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

The significance of at-risk symptoms for psychosis in children and adolescents

The early detection and treatment of people at risk for psychosis is currently regarded as a promising strategy in fighting the devastating consequences of psychotic disorders. Currently, the 2 most broadly used sets of at-risk criteria, that is, ultra-high risk (UHR) and basic symptom criteria, were developed mainly in adult samples. We review the data regarding the presence and relevance of at-risk symptoms for psychosis in children and adolescents. The few existing studies suggest that attenuated psychotic symptoms (APS) and brief limited intermittent psychotic symptoms (BLIPS) do have some clinical relevance in young adolescents from the general population. Nevertheless, their differentiation from atypical psychotic symptoms or an emerging schizotypal personality disorder, as well as their stability and predictive accuracy for psychosis, are still unclear. Further, standard interviews for UHR criteria do not define a minimum age for the assessment of APS and BLIPS or guidelines as to when and how to include information from parents. APS and basic symptoms may be predictive of conversion to psychosis in help-seeking young adolescents. Nevertheless, the rate and timing, and thus the required observation time, need further study. Moreover, no study has yet addressed the issue of how to treat children and adolescents presenting with at-risk symptoms and criteria. Further research is urgently needed to examine if current at-risk criteria and approaches have to be tailored to the special needs of children and adolescents. A preliminary rationale for how to deal with at-risk symptoms for psychosis in clinical practice is provided

The Schizophrenia Proneness Instrument, Child and Youth version (SPI-CY): Practicability and discriminative validity

Background Basic symptom (BS) criteria have been suggested to complement ultra-high risk (UHR) criteria in the early detection of psychosis in adults and in children and adolescents. To account for potential developmental particularities and a different clustering of BS in children and adolescents, the Schizophrenia Proneness Instrument, Child and Youth version (SPI-CY) was developed. Aims The SPI-CY was evaluated for its practicability and discriminative validity. Method The SPI-CY was administered to 3 groups of children and adolescents (mean age 16; range=8–18; 61% male): 23 at-risk patients meeting UHR and/or BS criteria (AtRisk), 22 clinical controls (CC), and 19 children and adolescents from the general population (GPS) matched to AtRisk in age, gender, and education. We expected AtRisk to score highest on the SPI-CY, and GPS lowest. Results The groups differed significantly on all 4 SPI-CY subscales. Pairwise post-hoc comparisons confirmed our expectations for all subscales and, at least on a descriptive level, most items. Pairwise subscale differences indicated at least moderate group effects (r≥0.37) which were largest for Adynamia (0.52≤r≥0.70). Adynamia also performed excellent to outstanding in ROC analyses (0.813≤AUC≥0.981). Conclusion The SPI-CY could be a helpful tool for detecting and assessing BS in the psychosis spectrum in children and adolescents, by whom it was well received. Furthermore, its subscales possess good discriminative validity. However, these results require validation in a larger sample, and the psychosis-predictive ability of the subscales in different age groups, especially the role of Adynamia, will have to be explored in longitudinal studies

[Developmental Aspects in the Early Detection and Intervention in Clinical High Risk States for Psychosis]

Entwicklungsaspekte bei der Früherkennung und Intervention in klinischen Hochrisiko-Staaten für Psychosen Die Früherkennung und Intervention bei Psychosen, die bereits bei Kindern und Jugendlichen eine Hauptquelle für behinderungsbereinigte Lebensjahre darstellen, ist in den letzten Jahren gut vorangekommen. Insbesondere die abgeschwächten und transienten Positivsymptome der Ultrahochrisikokriterien und das Basissymptomkriterium "Kognitive Störungen" eröffnen vielversprechende Wege zu einer indizierten Prävention und wurden kürzlich von der European Psychiatric Association als diagnostische Kriterien eines Psychose-Risiko-Syndroms in Betracht gezogen (EPA). Da ihre Assoziation mit einer Entwicklung der Psychose bei Kindern und Jugendlichen jedoch schwächer war als bei Erwachsenen, Für Kinder und Jugendliche wurde nur die Bewertung und Überwachung dieser Risikosymptome empfohlen, während Interventionen zur Prävention von Psychosen abgeraten wurden. Darüber hinaus charakterisiert die Behandlung komorbider psychischer Störungen und psychosozialer Probleme im Hinblick auf die Prävention einer möglichen zukünftigen Störung auch die Interventionsempfehlungen der EPA. Darüber hinaus geben sie psychologischen, insbesondere kognitiv-behavioralen Interventionen Vorrang vor psychopharmakologischen Behandlungen. Aber auch im Hinblick auf eine frühzeitige Intervention deuten aktuelle Erkenntnisse darauf hin, dass Kinder und Jugendliche weniger profitieren könnten als Erwachsene. Insgesamt werden altersbedingte oder entwicklungsbedingte Besonderheiten bei der Früherkennung und Intervention bei Psychosen immer offensichtlicher und sollten in zukünftigen Forschungen auf diesem Gebiet stärker berücksichtigt werden