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

• DOI: 10.1017/s003329171800171x
• Publication Date: 2018-06-28T07:10:24Z
• Authors: Adam J. Ciarleglio; Gary Brucato; Michael D. Masucci; Rebecca Altschuler; Tiziano Colibazzi; Cheryl M. Corcoran; Francesca M. Crump; Guillermo Horga; Eugénie Lehembre-...; Wei Leong; Scott A. Schobel; Melanie M. Wall; Lawrence H. Yang; Jeffrey A. Lieberman; Ragy R. Girgis
• Keywords: Concordance

Abstract Background The authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated be strong predictors conversion in high-risk (CHR) individuals. is based upon Structured Interview for Psychosis Risk Syndromes (SIPS) accompanying interview, yields scores indicating one's conversion. Methods Baseline data, including demographic measured by SIPS, were obtained on 199 CHR individuals seeking evaluation early detection intervention mental disorders program at New York State Psychiatric Institute Columbia University Medical Center. Each patient was followed up 2 years or until they syndromal DSM-4 disorder. A LASSO logistic fitting procedure used construct specifically psychotic Results At years, 64 patients (32.2%) converted top five variables with relatively large standardized effect sizes included SIPS subscales visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, violent ideation. concordance index (c-index) 0.73, moderately ability discriminate between converters non-converters. Conclusions prediction performed well classifying non-converters revealed measures are conversion, comparable calculator published NAPLS (c-index = 0.71), but requiring only structured interview. Future work will seek externally validate enhance its performance incorporation relevant biomarkers.