Psychosis prediction model could facilitate early intervention
By Joanna Lyford, Senior medwireNews Reporter
25 October 2013
Schizophr Bull 2013; Advance online publication

medwireNews: A integrated model has shown “outstanding” ability to predict the transition to psychosis in clinically high-risk (CHR) patients, report investigators in Schizophrenia Bulletin.

The model could potentially be used as a tool for individualized risk estimation, thereby allowing targeted early intervention, say the researchers.

Dorien Nieman (Academic Medical Center, Amsterdam, the Netherlands) and colleagues based their model on findings from the Dutch Prediction of Psychosis Study. This study assessed the potential predictive value of variables in five domains: neuropsychology, clinical variables, environmental factors, premorbid adjustment, and neurophysiology.

The model developed by Nieman et al took the most predictive variable in each of the five domains – namely, semantic verbal fluency; the item “social anhedonia and withdrawal” on the Structured Interview for Prodromal Syndromes; urbanicity; social-sexual aspects of life during early adolescence and social-personal adjustment on the Premorbid Adjustment Scale (PAS); and parietal P300 amplitude.

The team then applied the five variables to 61 CHR individuals aged 12-35 years with suspected prepsychotic development. All individuals were assessed at baseline and followed-up for 36 months, during which time 18 (29.5%) had made the transition to psychosis. Final diagnoses were schizophrenia (n=12), schizophreniform disorder (n=3), schizoaffective disorder (n=2), and brief psychotic disorder (n=1).

In Cox regression analysis, just two of the five variables were significantly associated with psychosis: parietal P300 amplitude (hazard ratio [HR]=1.27 for each 1-µv decrease) and premorbid social-personal adjustment on the PAS (HR=2.13 for each 1-point increase).

A model that combined these two variables had an area under the receiver operating characteristic curve of 0.91, giving an “outstanding” predictive ability.

The team then calculated individual prognostic scores using the same two variables and found that patients could be stratified into three statistically distinct groups, or “risk classes.”

Class I patients had a 3.7% transition rate and an estimated time to transition (ETT) of 35.5 months; class II patients had a 25.0% transition rate and an ETT of 31.9 months; and class III patients had a 73.7% transition rate and an ETT of 18.0 months.

“In the lowest risk class, none of the subjects transitioned within a year, while in the highest risk class, 47.4% of the subjects transitioned within this time frame, which should have a significant impact on interventional measures,” remark Nieman et al.

However they admit: “[T]ransferring our approach into clinical practice requires validation in an independent sample.”

medwireNews (www.medwirenews.com) is an independent clinical news service provided by Springer Healthcare Limited. © Springer Healthcare Ltd; 2013

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