CONTEXT: Early detection and prospective evaluation of individuals who will develop schizophrenia or other psychotic disorders are critical to efforts to isolate mechanisms underlying psychosis onset and to the testing of preventive interventions, but existing risk prediction approaches have achieved only modest predictive accuracy. OBJECTIVES: To determine the risk of conversion to psychosis and to evaluate a set of prediction algorithms maximizing positive predictive power in a clinical high-risk sample. DESIGN, SETTING, AND PARTICIPANTS: Longitudinal study with a 2 1/2-year follow-up of 291 prospectively identified treatment-seeking patients meeting Structured Interview for Prodromal Syndromes criteria. The patients were recruited and underwent evaluation across 8 clinical research centers as part of the North American Prodrome Longitudinal Study. MAIN OUTCOME MEASURE: Time to conversion to a fully psychotic form of mental illness. RESULTS: The risk of conversion to psychosis was 35%, with a decelerating rate of transition during the 2 1/2-year follow-up. Five features assessed at baseline contributed uniquely to the prediction of psychosis: a genetic risk for schizophrenia with recent deterioration in functioning, higher levels of unusual thought content, higher levels of suspicion/paranoia, greater social impairment, and a history of substance abuse. Prediction algorithms combining 2 or 3 of these variables resulted in dramatic increases in positive predictive power (ie, 68%-80%) compared with the prodromal criteria alone. CONCLUSIONS: These findings demonstrate that prospective ascertainment of individuals at risk for psychosis is feasible, with a level of predictive accuracy comparable to that in other areas of preventive medicine. They provide a benchmark for the rate and shape of the psychosis risk function against which standardized preventive intervention programs can be compared.
CONTEXT: Early detection and prospective evaluation of individuals who will develop schizophrenia or other psychotic disorders are critical to efforts to isolate mechanisms underlying psychosis onset and to the testing of preventive interventions, but existing risk prediction approaches have achieved only modest predictive accuracy. OBJECTIVES: To determine the risk of conversion to psychosis and to evaluate a set of prediction algorithms maximizing positive predictive power in a clinical high-risk sample. DESIGN, SETTING, AND PARTICIPANTS: Longitudinal study with a 2 1/2-year follow-up of 291 prospectively identified treatment-seeking patients meeting Structured Interview for Prodromal Syndromes criteria. The patients were recruited and underwent evaluation across 8 clinical research centers as part of the North American Prodrome Longitudinal Study. MAIN OUTCOME MEASURE: Time to conversion to a fully psychotic form of mental illness. RESULTS: The risk of conversion to psychosis was 35%, with a decelerating rate of transition during the 2 1/2-year follow-up. Five features assessed at baseline contributed uniquely to the prediction of psychosis: a genetic risk for schizophrenia with recent deterioration in functioning, higher levels of unusual thought content, higher levels of suspicion/paranoia, greater social impairment, and a history of substance abuse. Prediction algorithms combining 2 or 3 of these variables resulted in dramatic increases in positive predictive power (ie, 68%-80%) compared with the prodromal criteria alone. CONCLUSIONS: These findings demonstrate that prospective ascertainment of individuals at risk for psychosis is feasible, with a level of predictive accuracy comparable to that in other areas of preventive medicine. They provide a benchmark for the rate and shape of the psychosis risk function against which standardized preventive intervention programs can be compared.
Authors: Elaine F Walker; Patricia A Brennan; Michelle Esterberg; Joy Brasfield; Brad Pearce; Michael T Compton Journal: J Abnorm Psychol Date: 2010-05
Authors: Vijay A Mittal; Maria Jalbrzikowski; Melita Daley; Cristina Roman; Carrie E Bearden; Tyrone D Cannon Journal: Schizophr Res Date: 2011-06-01 Impact factor: 4.939
Authors: Larry J Seidman; Anthony J Giuliano; Eric C Meyer; Jean Addington; Kristin S Cadenhead; Tyrone D Cannon; Thomas H McGlashan; Diana O Perkins; Ming T Tsuang; Elaine F Walker; Scott W Woods; Carrie E Bearden; Bruce K Christensen; Keith Hawkins; Robert Heaton; Richard S E Keefe; Robert Heinssen; Barbara A Cornblatt Journal: Arch Gen Psychiatry Date: 2010-06
Authors: Judy L Thompson; Meredith Kelly; David Kimhy; Jill M Harkavy-Friedman; Shamir Khan; Julie W Messinger; Scott Schobel; Ray Goetz; Dolores Malaspina; Cheryl Corcoran Journal: Schizophr Res Date: 2009-01-25 Impact factor: 4.939