Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders.
Sprüngli-Toffel E., Studerus E., Curtis L., Conchon C., Alameda L., Bailey B., Caron C., Haase C., Gros J., Herbrecht E., Huber CG., Riecher-Rössler A., Conus P., Solida A., Armando M., Kapsaridi A., Ducommun MM., Klauser P., Plessen KJ., Urben S., Edan A., Nanzer N., Navarro AL., Schneider M., Genoud D., Michel C., Kindler J., Kaess M., Oliver D., Fusar-Poli P., Borgwardt S., Andreou C.
INTRODUCTION: Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population. METHOD: The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis. RESULTS: Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years. CONCLUSION: Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.