An imaging-based risk calculator for prediction of conversion to psychosis in clinical high-risk individuals using glutamate 1HMRS

SCHIZOPHRENIA RESEARCH

卷:226

Article

Kegeles, Lawrence S.¹  Ciarleglio, Adam²  Leon-Ortiz, Pablo³  Reyes-Madrigal, Francisco³  Lieberman, Jeffrey A.¹  Brucato, Gary¹  Girgis, Ragy R.¹  de la Fuente-Sandoval, Camilo³ 

[1] Columbia Univ, Dept Psychiat, New York State Psychiat Inst NYSPI, New York, NY USA

[2] George Washington Univ, Milken Inst, Sch Publ Hlth, Dept Epidemiol & Biostat, Washington, DC USA

[3] Inst Nacl Neurol & Neurocirug, Lab Expt Psychiat, Insurgentes Sur 3877, Mexico City 14269, DF, Mexico

关键词: Risk calculator;    Glutamate;    Clinical high-risk;    Conversion to psychosis;   

DOI  :  10.1016/j.schres.2019.09.004

来源: Elsevier

【 摘 要 】

Risk calculators for prediction of conversion of Clinical High-Risk (CHR) individuals to syndromal psychosis have recently been developed and have generated considerable clinical use and research interest. Predictor variables in these calculators have been clinical rather than biological, and our goal was to incorporate a neurochemical imaging measure into this framework and assess its impact on prediction. We combined striatal glutamate (HMRS)-H-1 data with the SIPS symptoms identified by the Columbia Risk Calculator as having the greatest predictive value in order to develop an imaging-based risk calculator for conversion to psychosis. We evaluated the calculator in 19 CHR individuals, 7 (36.84%) of whom converted to syndromal psychosis during the 2-year followup. The receiver operating characteristic (ROC) curve for the logistic model including only striatal glutamate and visual perceptual abnormalities showed an AUC = 0.869 (95% CI = [0.667, 1.000]) and AUC(oa) = 0.823, with sensitivity of 0.714, specificity of 0.917, positive predictive value of 0.833, and negative predictive value of 0.846. These results represent modest improvements over each of the individual ROC curves based on either striatal glutamate or visual perceptual abnormalities alone. The preliminary model building and evaluation presented here in a small CHR sample suggests that the approach of incorporating predictive imaging measures into risk classification is not only feasible but offers the potential of enhancing risk assessment. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

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