Clinical high risk for psychosis in childhood and adolescence: findings from the 2-year follow-up of the ReARMS project

2018 ◽  
Vol 28 (7) ◽  
pp. 957-971 ◽  
Author(s):  
Michele Poletti ◽  
Lorenzo Pelizza ◽  
Silvia Azzali ◽  
Federica Paterlini ◽  
Sara Garlassi ◽  
...  
Keyword(s):  
High Risk ◽  
Childhood And Adolescence ◽  
Clinical High Risk

Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): when things are not what they seem

2020 ◽  
pp. 1-9
Author(s):  
Andrea Raballo ◽  
Michele Poletti ◽  
Antonio Preti
Keyword(s):  
High Risk ◽  
Meta Analysis ◽  
Prognostic Impact ◽  
Clinical High Risk ◽  
Qualitative Synthesis ◽  
Prognostic Effect ◽  
Inverse Variance ◽  
Arcsine Transformation ◽  
Estimate Prevalence

Abstract Background The clinical high-risk (CHR) for psychosis paradigm is changing psychiatric practice. However, a widespread confounder, i.e. baseline exposure to antipsychotics (AP) in CHR samples, is systematically overlooked. Such exposure might mitigate the initial clinical presentation, increase the heterogeneity within CHR populations, and confound the evaluation of transition to psychosis at follow-up. This is the first meta-analysis examining the prevalence and the prognostic impact on transition to psychosis of ongoing AP treatment at baseline in CHR cohorts. Methods Major databases were searched for articles published until 20 April 2020. The variance-stabilizing Freeman-Tukey double arcsine transformation was used to estimate prevalence. The binary outcome of transition to psychosis by group was estimated with risk ratio (RR) and the inverse variance method was used for pooling. Results Fourteen studies were eligible for qualitative synthesis, including 1588 CHR individuals. Out of the pooled CHR sample, 370 individuals (i.e. 23.3%) were already exposed to AP at the time of CHR status ascription. Transition toward full-blown psychosis at follow-up intervened in 112 (29%; 95% CI 24–34%) of the AP-exposed CHR as compared to 235 (16%; 14–19%) of the AP-naïve CHR participants. AP-exposed CHR had higher RR of transition to psychosis (RR = 1.47; 95% CI 1.18–1.83; z = 3.48; p = 0.0005), without influence by age, gender ratio, overall sample size, duration of the follow-up, or quality of the studies. Conclusions Baseline AP exposure in CHR samples is substantial and is associated with a higher imminent risk of transition to psychosis. Therefore, such exposure should be regarded as a non-negligible red flag for clinical risk management.

scholarly journals Two-year follow-up of a Chinese sample at clinical high risk for psychosis: timeline of symptoms, help-seeking and conversion

2016 ◽  
Vol 26 (3) ◽  
pp. 287-298 ◽  
Author(s):  
T. H. Zhang ◽  
H. J. Li ◽  
K. A. Woodberry ◽  
L. H. Xu ◽  
Y. Y. Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Symptom Onset ◽  
Help Seeking ◽  
Cox Regression ◽  
Clinical Population ◽  
Psychotic Symptoms ◽  
Clinical High Risk ◽  
Chinese Sample ◽  
Over Time

Background.Chinese psychiatrists have gradually started to focus on those who are deemed to be at ‘clinical high-risk (CHR)’ for psychosis; however, it is still unknown how often those individuals identified as CHR from a different country background than previously studied would transition to psychosis. The objectives of this study are to examine baseline characteristics and the timing of symptom onset, help-seeking, or transition to psychosis over a 2-year period in China.Method.The presence of CHR was determined with the Structured Interview for Prodromal Syndromes (SIPS) at the participants' first visit to the mental health services. A total of 86 (of 117) CHR participants completed the clinical follow-up of at least 2 years (73.5%). Conversion was determined using the criteria of presence of psychotic symptoms (in SIPS). Analyses examined baseline demographic and clinical predictors of psychosis and trajectory of symptoms over time. Survival analysis (Kaplan–Meier) methods along with Log-rank tests were performed to illustrate the relationship of baseline data to either conversion or non-conversion over time. Cox regression was performed to identify baseline predictors of conversion by the 2-year follow-up.Results.In total 25 (29.1%) of 86 completers transitioned to a psychotic disorder over the course of follow-up. Among the CHR sample, the mean time between attenuated symptom onset and professional help-seeking was about 4 months on average, and converters developed fully psychotic symptoms about 12 months after symptom onset. Compared with those CHR participants whose risk syndromes remitted over the course of the study, converters had significantly longer delays (p = 0.029) for their first visit to a professional in search of help. At baseline assessment, the conversion subgroup was younger, had poorer functioning, higher total SIPS positive symptom scores, longer duration of untreated prodromal symptoms, and were more often given psychosis-related diagnoses and subsequently prescribed antipsychotics in the clinic.Conclusions.Chinese CHR identified primarily by a novel clinical screening approach had a 2-year transition rate comparable with those of specialised help-seeking samples world-wide. Early clinical intervention with this functionally deteriorating clinical population who are suffering from attenuated psychotic symptoms, is a next step in applying the CHR construct in China.

scholarly journals The trait‐state distinction between schizotypy and clinical high risk: results from a one‐year follow‐up

2019 ◽  
Vol 18 (1) ◽  
pp. 108-109 ◽  
Author(s):  
Chantal Michel ◽  
Rahel Flückiger ◽  
Jochen Kindler ◽  
Daniela Hubl ◽  
Michael Kaess ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical High Risk ◽  
One Year

scholarly journals Relationship between jumping to conclusions and clinical outcomes in people at clinical high-risk for psychosis

2020 ◽  
pp. 1-9
Author(s):  
Ana Catalan ◽  
Stefania Tognin ◽  
Matthew J. Kempton ◽  
Daniel Stahl ◽  
Gonzalo Salazar de Pablo ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Outcomes ◽  
Cox Regression ◽  
Mental States ◽  
Clinical High Risk ◽  
Level Of Functioning ◽  
Jumping To Conclusions ◽  
Reasoning Bias ◽  
Beads Task

Abstract Background Psychosis is associated with a reasoning bias, which manifests as a tendency to ‘jump to conclusions’. We examined this bias in people at clinical high-risk for psychosis (CHR) and investigated its relationship with their clinical outcomes. Methods In total, 303 CHR subjects and 57 healthy controls (HC) were included. Both groups were assessed at baseline, and after 1 and 2 years. A ‘beads’ task was used to assess reasoning bias. Symptoms and level of functioning were assessed using the Comprehensive Assessment of At-Risk Mental States scale (CAARMS) and the Global Assessment of Functioning (GAF), respectively. During follow up, 58 (16.1%) of the CHR group developed psychosis (CHR-T), and 245 did not (CHR-NT). Logistic regressions, multilevel mixed models, and Cox regression were used to analyse the relationship between reasoning bias and transition to psychosis and level of functioning, at each time point. Results There was no association between reasoning bias at baseline and the subsequent onset of psychosis. However, when assessed after the transition to psychosis, CHR-T participants showed a greater tendency to jump to conclusions than CHR-NT and HC participants (55, 17, 17%; χ2 = 8.13, p = 0.012). There was a significant association between jumping to conclusions (JTC) at baseline and a reduced level of functioning at 2-year follow-up in the CHR group after adjusting for transition, gender, ethnicity, age, and IQ. Conclusions In CHR participants, JTC at baseline was associated with adverse functioning at the follow-up. Interventions designed to improve JTC could be beneficial in the CHR population.

scholarly journals Clinical and functional ultra-long-term outcome of patients with a clinical high risk (CHR) for psychosis

2019 ◽  
Vol 62 ◽  
pp. 30-37 ◽  
Author(s):  
Katharina Beck ◽  
Erich Studerus ◽  
Christina Andreou ◽  
Laura Egloff ◽  
Letizia Leanza ◽  
...  
Keyword(s):  
High Risk ◽  
Psychosocial Functioning ◽  
Original Sample ◽  
Clinical High Risk ◽  
Term Outcome ◽  
Risk Status ◽  
Long Term Outcome ◽  
Long Term Follow Up

Abstract Background: Few studies have followed up patients with a clinical high risk (CHR) for psychosis for more than 2–3 years. We aimed to investigate the rates and baseline predictors for remission from CHR and transition to psychosis over a follow-up period of up to 16 years. Additionally, we examined the clinical and functional long-term outcome of CHR patients who did not transition. Methods: We analyzed the long-term course of CHR patients that had been included in the longitudinal studies “Früherkennung von Psychosen” (FePsy) or “Bruderholz” (BHS). Those patients who had not transitioned to psychosis during the initial follow-up periods (2/5 years), were invited for additional follow-ups. Results: Originally, 255 CHR patients had been included. Of these, 47 had transitioned to psychosis during the initial follow-ups. Thus, 208 were contacted for the long-term follow-up, of which 72 (34.6%) participated. From the original sample of 255, 26%, 31%, 35%, and 38% were estimated to have transitioned after 3, 5, 10, and 16 years, respectively, and 51% had remitted from their high risk status at the latest follow-up. Better psychosocial functioning at baseline was associated with a higher rate of remission. Of the 72 CHR patients re-assessed at long-term follow-up, 60 had not transitioned, but only 28% of those were fully recovered clinically and functionally. Conclusions: Our study shows the need for follow-ups and clinical attention longer than the usual 2–3 years as there are several CHR patients with later transitions and only a minority of CHR those without transition fully recovers.

scholarly journals Baseline Cortical Thickness Reductions in Clinical High Risk for Psychosis: Brain Regions Associated with Conversion to Psychosis Versus Non-Conversion as Assessed at One-Year Follow-Up in the Shanghai-At-Risk-for-Psychosis (SHARP) Study

2020 ◽  
Author(s):  
Elisabetta C Del Re ◽  
William S Stone ◽  
Sylvain Bouix ◽  
Johanna Seitz ◽  
Victor Zeng ◽  
...  
Keyword(s):  
High Risk ◽  
Language Processing ◽  
Cortical Thickness ◽  
Brain Regions ◽  
Group Differences ◽  
Clinical High Risk ◽  
Pars Opercularis ◽  
One Year ◽  
First Time

Abstract Objective To assess cortical thickness (CT) and surface area (SA) of frontal, temporal, and parietal brain regions in a large clinical high risk for psychosis (CHR) sample, and to identify cortical brain abnormalities in CHR who convert to psychosis and in the whole CHR sample, compared with the healthy controls (HC). Methods Magnetic resonance imaging, clinical, and cognitive data were acquired at baseline in 92 HC, 130 non-converters, and 22 converters (conversion assessed at 1-year follow-up). CT and SA at baseline were calculated for frontal, temporal, and parietal subregions. Correlations between regions showing group differences and clinical scores and age were also obtained. Results CT but not SA was significantly reduced in CHR compared with HC. Two patterns of findings emerged: (1) In converters, CT was significantly reduced relative to non-converters and controls in the banks of superior temporal sulcus, Heschl’s gyrus, and pars triangularis and (2) CT in the inferior parietal and supramarginal gyrus, and at trend level in the pars opercularis, fusiform, and middle temporal gyri was significantly reduced in all high-risk individuals compared with HC. Additionally, reduced CT correlated significantly with older age in HC and in non-converters but not in converters. Conclusions These results show for the first time that fronto-temporo-parietal abnormalities characterized all CHR, that is, both converters and non-converters, relative to HC, while CT abnormalities in converters relative to CHR-NC and HC were found in core auditory and language processing regions.

scholarly journals S94. PREDICTION OF CANNABIS RELAPSE IN CLINICAL HIGH-RISK INDIVIDUALS AND RECENT ONSET PSYCHOSIS - PRELIMINARY RESULTS FROM THE PRONIA STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S69-S70
Author(s):  
Nora Penzel ◽  
Rachele Sanfelici ◽  
Linda Betz ◽  
Linda Antonucci ◽  
Peter Falkai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Random Forest ◽  
Cannabis Use ◽  
Environmental Data ◽  
Clinical High Risk ◽  
Recent Onset ◽  
Cannabis Consumption ◽  
General Functioning

Abstract Background Evidence exists that cannabis consumption is associated with the development of psychosis. Further, continued cannabis use in individuals with recent onset psychosis (ROP) increases the risk for rehospitalization, high symptom severity and low general functioning. Clear inter-individual differences in the vulnerability to the harmful effects of the drug have been pointed out. These findings emphasize the importance of investigating the inter-individual variability in the role of cannabis use in ROP and to understand how cannabis use relates to subclinical conditions that predate the full-blown disease in clinical high-risk (CHR). Specific symptoms have been linked with continued cannabis consume, still research is lacking on how different factors contribute together to an elevated risk of cannabis relapse. Multivariate techniques have the capacity to extract complex patterns from high dimensional data and apply generalized rules to unseen cases. The aim of the study is therefore to assess the predictability of cannabis relapse in ROP and CHR by applying machine learning to clinical and environmental data. Methods All participants were recruited within the multi-site, longitudinal PRONIA study (www.pronia.eu). 112 individuals (58 ROP and 54 CHR) from 8 different European research centres reported lifetime cannabis consume at baseline and were abstinent for at least 4 weeks. We defined cannabis relapse as any cannabis consume between baseline and 9 months follow-up reported by the individual. To predict cannabis relapse, we trained a random forest algorithm implemented in the mlr package, R version 3.5.2. on 183 baseline variables including clinical symptoms, general functioning, demographics and consume patterns within a repeated-nested cross-validation framework. The data underwent pre-processing through pruning of non-informative variables and median-imputation for missing values. The number of trees was set to 500, while the number of nodes, sample fraction and mtry were optimized. All hyperparameters were tuned with the model-based optimization implemented in the mlrMBO R package. Results After 9 months 50 individuals (48 % ROP, 52 % CHR) have relapsed on cannabis use. Relapse was over all timepoints associated with more severe psychotic symptoms measured by PANSS positive and PANSS general (p<0.05) and a significant interaction between positive symptoms and time of measurement (p<0.05). Our random forest classifier could predict cannabis relapse with a balanced accuracy, sensitivity, and specificity of, respectively, 66.5 %, 66.0 % and 67.0 %. The most predictive variables were a higher cumulative frequency of cannabis consumption in the last 3 months, worse general functioning in the last month, higher density of place of living, younger age and a shorter interval time since the last consumption. Discussion Our results using a state-of-the-art machine learning approach suggest that the multivariate signature of baseline demographic and clinical data could predict follow up cannabis relapse above chance level in CHR and ROP. Our findings revealing that cannabis relapse is associated with more severe symptoms is in line with previous literature and emphasizes the need for targeted treatment towards abstinence from cannabis. The information of demographic and clinical patterns might be useful in order to specifically address therapeutic strategies in individuals at higher risk for relapse. This might include special programs for younger patients and taking into account the place of living, like urban areas. Further research is needed in order to validate our model in an independent sample.

Subtypes of Clinical High Risk for Psychosis that Predict Antipsychotic Effectiveness in Long-Term Remission

2020 ◽  
Vol 54 (01) ◽  
pp. 23-30
Author(s):  
TianHong Zhang ◽  
JunJie Wang ◽  
LiHua Xu ◽  
YanYan Wei ◽  
XiaoChen Tang ◽  
...  
Keyword(s):  
High Risk ◽  
Cognitive Deficits ◽  
Negative Symptoms ◽  
Treatment Decision ◽  
Positive Symptom ◽  
Clinical High Risk ◽  
Clinical Value ◽  
Medication History ◽  
Treatment Decision Making

Abstract Introduction In a previous report, we used canonical correlation analysis to classify individuals with clinical high risk (CHR) of psychosis into the 3 subtypes: subtype-1, characterized by extensive negative symptoms and cognitive deficits, appeared to have the highest risk for conversion to psychosis; subtype-2, characterized by thought and behavioral disorganization, with moderate cognitive impairment; subtype-3, characterized by the mildest symptoms and cognitive deficits. The present study attempted to identify these subtypes’ response to antipsychotic (AP) treatment. Methods A total of 289 individuals with CHR were identified and followed up for 2 years. Individuals with CHR were classified by subtype. Use of APs was examined at 2-month, 1-year, and 2-year follow-up interviews that inquired after the subjects’ medication history since the first visit. The main outcome was remission, determined according to global assessment of function (GAF) score (i. e., functional outcome) and SIPS positive symptom score (symptomatic outcome) at the follow-up points. Results Among the 289 individuals with CHR included in the current analysis, 223 (77.2%) were treated using APs for at least 2 weeks during the follow-up period. Individuals with CHR tended to show significant improvement in both symptoms and function after 2 years, but subtypes exhibited significantly different trajectories. Subtype status can predict AP treatment outcome in terms of remission. The likelihood of remission differed significantly among the subtype groups. The remission rates for individuals with subtypes 1–3 treated using AP were 13.5%, 36.1%, and 67.0%, respectively. Discussion These subtypes may be of clinical value in AP treatment decision-making in the CHR population.

Psychosocial outcome in patients at clinical high risk of psychosis: a prospective follow-up

2012 ◽  
Vol 48 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Raimo K. R. Salokangas ◽  
◽  
Dorien H. Nieman ◽  
Markus Heinimaa ◽  
Tanja Svirskis ◽  
...  
Keyword(s):  
High Risk ◽  
Psychosocial Outcome ◽  
Clinical High Risk

Axis I diagnoses and transition to psychosis in clinical high-risk patients EPOS project: Prospective follow-up of 245 clinical high-risk outpatients in four countries

2012 ◽  
Vol 138 (2-3) ◽  
pp. 192-197 ◽  
Author(s):  
Raimo K.R. Salokangas ◽  
Stephan Ruhrmann ◽  
Heinrich Graf von Reventlow ◽  
Markus Heinimaa ◽  
Tanja Svirskis ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical High Risk ◽  
High Risk Patients ◽  
Risk Patients ◽  
Axis I
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