UCSF

Background

Improving predictive accuracy is of paramount importance for early detection and prevention of psychosis. We sought a symptom severity classifier that would improve psychosis risk prediction.

Methods

Subjects were from two cohorts of the North American Prodrome Longitudinal Study. All subjects met Criteria of Psychosis-Risk States. In Cohort-1 (n=296) we developed a classifier that included those items of the Scale of Psychosis-Risk Symptoms that best distinguished subjects who converted to psychosis from nonconverters, with performance initially validated by randomization tests in Cohort-1. Cohort-2 (n=592) served as an independent test set.

Results

We derived 2-Item and 4-Item subscales. Both included unusual thought content and suspiciousness; the latter added reduced ideational richness and difficulties with focus/concentration. The Concordance Index (C-Index), a measure of discrimination, was similar for each subscale across cohorts (4-Item subscale Cohort-2: 0.71, 95% CI=[0.64, 0.77], Cohort-1: 0.74, 95% CI=[0.69, 0.80]; 2-Item subscale Cohort-2: 0.68, 95% CI=[0.3, 0.76], Cohort-1: 0.72, 95% CI=[0.66-0.79]). The 4-Item performed better than the 2-Item subscale in 742/1000 random selections of 80% subsets of Cohort-2 subjects (p-value=1.3E-55). Subscale calibration between cohorts was proportional (higher scores/lower survival), but absolute conversion risk predicted from Cohort-1 was higher than that observed in Cohort-2, reflecting the cohorts' differences in 2-year conversion rates (Cohort-2: 0.16, 95% CI=[0.13, 0.19]; Cohort-1: 0.30, 95% CI=[0.24, 0.36]).

Conclusion

Severity of unusual thought content, suspiciousness, reduced ideational richness, and difficulty with focus/concentration informed psychosis risk prediction. Scales based on these symptoms may have utility in research and, assuming further validation, eventual clinical applications.