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Determination of psychosis-related clinical profiles in children with autism spectrum disorders using latent class analysis

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)301-307
Number of pages7
JournalEuropean child & adolescent psychiatry
Issue number3
Early online date26 Jun 2014
E-pub ahead of print26 Jun 2014
PublishedMar 2015


King's Authors


In children with autism spectrum disorders (ASD), high rates of idiosyncratic fears and anxiety reactions and thought disorder are thought to increase the risk of psychosis. The critical next step is to identify whether combinations of these symptoms can be used to categorise individual patients into ASD subclasses, and to test their relevance to psychosis. All patients with ASD (n = 84) admitted to a specialist national inpatient unit from 2003 to 2012 were rated for the presence or absence of impairment in affective regulation and anxiety (peculiar phobias, panic episodes, explosive reactions to anxiety), social deficits (social disinterest, avoidance or withdrawal and abnormal attachment) and thought disorder (disorganised or illogical thinking, bizarre fantasies, overvalued or delusional ideas). Latent class analysis of individual symptoms was conducted to identify ASD classes. External validation of these classes was performed using as a criterion the presence of hallucinations. Latent class analysis identified two distinct classes. Bizarre fears and anxiety reactions and thought disorder symptoms differentiated ASD patients into those with psychotic features (ASD-P: 51 %) and those without (ASD-NonP: 49 %). Hallucinations were present in 26 % of the ASD-P class but only 2.4 % of the ASD-NonP. Both the ASD-P and the ASD-NonP class benefited from inpatient treatment although inpatient stay was prolonged in the ASD-P class. This study provides the first empirically derived classification of ASD in relation to psychosis based on three underlying symptom dimensions, anxiety, social deficits and thought disorder. These results can be further developed by testing the reproducibility and prognostic value of the identified classes.

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