Cannabis use, self‐perceived risk, perceived peer approval and parental attitudes among youth at clinical high‐risk for psychosis

2021 ◽  
Author(s):  
Ivanka Ristanovic ◽  
Katherine S. F. Damme ◽  
Jordan E. DeVylder ◽  
Jason Schiffman ◽  
Vijay A. Mittal
Keyword(s):  
High Risk ◽  
Perceived Risk ◽  
Parental Attitudes ◽  
Cannabis Use ◽  
Clinical High Risk ◽  
Peer Approval

scholarly journals Evaluating the impact of cannabis use on thalamic connectivity in youth at clinical high risk of psychosis

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Lisa Buchy ◽  
Tyrone D. Cannon ◽  
Alan Anticevic ◽  
Kristina Lyngberg ◽  
Kristin S. Cadenhead ◽  
...  
Keyword(s):  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk ◽  
The Impact

scholarly journals Relation between cannabis use and subcortical volumes in people at clinical high risk of psychosis

2016 ◽  
Vol 254 ◽  
pp. 3-9 ◽  
Author(s):  
Lisa Buchy ◽  
Daniel H. Mathalon ◽  
Tyrone D. Cannon ◽  
Kristin S. Cadenhead ◽  
Barbara A. Cornblatt ◽  
...  
Keyword(s):  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk ◽  
Subcortical Volumes

scholarly journals Cannabis use and age at onset of symptoms in subjects at clinical high risk for psychosis

2011 ◽  
Vol 125 (1) ◽  
pp. 45-53 ◽  
Author(s):  
S. Dragt ◽  
D. H. Nieman ◽  
F. Schultze-Lutter ◽  
F. van der Meer ◽  
H. Becker ◽  
...  
Keyword(s):  
High Risk ◽  
Age At Onset ◽  
Cannabis Use ◽  
Clinical High Risk

Poster #184 CANNABIS USE IN PATIENTS AT CLINICAL HIGH RISK OF PSYCHOSIS: IMPACT ON PRODROMAL SYMPTOMS AND TRANSITION TO PSYCHOSIS – A SYSTEMATIC REVIEW

2012 ◽  
Vol 136 ◽  
pp. S347
Author(s):  
Floor J. van der Meer ◽  
Eva Veithorst ◽  
Carin J. Meijer ◽  
Marise W. Machielsen ◽  
Paolo Fusar-Poli ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk ◽  
Prodromal Symptoms

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.

Cannabis Use in Patients at Clinical High Risk of Psychosis: Impact on Prodromal Symptoms and Transition to Psychosis

2012 ◽  
Vol 18 (32) ◽  
pp. 5036-5044 ◽  
Author(s):  
Floor J. van der Meer ◽  
Eva Velthorst ◽  
Carin J. Meijer ◽  
Marise W.J. Machielsen ◽  
Lieuwe de Haan
Keyword(s):  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk ◽  
Prodromal Symptoms

scholarly journals Stress-Induced Dopamine Response in Subjects at Clinical High Risk for Schizophrenia with and without Concurrent Cannabis Use

2013 ◽  
Vol 39 (6) ◽  
pp. 1479-1489 ◽  
Author(s):  
Romina Mizrahi ◽  
Miran Kenk ◽  
Ivonne Suridjan ◽  
Isabelle Boileau ◽  
Tony P George ◽  
...  
Keyword(s):  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk

A mobile-based app to monitor cannabis use among youth at clinical high risk (CHR) for psychosis: Feasibility and acceptability of LooseLeaf

2020 ◽  
Vol 222 ◽  
pp. 505-506
Author(s):  
Olga Santesteban-Echarri ◽  
Preston Haffey ◽  
GaHyung Kim ◽  
Jacky Tang ◽  
Jean Addington
Keyword(s):  
High Risk ◽  
Cannabis Use ◽  
Clinical High Risk
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