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Schizophr Res. 2018 Jan 18. pii: S0920-9964(17)30728-4. doi: 10.1016/j.schres.2017.11.030. [Epub ahead of print]

The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.

Author information

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, Unit 31, New York, NY 10032, USA. Electronic address: Ragy.girgis@nyspi.columbia.edu.
3
Department of Biomedical Engineering, The 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
Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta T2N 4Z6, Canada. Electronic address: jmadding@ucalgary.ca.
5
Department of Psychiatry, School of Medicine, The University of North Carolina at Chapel Hill, 101 Manning Dr, Chapel Hill, NC 27514, USA. Electronic address: diana_perkins@med.unc.edu.
6
Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California Los Angeles, 300 Medical Plaza, Rm 2265, Los Angeles, CA 90095. Electronic address: cbearden@mednet.ucla.edu.
7
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.
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: cornblat@lij.edu.
10
New York State Psychiatric Institute, 1051 Riverside Drive, Unit 31, New York, NY 10032, USA.
11
PRIME Psychosis Prodrome Research Clinic, Connecticut Mental Health Center B-38, 34 Park Street, New Haven, CT 06519, USA. Electronic address: barbara.walsh@yale.edu.
12
TeleSage, Inc., 201 East Rosemary St., Chapel Hill, NC 27514, USA. Electronic address: kelkin@telesage.com.
13
The University of North Carolina at Chapel Hill, 434 Greenlaw, Campus Box 3520, Chapel Hill, NC 27599. Electronic address: brodey@email.unc.edu.

Abstract

Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire-Brief Version (PQ-B) and 148 additional items were administered to 229 individuals being screened with the SIPS at 7 North American Prodrome Longitudinal Study sites and at Columbia University. Fifty individuals were found to have SIPS scores of 0, 1, or 2, making them clinically low risk (CLR) controls; 144 were classified as clinically high risk (CHR) (SIPS 3-5) and 35 were found to have first episode psychosis (FEP) (SIPS 6). Spectral clustering analysis, performed on 124 of the items, yielded two cohesive item groups, the first mostly related to psychosis and mania, the second mostly related to depression, anxiety, and social and general work/school functioning. Items within each group were sorted according to their usefulness in distinguishing between CLR and CHR individuals using the Minimum Redundancy Maximum Relevance procedure. A receiver operating characteristic area under the curve (AUC) analysis indicated that maximal differentiation of CLR and CHR participants was achieved with a 26-item solution (AUC=0.899±0.001). The EPS-26 outperformed the PQ-B (AUC=0.834±0.001). For screening purposes, the self-report EPS-26 appeared to differentiate individuals who are either CLR or CHR approximately as well as the clinician-administered SIPS. The EPS-26 may prove useful as a self-report screener and may lead to a decrease in the duration of untreated psychosis. A validation of the EPS-26 against actual conversion is underway.

KEYWORDS:

Machine learning; NAPLS; PQ-B; Prodromal; Psychosis; SIPS; Schizophrenia; Screener

PMID:
29358019
PMCID:
PMC6051928
[Available on 2019-07-18]
DOI:
10.1016/j.schres.2017.11.030

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