A predictive model for conversion to psychosis in clinical high-risk patients
| Autor: | Eugénie Lehembre-Shiah, Francesca Crump, Tiziano Colibazzi, Rebecca Altschuler, Melanie M. Wall, Jeffrey A. Lieberman, Cheryl Corcoran, Guillermo Horga, Gary Brucato, Ragy R. Girgis, Scott Schobel, Lawrence H. Yang, Michael D. Masucci, Adam Ciarleglio, Wei Leong |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Psychosis Adolescent New York Risk Assessment Article Young Adult 03 medical and health sciences 0302 clinical medicine Clinical Decision Rules Intervention (counseling) Interview Psychological Humans Medicine Dysphoric mood Applied Psychology High risk patients business.industry Baseline data Ideation medicine.disease 030227 psychiatry Psychiatry and Mental health Psychotic Disorders Schizophrenia Structured interview Female business 030217 neurology & neurosurgery Clinical psychology |
| Zdroj: | Psychol Med |
| ISSN: | 1469-8978 0033-2917 |
| DOI: | 10.1017/s003329171800171x |
| Popis: | BackgroundThe 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.MethodsBaseline 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.ResultsAt 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.ConclusionsThe 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: | OpenAIRE |
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