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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.
Transl Psychiatry. 2016 09 20; 6(9):e897.TP

Abstract

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.

Authors+Show Affiliations

Discipline of Psychiatry, Royal Adelaide Hospital, University of Adelaide, Adelaide, SA, Australia.Discipline of Psychiatry, Royal Adelaide Hospital, University of Adelaide, Adelaide, SA, Australia.Discipline of Psychiatry, Royal Adelaide Hospital, University of Adelaide, Adelaide, SA, Australia.Orygen, The National Centre of Excellence in Youth Mental Health and Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.Department of Psychiatry, University Hospital Jena, Jena, Germany.Orygen, The National Centre of Excellence in Youth Mental Health and Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.Orygen, The National Centre of Excellence in Youth Mental Health and Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria.Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.Orygen, The National Centre of Excellence in Youth Mental Health and Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.Orygen, The National Centre of Excellence in Youth Mental Health and Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

27648919

Citation

Clark, S R., et al. "Prediction of Transition From Ultra-high Risk to First-episode Psychosis Using a Probabilistic Model Combining History, Clinical Assessment and Fatty-acid Biomarkers." Translational Psychiatry, vol. 6, no. 9, 2016, pp. e897.
Clark SR, Baune BT, Schubert KO, et al. Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers. Transl Psychiatry. 2016;6(9):e897.
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. (2016). Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers. Translational Psychiatry, 6(9), e897. https://doi.org/10.1038/tp.2016.170
Clark SR, et al. Prediction of Transition From Ultra-high Risk to First-episode Psychosis Using a Probabilistic Model Combining History, Clinical Assessment and Fatty-acid Biomarkers. Transl Psychiatry. 2016 09 20;6(9):e897. PubMed PMID: 27648919.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers. AU - Clark,S R, AU - Baune,B T, AU - Schubert,K O, AU - Lavoie,S, AU - Smesny,S, AU - Rice,S M, AU - Schäfer,M R, AU - Benninger,F, AU - Feucht,M, AU - Klier,C M, AU - McGorry,P D, AU - Amminger,G P, Y1 - 2016/09/20/ PY - 2015/11/26/received PY - 2016/06/29/revised PY - 2016/07/20/accepted PY - 2016/9/21/entrez PY - 2016/9/21/pubmed PY - 2017/11/8/medline SP - e897 EP - e897 JF - Translational psychiatry JO - Transl Psychiatry VL - 6 IS - 9 N2 - 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. SN - 2158-3188 UR - https://www.unboundmedicine.com/medline/citation/27648919/Prediction_of_transition_from_ultra_high_risk_to_first_episode_psychosis_using_a_probabilistic_model_combining_history_clinical_assessment_and_fatty_acid_biomarkers_ L2 - https://doi.org/10.1038/tp.2016.170 DB - PRIME DP - Unbound Medicine ER -