events via smartphones to compare the questionnaire outcomes with an objective measure of social behaviour. Results: WHODAS results revealed that both AD (p &lt; 0.001) and SZ (p &lt; 0.004) patients significantly overestimate their social functioning relative to the assessment of their caregivers and/or researchers. This overestimation is mediated by the severity of cognitive impairments (MMSE; p = 0.019) in AD, and negative symptoms (PANSS; p = 0.028) in SZ. Subsequently, we showed that the proxy scores correlated more strongly with the smartphone communication events of the patient when compared to the patient-rated questionnaire scores (self; p = 0.076, caregiver; p &lt; 0.001, researcher-rated; p = 0.046). Conclusion: Here we show that the observed overestimation of WHODAS social functioning scores in AD and SZ patients is partly driven by disease-related biases such as cognitive impairments and negative symptoms, respectively. Therefore, we postulate the development and implementation of objective measures of social functioning that may be less susceptible to such biases.The PRISM project (www.prism-project.eu) leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115916. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the authors’ views neither IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein. Dr. Arango has also received funding support by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. Fundación Familia Alonso and Fundación Alicia Koplowit

Cross-disorder and disorder-specific deficits in social functioning among schizophrenia and Alzheimer's disease patients

BACKGROUND: Social functioning is often impaired in schizophrenia (SZ) and Alzheimer's disease (AD). However, commonalities and differences in social dysfunction among these patient groups remain elusive.MATERIALS AND METHODS: Using data from the PRISM study, behavioral (all subscales and total score of the Social Functioning Scale) and affective (perceived social disability and loneliness) indicators of social functioning were measured in patients with SZ (N = 56), probable AD (N = 50) and age-matched healthy controls groups (HC, N = 29 and N = 28). We examined to what extent social functioning differed between disease and age-matched HC groups, as well as between patient groups. Furthermore, we examined how severity of disease and mood were correlated with social functioning, irrespective of diagnosis.RESULTS: As compared to HC, both behavioral and affective social functioning seemed impaired in SZ patients (Cohen's d's 0.81-1.69), whereas AD patients mainly showed impaired behavioral social function (Cohen's d's 0.65-1.14). While behavioral indices of social functioning were similar across patient groups, SZ patients reported more perceived social disability than AD patients (Cohen's d's 0.65). Across patient groups, positive mood, lower depression and anxiety levels were strong determinants of better social functioning (p's &lt;0.001), even more so than severity of disease.CONCLUSIONS: AD and SZ patients both exhibit poor social functioning in comparison to age- and sex matched HC participants. Social dysfunction in SZ patients may be more severe than in AD patients, though this may be due to underreporting by AD patients. Across patients, social functioning appeared as more influenced by mood states than by severity of disease.</p

Relationships between social withdrawal and facial emotion recognition in neuropsychiatric disorders

Background: Emotion recognition constitutes a pivotal process of social cognition. It involves decoding social cues (e.g., facial expressions) to maximise social adjustment. Current theoretical models posit the relationship between social withdrawal factors (social disengagement, lack of social interactions and loneliness) and emotion decoding. Objective: To investigate the role of social withdrawal in patients with schizophrenia (SZ) or probable Alzheimer's disease (AD), neuropsychiatric conditions associated with social dysfunction. Methods: A sample of 156 participants was recruited: schizophrenia patients (SZ; n = 53), Alzheimer's disease patients (AD; n = 46), and two age-matched control groups (SZc, n = 29; ADc, n = 28). All participants provided self-report measures of loneliness and social functioning, and completed a facial emotion detection task. Results: Neuropsychiatric patients (both groups) showed poorer performance in detecting both positive and negative emotions compared with their healthy counterparts (p < .01). Social withdrawal was associated with higher accuracy in negative emotion detection, across all groups. Additionally, neuropsychiatric patients with higher social withdrawal showed lower positive emotion misclassification. Conclusions: Our findings help to detail the similarities and differences in social function and facial emotion recognition in two disorders rarely studied in parallel, AD and SZ. Transdiagnostic patterns in these results suggest that social withdrawal is associated with heightened sensitivity to negative emotion expressions, potentially reflecting hypervigilance to social threat. Across the neuropsychiatric groups specifically, this hypervigilance associated with social withdrawal extended to positive emotion expressions, an emotional-cognitive bias that may impact social functioning in people with severe mental illness

Extreme deviations from the normative model reveal cortical heterogeneity and associations with negative symptom severity in first-episode psychosis from the OPTiMiSE and GAP studies

There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/– 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification

Prednisolone versus placebo addition in the treatment of patients with recent-onset psychotic disorder: a trial design

BACKGROUND: The symptom severity of a substantial group of schizophrenia patients (30-40%) does not improve through pharmacotherapy with antipsychotic medication, indicating a clear need for new treatment options to improve schizophrenia outcome. Meta-analyses, genetic studies, randomized controlled trials, and post-mortem studies suggest that immune dysregulation plays a role in the pathophysiology of schizophrenia. Some anti-inflammatory drugs have shown beneficial effects on the symptom severity of schizophrenia patients. Corticosteroids are effective in various chronic inflammatory and autoimmune disorders. Prednisolone, a potent glucocorticosteroid, has minor mineral-corticosteroid potencies and can adequately pass the blood-brain barrier

Association of Complement and Coagulation Pathway Proteins With Treatment Response in First-Episode Psychosis: A Longitudinal Analysis of the OPTiMiSE Clinical Trial

BACKGROUND AND HYPOTHESIS: Treatment response to specific antipsychotic medications is difficult to predict on clinical grounds alone. The current study hypothesizes that the baseline complement pathway activity predicts the treatment response and investigates the relationship between baseline plasma biomarkers with treatment response to antipsychotic medications. STUDY DESIGN: Baseline plasma samples were collected from first episode of psychosis patients (n = 243) from a multi-center clinical trial. The participants were treated with amisulpride for 4 weeks. Levels of complement and coagulation proteins at baseline were measured using both data-dependent and data-independent mass spectrometry approaches. The primary outcome was remission status at 4 weeks and the secondary outcomes included change in psychotic and functional symptoms over the period of treatment. In addition, immunoassays were performed at baseline for complement C1R, as well as for activation markers C4a and sC5b-9. STUDY RESULTS: The plasma level of complement variant C4A was significantly associated with remission at 4 weeks. Moreover, higher levels of several complement and coagulation pathway proteins were associated with a reduction in psychotic symptoms and an improvement in functioning. Immunoassays showed an association of baseline levels of C1R and C4a as well as complement activation marker sC5b-9 levels with treatment response. CONCLUSION: The results demonstrated that the response to antipsychotic treatment might be related to pre-treatment levels of plasma complement and coagulation pathway proteins. This is consistent with independent evidence associating immune dysfunction with the pathophysiology of psychosis. Moreover, these results inform the development of novel therapeutic approaches that target the complement system for psychosis

Association of complement and coagulation pathway proteins with treatment response in first-episode psychosis: a longitudinal analysis of the OPTiMiSE clinical trial

Background and Hypothesis Treatment response to specific antipsychotic medications is difficult to predict on clinical grounds alone. The current study hypothesizes that the baseline complement pathway activity predicts the treatment response and investigates the relationship between baseline plasma biomarkers with treatment response to antipsychotic medications. Study Design Baseline plasma samples were collected from first episode of psychosis patients (n = 243) from a multi-center clinical trial. The participants were treated with amisulpride for 4 weeks. Levels of complement and coagulation proteins at baseline were measured using both data-dependent and data-independent mass spectrometry approaches. The primary outcome was remission status at 4 weeks and the secondary outcomes included change in psychotic and functional symptoms over the period of treatment. In addition, immunoassays were performed at baseline for complement C1R, as well as for activation markers C4a and sC5b-9. Study Results The plasma level of complement variant C4A was significantly associated with remission at 4 weeks. Moreover, higher levels of several complement and coagulation pathway proteins were associated with a reduction in psychotic symptoms and an improvement in functioning. Immunoassays showed an association of baseline levels of C1R and C4a as well as complement activation marker sC5b-9 levels with treatment response. Conclusion The results demonstrated that the response to antipsychotic treatment might be related to pre-treatment levels of plasma complement and coagulation pathway proteins. This is consistent with independent evidence associating immune dysfunction with the pathophysiology of psychosis. Moreover, these results inform the development of novel therapeutic approaches that target the complement system for psychosis