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Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis
INTRODUCTION: Identification of participants at clinical high-risk (CHR) for the development of psychosis is an important objective of current preventive efforts in mental health research. However, the utility of using web-based screening approaches to detect CHR participants at the population level...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6483579/ https://www.ncbi.nlm.nih.gov/pubmed/29889271 http://dx.doi.org/10.1093/schbul/sby069 |
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author | McDonald, Mhairi Christoforidou, Eleni Van Rijsbergen, Nicola Gajwani, Ruchika Gross, Joachim Gumley, Andrew I Lawrie, Stephen M Schwannauer, Matthias Schultze-Lutter, Frauke Uhlhaas, Peter J |
author_facet | McDonald, Mhairi Christoforidou, Eleni Van Rijsbergen, Nicola Gajwani, Ruchika Gross, Joachim Gumley, Andrew I Lawrie, Stephen M Schwannauer, Matthias Schultze-Lutter, Frauke Uhlhaas, Peter J |
author_sort | McDonald, Mhairi |
collection | PubMed |
description | INTRODUCTION: Identification of participants at clinical high-risk (CHR) for the development of psychosis is an important objective of current preventive efforts in mental health research. However, the utility of using web-based screening approaches to detect CHR participants at the population level has not been investigated. METHODS: We tested a web-based screening approach to identify CHR individuals. Potential participants were invited to a website via e-mail invitations, flyers, and invitation letters involving both the general population and mental health services. Two thousand two hundred seventy-nine participants completed the 16-item version of the prodromal questionnaire (PQ-16) and a 9-item questionnaire of perceptual and cognitive aberrations (PCA) for the assessment of basic symptoms (BS) online. 52.3% of participants met a priori cut-off criteria for the PQ and 73.6% for PCA items online. One thousand seven hundred eighty-seven participants were invited for a clinical interview and n = 356 interviews were conducted (response rate: 19.9%) using the Comprehensive Assessment of At-Risk Mental State (CAARMS) and the Schizophrenia Proneness Interview, Adult Version (SPI-A). n = 101 CHR participants and n = 8 first-episode psychosis (FEP) were detected. ROC curve analysis revealed good to moderate sensitivity and specificity for predicting CHR status based on online results for both UHR and BS criteria (sensitivity/specificity: PQ-16 = 82%/46%; PCA = 94%/12%). Selection of a subset of 10 items from both PQ-16 and PCA lead to an improved of specificity of 57% while only marginally affecting sensitivity (81%). CHR participants were characterized by similar levels of functioning and neurocognitive deficits as clinically identified CHR groups. CONCLUSION: These data provide evidence for the possibility to identify CHR participants through population-based web screening. This could be an important strategy for early intervention and diagnosis of psychotic disorders. |
format | Online Article Text |
id | pubmed-6483579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64835792019-04-30 Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis McDonald, Mhairi Christoforidou, Eleni Van Rijsbergen, Nicola Gajwani, Ruchika Gross, Joachim Gumley, Andrew I Lawrie, Stephen M Schwannauer, Matthias Schultze-Lutter, Frauke Uhlhaas, Peter J Schizophr Bull Regular Articles INTRODUCTION: Identification of participants at clinical high-risk (CHR) for the development of psychosis is an important objective of current preventive efforts in mental health research. However, the utility of using web-based screening approaches to detect CHR participants at the population level has not been investigated. METHODS: We tested a web-based screening approach to identify CHR individuals. Potential participants were invited to a website via e-mail invitations, flyers, and invitation letters involving both the general population and mental health services. Two thousand two hundred seventy-nine participants completed the 16-item version of the prodromal questionnaire (PQ-16) and a 9-item questionnaire of perceptual and cognitive aberrations (PCA) for the assessment of basic symptoms (BS) online. 52.3% of participants met a priori cut-off criteria for the PQ and 73.6% for PCA items online. One thousand seven hundred eighty-seven participants were invited for a clinical interview and n = 356 interviews were conducted (response rate: 19.9%) using the Comprehensive Assessment of At-Risk Mental State (CAARMS) and the Schizophrenia Proneness Interview, Adult Version (SPI-A). n = 101 CHR participants and n = 8 first-episode psychosis (FEP) were detected. ROC curve analysis revealed good to moderate sensitivity and specificity for predicting CHR status based on online results for both UHR and BS criteria (sensitivity/specificity: PQ-16 = 82%/46%; PCA = 94%/12%). Selection of a subset of 10 items from both PQ-16 and PCA lead to an improved of specificity of 57% while only marginally affecting sensitivity (81%). CHR participants were characterized by similar levels of functioning and neurocognitive deficits as clinically identified CHR groups. CONCLUSION: These data provide evidence for the possibility to identify CHR participants through population-based web screening. This could be an important strategy for early intervention and diagnosis of psychotic disorders. Oxford University Press 2019-04 2018-06-08 /pmc/articles/PMC6483579/ /pubmed/29889271 http://dx.doi.org/10.1093/schbul/sby069 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Regular Articles McDonald, Mhairi Christoforidou, Eleni Van Rijsbergen, Nicola Gajwani, Ruchika Gross, Joachim Gumley, Andrew I Lawrie, Stephen M Schwannauer, Matthias Schultze-Lutter, Frauke Uhlhaas, Peter J Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title | Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title_full | Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title_fullStr | Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title_full_unstemmed | Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title_short | Using Online Screening in the General Population to Detect Participants at Clinical High-Risk for Psychosis |
title_sort | using online screening in the general population to detect participants at clinical high-risk for psychosis |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6483579/ https://www.ncbi.nlm.nih.gov/pubmed/29889271 http://dx.doi.org/10.1093/schbul/sby069 |
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