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Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal
Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613280/ https://www.ncbi.nlm.nih.gov/pubmed/37898606 http://dx.doi.org/10.1038/s41398-023-02623-y |
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author | Byrne, Jonah F. Mongan, David Murphy, Jennifer Healy, Colm Fӧcking, Melanie Cannon, Mary Cotter, David R. |
author_facet | Byrne, Jonah F. Mongan, David Murphy, Jennifer Healy, Colm Fӧcking, Melanie Cannon, Mary Cotter, David R. |
author_sort | Byrne, Jonah F. |
collection | PubMed |
description | Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis. |
format | Online Article Text |
id | pubmed-10613280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106132802023-10-30 Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal Byrne, Jonah F. Mongan, David Murphy, Jennifer Healy, Colm Fӧcking, Melanie Cannon, Mary Cotter, David R. Transl Psychiatry Systematic Review Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis. Nature Publishing Group UK 2023-10-28 /pmc/articles/PMC10613280/ /pubmed/37898606 http://dx.doi.org/10.1038/s41398-023-02623-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Systematic Review Byrne, Jonah F. Mongan, David Murphy, Jennifer Healy, Colm Fӧcking, Melanie Cannon, Mary Cotter, David R. Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title | Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title_full | Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title_fullStr | Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title_full_unstemmed | Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title_short | Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
title_sort | prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613280/ https://www.ncbi.nlm.nih.gov/pubmed/37898606 http://dx.doi.org/10.1038/s41398-023-02623-y |
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