Diana O Perkins1, Clark D Jeffries2, Barbara A Cornblatt3, Scott W Woods4, Jean Addington5, Carrie E Bearden6, Kristin S Cadenhead7, Tyrone D Cannon8, Robert Heinssen9, Daniel H Mathalon10, Larry J Seidman11, Ming T Tsuang7, Elaine F Walker12, Thomas H McGlashan4. 1. Department of Psychiatry, University of North Carolina, Chapel Hill, United States. Electronic address: Diana_Perkins@unc.edu. 2. Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, United States. 3. Department of Psychiatry, Zucker Hillside Hospital, Long Island, NY, United States. 4. Department of Psychiatry, Yale University, New Haven, CT, United States. 5. Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Alberta, Canada. 6. Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, United States; Department of Psychology, UCLA, Los Angeles, CA, United States. 7. Department of Psychiatry, UCSD, San Diego, CA, United States. 8. Department of Psychiatry, Yale University, New Haven, CT, United States; Department of Psychology, Yale University, New Haven, CT, United States. 9. National Institute of Mental Health, United States. 10. Department of Psychiatry, UCSF, San Francisco, CA, United States. 11. Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, MA, United States; Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States. 12. Department of Psychology, Emory University, Atlanta, GA, United States; Department of Psychiatry, Emory University, Atlanta, GA, United States.
Abstract
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.
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.
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