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

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

Therapieprogramm Robin für Jugendliche mit einem erhöhten Psychoserisiko

Jugendliche, die an einer psychotischen Störung erkranken, werden in ihrer Entwicklung stark beeinträchtigt und weisen nach aktuellem Forschungsstand eine schlechtere Prognose auf als erwachsene Ersterkrankte. Das Ziel aller Früherkennungszentren ist, die Symptome früh zu erkennen und in der Folge früh zu behandeln. In der Klinik für Kinder- und Jugendpsychiatrie und Psychotherapie Zürich wurde ein innovatives Therapieprogramm für Jugendliche entwickelt, mit dem Ziel, den jungen Patienten eine Behandlung anzubieten, die altersgerecht, symptom- und ressourcenorientiert ist. Unterstützt wird die therapeutische Behandlung durch eine Smartphone-App

Psychotische Störungen: Die Frühbehandlung bei Kindern und Jugendlichen

Studien haben gezeigt, dass die Früherkennung und -behandlung die ­Prognose einer psychotischen Störung deutlich verbessert. Bei Verdacht auf eine psychotische Entwicklung ist daher eine frühzeitige Anmeldung bei Spezialisten wichtig

Jugendliche mit erhöhtem Psychoserisiko : App-unterstützte Behandlung mit dem Therapieprogramm Robin

Jugendliche, die an einer psychotischen Störung erkranken, können in ihrer Entwicklung stark beeinträchtigt werden und haben insgesamt einen schlechteren Krankheitsverlauf als erwachsene Ersterkrankte. Für eine bessere Prognose ist es wichtig, Patienten mit einem erhöhten Psychose-Risiko frühzeitig zu erkennen und zu behandeln. Während es einige Therapiekonzepte für Frühinterventionen mit Erwachsenen gibt, fehlte es bisher an therapeutischen Konzepten für Jugendliche mit At Risk-Symptomen. Robin ist ein innovatives Therapieprogramm und speziell für Jugendliche entwickelt, mit dem Ziel, den jungen Patienten eine Behandlung anzubieten, die altersgerecht, symptom- und ressourcenorientiert ist. Das Therapiemanual ist aufgrund der Heterogenität dieser Patientengruppe modular aufgebaut. Die Module können den Bedürfnissen der Patienten entsprechend individuell zusammengestellt und angewendet werden. Unterstützt wird die therapeutische Behandlung durch die frei verfügbare Smartphone-App «Robin Z». Dieses Buch richtet sich an Psychiater und Psychotherapeuten, die mit Jugendlichen mit erhöhtem Risiko für die Entwicklung einer Psychose arbeiten und das Therapieprogramm Robin oder Teile davon einsetzen und sich so das moderne Smartphone-Nutzungsverhalten ihrer Patienten zu Nutze machen möchten

Robin Smartphone App für Jugendliche mit einem erhöhten Psychoserisiko

Psychotische Störungen können sich über einen längeren Zeitraum hinweg entwickeln. Betroffene bemerken meist schon lange Zeit vor dem Ausbruch der Erkrankung Veränderungen in ihrer Wahrnehmung, kognitiven Leistungsfähigkeit und sozialen Fertigkeiten. Die Forschung zur Früherkennung von Psychosen hat diese beeinträchtigenden Veränderungen intensiv untersucht. So ist der Begriff der „At Risk-Symptome“ entstanden. At Risk-Symptome sind erste subtile Veränderungen im Denken, der Stimmung und der Wahrnehmung, die zwar noch nicht die Qualität von psychotischen Symptomen haben, jedoch bereits einen eigenen Krankheitswert aufweisen (Miller et al., 2003; Schultze-Lutter & Koch, 201 0). Verschiedene Kliniker und Forscher haben dafür plädiert, dass bereits At Risk-Symptome behandelt werden sollten, unabhängig von der Übergangsrate in eine psychotische Erkrankung, da der Leidensdruck ausreiche, um eine Behandlung zu rechtfertigen (z.B. Bertolote & McGorry, 2005; Correll, Hauser, Auther & Cornblatt, 201 0; Fusar-Poli, Yung, McGorry & van Os, 2014; Schmidt et al., 2015). Weltweit sind in den letzten Jahren Früherkennungszentren für Psychosen entstanden, die sich die Frühintervention zum Ziel gesetzt haben

Evaluation of the Combined Treatment Approach “Robin” (Standardized Manual and Smartphone App) for Adolescents at Clinical High Risk for Psychosis

Introduction: The prevention of schizophrenia and other psychotic disorders has led researchers to focus on early identification of individuals at clinical high risk (CHR) for psychosis and to treat the at-risk symptoms in the pre-psychotic period. Although at-risk symptoms such as attenuated hallucinations or delusions are common in adolescents and associated with a marked reduction in global functioning, the evidence base of effective interventions for adolescents at CHR state and even first-episode psychosis is limited. Thus, the present protocol describes a study design that combines therapy modules for CHR adolescents with a smartphone application supporting the young individuals between the therapy sessions. The treatment approach “Robin” is based on existing therapy strategies for adolescents with first episode of psychosis and the available recommendations for adults with at-risk symptoms. Methods: The evaluation aims firstly to compare the efficacy of Robin in 30 CHR adolescents aged 14–18 to an active control group (treatment as usual) from a previous study. Primary outcome measures will be at-risk symptomatology, comorbid diagnosis, functioning, self-efficacy, and quality of life. For the prospective intervention condition (16 weekly individual sessions + a minimum 4 family sessions), help-seeking adolescents with CHR for psychosis, aged 14–18, will be recruited over 3 years. At-risk and comorbid symptoms, functioning, self-efficacy, and quality of life are monitored at six time points (baseline, during the treatment period; immediately after intervention; and 6, 12, and 24 months later) and compared with the respective measures of the active control group. Discussion: To the best of our knowledge, this is the first controlled trial to test the efficacy of a specific early psychosis treatment in combination with a smartphone application for adolescents at CHR for developing psychosis. The results of the study are expected to add information that may substantially decrease the burden of CHR adolescents and increase their resilience. It may offer age-adapted and targeted strategies to guide clinicians in the treatment of these vulnerable individuals. Furthermore, research in the field of early intervention will be enriched by our findings