A predictive model for conversion to psychosis in clinical high-risk patients.

Autor:	Ciarleglio AJ; Department of Biostatistics,Mailman School of Public Health,Columbia University,New York,NY,USA., Brucato G; Department of Psychiatry,Columbia University,New York,NY,USA., Masucci MD; Department of Psychiatry,Columbia University,New York,NY,USA., Altschuler R; The Center of Prevention and Evaluation,New York State Psychiatric Institute,Columbia University Medical Center,New York,NY,USA., Colibazzi T; Department of Psychiatry,Columbia University,New York,NY,USA., Corcoran CM; Icahn School of Medicine at Mt. Sinai,New York,NY,USA., Crump FM; The Center of Prevention and Evaluation,New York State Psychiatric Institute,Columbia University Medical Center,New York,NY,USA., Horga G; Department of Psychiatry,Columbia University,New York,NY,USA., Lehembre-Shiah E; The Center of Prevention and Evaluation,New York State Psychiatric Institute,Columbia University Medical Center,New York,NY,USA., Leong W; The Center of Prevention and Evaluation,New York State Psychiatric Institute,Columbia University Medical Center,New York,NY,USA., Schobel SA; F. Hoffman-LaRoche A.G.,Basel,Switzerland., Wall MM; Department of Biostatistics,Mailman School of Public Health,Columbia University,New York,NY,USA., Yang LH; College of Global Public Health,New York University,New York,NY,USA., Lieberman JA; Department of Psychiatry,Columbia University,New York,NY,USA., Girgis RR; Department of Psychiatry,Columbia University,New York,NY,USA.
Jazyk:	angličtina
Zdroj:	Psychological medicine [Psychol Med] 2019 May; Vol. 49 (7), pp. 1128-1137. Date of Electronic Publication: 2018 Jun 28.
DOI:	10.1017/S003329171800171X
Abstrakt:	Background: The authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated to be strong predictors of conversion to psychosis in clinical high-risk (CHR) individuals. The model is based upon the Structured Interview for Psychosis Risk Syndromes (SIPS) and accompanying clinical interview, and yields scores indicating one's risk of conversion. Methods: Baseline data, including demographic and clinical characteristics measured by the SIPS, were obtained on 199 CHR individuals seeking evaluation in the early detection and intervention for mental disorders program at the New York State Psychiatric Institute at Columbia University Medical Center. Each patient was followed for up to 2 years or until they developed a syndromal DSM-4 disorder. A LASSO logistic fitting procedure was used to construct a model for conversion specifically to a psychotic disorder. Results: At 2 years, 64 patients (32.2%) converted to a psychotic disorder. The top five variables with relatively large standardized effect sizes included SIPS subscales of visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, and violent ideation. The concordance index (c-index) was 0.73, indicating a moderately strong ability to discriminate between converters and non-converters. Conclusions: The prediction model performed well in classifying converters and non-converters and revealed SIPS measures that are relatively strong predictors of conversion, comparable with the risk calculator published by NAPLS (c-index = 0.71), but requiring only a structured clinical interview. Future work will seek to externally validate the model and enhance its performance with the incorporation of relevant biomarkers.
Databáze:	MEDLINE
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