Although some statistically significant effects on negative symptoms were evident, none reached the threshold for clinically significant improvement.

Our results indicate that the high-risk state is characterised by consistent and large impairments of functioning and reduction in QoL similar to those in other coded psychiatric disorders.

No abstract

Among UHR patients, persistence or recurrence of non-psychotic comorbid mental disorders, mostly affective disorders, is associated with 6-year poor functional outcomes.

Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%–82% (NNT = 3) with 2 positive tests, 12%–21% (NNT = 11–18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.

No abstract

The AdAS Spectrum showed excellent internal consistency and test-retest reliability and strong convergent validity with alternative dimensional measures of ASD. The questionnaire performed differently among the three diagnostic groups and enlightened some significant effects of gender in the expression of autistic traits.

Background. Several psychometric instruments are available for the diagnostic interview of subjects at ultra high risk (UHR) of psychosis. Their diagnostic comparability is unknown. Methods. All referrals to the OASIS (London) or CAMEO (Cambridgeshire) UHR services from May 13 to Dec 14 were interviewed for a UHR state using both the CAARMS 12/2006 and the SIPS 5.0. Percent overall agreement, kappa, the McNemar-Bowker χ 2 test, equipercentile methods, and residual analyses were used to investigate diagnostic outcomes and symptoms severity or frequency. A conversion algorithm (CONVERT) was validated in an independent UHR sample from the Seoul Youth Clinic (Seoul). Results. There was overall substantial CAARMS-versus-SIPS agreement in the identification of UHR subjects (n = 212, percent overall agreement = 86%; kappa = 0.781, 95% CI from 0.684 to 0.878; McNemar-Bowker test = 0.069), with the exception of the brief limited intermittent psychotic symptoms (BLIPS) subgroup. Equipercentile-linking table linked symptoms severity and frequency across the CAARMS and SIPS. The conversion algorithm was validated in 93 UHR subjects, showing excellent diagnostic accuracy (CAARMS to SIPS: ROC area 0.929; SIPS to CAARMS: ROC area 0.903). Conclusions. This study provides initial comparability data between CAARMS and SIPS and will inform ongoing multicentre studies and clinical guidelines for the UHR psychometric diagnostic interview.