Developing Psychosis and Its Risk States Through the Lens of Schizotypy

Starting from the early descriptions of Kraepelin and Bleuler, the construct of schizotypy was developed from observations of aberrations in nonpsychotic family members of schizophrenia patients. In contemporary diagnostic manuals, the positive symptoms of schizotypal personality disorder were included in the ultra high-risk (UHR) criteria 20 years ago, and nowadays are broadly employed in clinical early detection of psychosis. The schizotypy construct, now dissociated from strict familial risk, also informed research on the liability to develop any psychotic disorder, and in particular schizophrenia-spectrum disorders, even outside clinical settings. Against the historical background of schizotypy it is surprising that evidence from longitudinal studies linking schizotypy, UHR, and conversion to psychosis has only recently emerged; and it still remains unclear how schizotypy may be positioned in high-risk research. Following a comprehensive literature search, we review 18 prospective studies on 15 samples examining the evidence for a link between trait schizotypy and conversion to psychosis in 4 different types of samples: general population, clinical risk samples according to UHR and/or basic symptom criteria, genetic (familial) risk, and clinical samples at-risk for a nonpsychotic schizophrenia-spectrum diagnosis. These prospective studies underline the value of schizotypy in high-risk research, but also point to the lack of evidence needed to better define the position of the construct of schizotypy within a developmental psychopathology perspective of emerging psychosis and schizophrenia-spectrum disorder

Rekonstruktive Forschungsmethoden in der Lehre: Eine Beforschung der Fallwerkstätten durch die involvierten Lehrpersonen nach der Idee von Scholarship of Teaching and Learning (SoTL)

Das vorliegende Forschungsprojekt hat das Ziel, das Potential des rekonstruktiven Ansatzes für die Kompetenzentwicklung im Rahmen der praxisbegleitenden Module (Fallwerkstätte) von Studierenden der Sozialen Arbeit zu untersuchen. Das Forschungsprojekt untersuchte die Fallwerkstätten über drei Semester. Dabei wurde der Fokus auf den Einsatz von Methoden der rekonstruktiven Sozialforschung (Kapitel 3) gelegt und dabei insbesondere auf deren Nutzen und Leistungsfähigkeit für die Kompetenzentwicklung der Studierenden und die Praxis der Sozialen Arbeit. Das Forschungsteam bestand im Sinne von Scholarship of Teaching and Learning (Kapitel 3.1) aus den Lehrpersonen der beforschten Fallwerkstätten

An ecological momentary assessment study of age effects on perceptive and non-perceptive clinical high-risk symptoms of psychosis.

Among individuals with clinical high risk for psychosis (CHR), perceptive symptoms are more frequent but have less clinical significance in children/adolescents compared to adults. However, findings are based on clinical interviews relying on patient's recall capacity. Ecological momentary assessment (EMA) can be used to explore experiences in real-time in the subject's daily life. The aim of this study was to assess frequency and stability of (perceptive and non-perceptive) CHR symptoms and to explore potential age effects. EMA was used in a sample of an early detection for psychosis service in Bern, Switzerland (N = 66; 11-36 years). CHR symptoms were recorded in random time intervals for seven days: eight assessments per day per subject, minimum time between prompts set at 25 min. CHR symptoms were additionally assessed with semi-structured interviews including the 'Structured Interview for Psychosis-Risk Syndromes' and the 'Schizophrenia Proneness Instruments'. Mixed-effects linear regression analysis on the frequency of CHR symptoms revealed a significant effect of age group, and the interaction CHR symptoms x age group for both perceptive and non-perceptive symptoms. Further, regarding stability of CHR symptoms, there was a significant effect of the interaction CHR symptoms x age group for perceptive symptoms only. Based on EMA, perceptive CHR symptoms were more frequently reported but less stable in children/adolescents compared with adults. Together with previous findings, our finding of higher instability/variability of perceptive symptoms in younger persons might suggest that with advancing age and more stability of CHR symptoms, clinical relevance (reduced psychosocial functioning) may increase

Clinical high-risk criteria of psychosis in 8–17-year-old community subjects and inpatients not suspected of developing psychosis

BACKGROUND In children and adolescents compared to adults, clinical high-risk of psychosis (CHR) criteria and symptoms are more prevalent but less psychosis-predictive and less clinically relevant. Based on high rates of non-converters to psychosis, especially in children and adolescents, it was suggested that CHR criteria were: (1) Pluripotential; (2) A transdiagnostic risk factor; and (3) Simply a severity marker of mental disorders rather than specifically psychosis-predictive. If any of these three alternative explanatory models were true, their prevalence should differ between persons with and without mental disorders, and their severity should be associated with functional impairment as a measure of severity. AIM To compare the prevalence and severity of CHR criteria/symptoms in children and adolescents of the community and inpatients. METHODS In the mainly cross-sectional examinations, 8–17-year-old community subjects (n = 233) randomly chosen from the population register of the Swiss Canton Bern, and inpatients (n = 306) with primary diagnosis of attention-deficit/hyperactivity disorder (n = 86), eating disorder (n = 97), anxiety including obsessive–compulsive disorder (n = 94), or autism spectrum disorder (n = 29), not clinically suspected to develop psychosis, were examined for CHR symptoms/criteria. Positive items of the Structured Interview for Psychosis-Risk Syndromes (SIPS) were used to assess the symptomatic ultra-high-risk criteria, and the Schizophrenia Proneness Instrument, Child and Youth version (SPI-CY) was used to assess the 14 basic symptoms relevant to basic symptom criteria. We examined group differences in frequency and severity of CHR symptoms/criteria using χ2 tests and nonparametric tests with Cramer’s V and Rosenthal’s r as effect sizes, and their association with functioning using correlation analyses. RESULTS The 7.3% prevalence rate of CHR criteria in community subjects did not differ significantly from the 9.5% rate in inpatients. Frequency and severity of CHR criteria never differed between the community and the four inpatient groups, while the frequency and severity of CHR symptoms differed only minimally. Group differences were found in only four CHR symptoms: suspiciousness/persecutory ideas of the SIPS [χ2 (4) = 9.425; P = 0.051, Cramer’s V = 0.132; and Z = -4.281, P < 0.001; Rosenthal’s r = 0.184], and thought pressure [χ2 (4) = 11.019; P = 0.026, Cramer’s V = 0.143; and Z = -2.639, P = 0.008; Rosenthal’s r = 0.114], derealization [χ2 (4) = 32.380; P < 0.001, Cramer’s V = 0.245; and Z = -3.924, P < 0.001; Rosenthal’s r = 0.169] and visual perception disturbances [χ2 (4) = 10.652; P = 0.031, Cramer’s V = 0.141; and Z = -2.822, P = 0.005; Rosenthal’s r = 0.122] of the SPI-CY. These were consistent with a transdiagnostic risk factor or dimension, i.e., displayed higher frequency and severity in inpatients, in particular in those with eating, anxiety/obsessive–compulsive and autism spectrum disorders. Low functioning, however, was at most weakly related to the severity of CHR criteria/symptoms, with the highest correlation yielded for suspiciousness/persecutory ideas (Kendall’s tau = -0.172, P < 0.001). CONCLUSION The lack of systematic differences between inpatients and community subjects does not support suggestions that CHR criteria/symptoms are pluripotential or transdiagnostic syndromes, or merely markers of symptom severity

Sociodemographic and clinical predictors of depression in children and adolescents: results of a two-year follow-up study

Depressive disorders are a main cause of disability-adjusted life years already in children and adolescents, in whom the clinical picture somewhat differs from adult-onset depression. Thus, we studied sociodemographic and clinical predictors of depression/dysthymia in a sample of minors. Our baseline sample (N=676) included patients at clinical high-risk for psychosis (CHR-P, n=183), inpatients admitted for non-psychotic, non-affective disorders (n=277), and community participants (n=216) of age 7.0 to 17.9 years (43.8% male). They were assessed by clinical psychologists for mental disorders and symptoms with various clinical interviews including the Mini International Neuropsychiatric Interview for Children and Adolescents, which was also used to assess depression/dysthymia in the CHR-P group at 1- and 2-year-follow up (n=117 and 73, respectively). Analyses followed a stepwise procedure at baseline with stepwise logistic regression analyses to identify the final baseline model that was tested in the follow-up samples. The final cross-sectional model included nationality and 13 clinical variables Mild depressive symptoms in particular played a decisive role here. Variables contributing significantly to the prediction varied over time, indicating that CAD depression/dysthymia may require different predictors depending on the follow-up time. Furthermore, the prospective accuracy of ruling out depression/dysthymia was superior to the accuracy of ruling it in. This lower positive likelihood ratio might be overcome in future by stepwise approaches that further stratify risk in those initially identified as at increased risk of depression/dysthymia

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Adverse effects of psychotherapy: protocol for a systematic review and meta-analysis

Background: While it is well known that psychotherapy is efficacious in the treatment of mental disorders, much less is known about the adverse effects of psychotherapeutic interventions. The aim of this systematic review is to examine the definition, frequency, nature, and severity of adverse effects occurring parallel to or following psychotherapeutic treatment and to compare it against control groups. Methods: All registered randomised controlled trials published since 2004 (publication year of harm-reporting extension of the CONSORT statement) with adult patients fulfilling clinical criteria of defined mental disorders, which compare individual or group psychotherapy against a control group, will be included. First, a search through international trial registers as well as a search in literature databases (e.g. MEDLINE) and in relevant journals (e.g. Trials) for study protocols will be conducted to identify eligible trials. In a second step, we will search for respective publications of the results of the eligible studies. Publications will be retrieved and screened for eligibility. Two previously trained, independent raters will extract the data in duplicate. Reporting of adverse effects will be descriptively analysed regarding frequency, heterogeneity, and longitudinal course. We will further compare the adverse effects of psychotherapeutic interventions against various control groups. For each categorical outcome, we will calculate relative risks (RR) together with 95% confidence intervals. For continuous outcomes, standardised mean differences (Hedges' g) with a 95% confidence interval will be computed. Between-study heterogeneity will be tested with the Q statistic and quantified using I$^{2}$. Disscussion: Preselecting studies with regard to randomised controlled trials might induce bias due to dropout before the beginning of treatment or end of treatment. However, we will thoroughly assess the negative effects of randomisation, e.g. reasons for non-randomisation, if reported. Even if delayed adverse effects might be overlooked in randomised controlled trials, these are the only sources of causal evidence. Systematic review registration: PROSPERO International Prospective Register of Systematic Reviews 2017: CRD42017055507 (17 January 2017)