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Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historic...

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Autores principales: Clark, S R, Baune, B T, Schubert, K O, Lavoie, S, Smesny, S, Rice, S M, Schäfer, M R, Benninger, F, Feucht, M, Klier, C M, McGorry, P D, Amminger, G P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048208/
https://www.ncbi.nlm.nih.gov/pubmed/27648919
http://dx.doi.org/10.1038/tp.2016.170
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author Clark, S R
Baune, B T
Schubert, K O
Lavoie, S
Smesny, S
Rice, S M
Schäfer, M R
Benninger, F
Feucht, M
Klier, C M
McGorry, P D
Amminger, G P
author_facet Clark, S R
Baune, B T
Schubert, K O
Lavoie, S
Smesny, S
Rice, S M
Schäfer, M R
Benninger, F
Feucht, M
Klier, C M
McGorry, P D
Amminger, G P
author_sort Clark, S R
collection PubMed
description Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.
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spelling pubmed-50482082016-10-18 Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers Clark, S R Baune, B T Schubert, K O Lavoie, S Smesny, S Rice, S M Schäfer, M R Benninger, F Feucht, M Klier, C M McGorry, P D Amminger, G P Transl Psychiatry Original Article Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview. Nature Publishing Group 2016-09 2016-09-20 /pmc/articles/PMC5048208/ /pubmed/27648919 http://dx.doi.org/10.1038/tp.2016.170 Text en Copyright © 2016 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Original Article
Clark, S R
Baune, B T
Schubert, K O
Lavoie, S
Smesny, S
Rice, S M
Schäfer, M R
Benninger, F
Feucht, M
Klier, C M
McGorry, P D
Amminger, G P
Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title_full Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title_fullStr Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title_full_unstemmed Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title_short Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
title_sort prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048208/
https://www.ncbi.nlm.nih.gov/pubmed/27648919
http://dx.doi.org/10.1038/tp.2016.170
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