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Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This syst...

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Autores principales: Meehan, Alan J., Lewis, Stephanie J., Fazel, Seena, Fusar-Poli, Paolo, Steyerberg, Ewout W., Stahl, Daniel, Danese, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156409/
https://www.ncbi.nlm.nih.gov/pubmed/35365801
http://dx.doi.org/10.1038/s41380-022-01528-4
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author Meehan, Alan J.
Lewis, Stephanie J.
Fazel, Seena
Fusar-Poli, Paolo
Steyerberg, Ewout W.
Stahl, Daniel
Danese, Andrea
author_facet Meehan, Alan J.
Lewis, Stephanie J.
Fazel, Seena
Fusar-Poli, Paolo
Steyerberg, Ewout W.
Stahl, Daniel
Danese, Andrea
author_sort Meehan, Alan J.
collection PubMed
description Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study’s risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.
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spelling pubmed-91564092022-06-02 Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges Meehan, Alan J. Lewis, Stephanie J. Fazel, Seena Fusar-Poli, Paolo Steyerberg, Ewout W. Stahl, Daniel Danese, Andrea Mol Psychiatry Systematic Review Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study’s risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care. Nature Publishing Group UK 2022-04-01 2022 /pmc/articles/PMC9156409/ /pubmed/35365801 http://dx.doi.org/10.1038/s41380-022-01528-4 Text en © The Author(s) 2022 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
Meehan, Alan J.
Lewis, Stephanie J.
Fazel, Seena
Fusar-Poli, Paolo
Steyerberg, Ewout W.
Stahl, Daniel
Danese, Andrea
Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title_full Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title_fullStr Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title_full_unstemmed Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title_short Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
title_sort clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156409/
https://www.ncbi.nlm.nih.gov/pubmed/35365801
http://dx.doi.org/10.1038/s41380-022-01528-4
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