B B Brodey1, R R Girgis2, O V Favorov3, C E Bearden4, S W Woods5, J Addington6, D O Perkins7, E F Walker8, B A Cornblatt9, G Brucato10, S E Purcell11, I S Brodey12, K S Cadenhead13. 1. TeleSage, Inc., 201 East Rosemary St., Chapel Hill, NC 27514, USA. Electronic address: bb@telesage.com. 2. New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA. Electronic address: Ragy.Girgis@nyspi.columbia.edu. 3. Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 152 MacNider Hall, Campus Box 7575, Chapel Hill, NC 27599, USA. Electronic address: favorov@email.unc.edu. 4. Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, USA. Electronic address: cbearden@mednet.ucla.edu. 5. PRIME Psychosis Prodrome Research Clinic, Connecticut Mental Health Center B-38, 34 Park Street, New Haven, CT 06519, USA. Electronic address: scott.woods@yale.edu. 6. Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta T2N 4Z6, Canada. Electronic address: jmadding@ucalgary.ca. 7. Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Dr, Chapel Hill, NC 27514, USA. Electronic address: diana_perkins@med.unc.edu. 8. Departments of Psychology and Psychiatry, Emory University, 36 Eagle Row, Atlanta, GA 30322, USA. Electronic address: psyefw@emory.edu. 9. Department of Psychiatry Research, The Zucker Hillside Hospital, 75-59 263rd St., Glen Oaks, New York 11004, USA. Electronic address: BCornblatt@northwell.edu. 10. New York State Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA. Electronic address: Gary.Brucato@nyspi.columbia.edu. 11. TeleSage, Inc., 201 East Rosemary St., Chapel Hill, NC 27514, USA. Electronic address: spurcell@telesage.com. 12. Department of English and Comparative Literature, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA. Electronic address: brodey@email.unc.edu. 13. Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0810, USA. Electronic address: kcadenhead@ucsd.edu.
Abstract
INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention. METHODS: Self-report Likert-scale survey items were administered to individuals being screened with the Structured Interview for Psychosis-risk Syndromes (SIPS) and followed at eight early psychosis clinics. An a priori analytic plan included Spectral Clustering Analysis to reduce the item pool, followed by development of Support Vector Machine (SVM) classifiers. RESULTS: The cross-validated positive predictive value (PPV) of the EPSI at the default cut-off (76.5%) exceeded that of the clinician-administered SIPS (68.5%) at separating individuals who would not convert to psychosis within 12 months from those who either would convert within 12 months or who had already experienced a first episode psychosis (FEP). When used in tandem with the SIPS on clinical high risk participants, the EPSI increased the combined PPV to 86.6%. The SVM classified as FEP/converters only 1% of individuals in non-clinical and 4% of clinical low risk populations. Sensitivity of the EPSI, however, was 51% at the default cut-off. DISCUSSION: The EPSI identifies, comparably to the SIPS but in less time and with fewer resources, individuals who are either at very high risk to develop a psychotic disorder within 12 months or who are already psychotic. At its default cut-off, EPSI misses 49% of current or future psychotic cases. The cut-off can, however, be adjusted based on purpose. The EPSI is the first validated assessment to predict 12-month psychotic conversion. An online screening system, www.eps.telesage.org, is under development.
INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention. METHODS: Self-report Likert-scale survey items were administered to individuals being screened with the Structured Interview for Psychosis-risk Syndromes (SIPS) and followed at eight early psychosis clinics. An a priori analytic plan included Spectral Clustering Analysis to reduce the item pool, followed by development of Support Vector Machine (SVM) classifiers. RESULTS: The cross-validated positive predictive value (PPV) of the EPSI at the default cut-off (76.5%) exceeded that of the clinician-administered SIPS (68.5%) at separating individuals who would not convert to psychosis within 12 months from those who either would convert within 12 months or who had already experienced a first episode psychosis (FEP). When used in tandem with the SIPS on clinical high risk participants, the EPSI increased the combined PPV to 86.6%. The SVM classified as FEP/converters only 1% of individuals in non-clinical and 4% of clinical low risk populations. Sensitivity of the EPSI, however, was 51% at the default cut-off. DISCUSSION: The EPSI identifies, comparably to the SIPS but in less time and with fewer resources, individuals who are either at very high risk to develop a psychotic disorder within 12 months or who are already psychotic. At its default cut-off, EPSI misses 49% of current or future psychotic cases. The cut-off can, however, be adjusted based on purpose. The EPSI is the first validated assessment to predict 12-month psychotic conversion. An online screening system, www.eps.telesage.org, is under development.
Authors: Joel Weijia Lai; Candice Ke En Ang; U Rajendra Acharya; Kang Hao Cheong Journal: Int J Environ Res Public Health Date: 2021-06-05 Impact factor: 3.390