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

Adam J. Ciarleglio, Gary Brucato, Michael D. Masucci, Rebecca Altschuler, Tiziano Colibazzi, Cheryl M. Corcoran, Francesca M. Crump, Guillermo Horga, Eugénie Lehembre-Shiah, Wei Leong, Scott A. Schobel, Melanie M. Wall, Larry Yang, Jeffrey A. Lieberman, Ragy R. Girgis

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalPsychological Medicine
DOIs
StateAccepted/In press - Jun 28 2018

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Psychotic Disorders
Interviews
Aptitude
Mental Disorders
Psychiatry
Biomarkers
Communication
Demography

Keywords

  • Clinical high risk
  • prediction model
  • psychosis prediction
  • schizophrenia

ASJC Scopus subject areas

  • Applied Psychology
  • Psychiatry and Mental health

Cite this

Ciarleglio, A. J., Brucato, G., Masucci, M. D., Altschuler, R., Colibazzi, T., Corcoran, C. M., ... Girgis, R. R. (Accepted/In press). A predictive model for conversion to psychosis in clinical high-risk patients. Psychological Medicine, 1-10. https://doi.org/10.1017/S003329171800171X

A predictive model for conversion to psychosis in clinical high-risk patients. / Ciarleglio, Adam J.; Brucato, Gary; Masucci, Michael D.; Altschuler, Rebecca; Colibazzi, Tiziano; Corcoran, Cheryl M.; Crump, Francesca M.; Horga, Guillermo; Lehembre-Shiah, Eugénie; Leong, Wei; Schobel, Scott A.; Wall, Melanie M.; Yang, Larry; Lieberman, Jeffrey A.; Girgis, Ragy R.

In: Psychological Medicine, 28.06.2018, p. 1-10.

Research output: Contribution to journalArticle

Ciarleglio, AJ, Brucato, G, Masucci, MD, Altschuler, R, Colibazzi, T, Corcoran, CM, Crump, FM, Horga, G, Lehembre-Shiah, E, Leong, W, Schobel, SA, Wall, MM, Yang, L, Lieberman, JA & Girgis, RR 2018, 'A predictive model for conversion to psychosis in clinical high-risk patients', Psychological Medicine, pp. 1-10. https://doi.org/10.1017/S003329171800171X
Ciarleglio AJ, Brucato G, Masucci MD, Altschuler R, Colibazzi T, Corcoran CM et al. A predictive model for conversion to psychosis in clinical high-risk patients. Psychological Medicine. 2018 Jun 28;1-10. https://doi.org/10.1017/S003329171800171X
Ciarleglio, Adam J. ; Brucato, Gary ; Masucci, Michael D. ; Altschuler, Rebecca ; Colibazzi, Tiziano ; Corcoran, Cheryl M. ; Crump, Francesca M. ; Horga, Guillermo ; Lehembre-Shiah, Eugénie ; Leong, Wei ; Schobel, Scott A. ; Wall, Melanie M. ; Yang, Larry ; Lieberman, Jeffrey A. ; Girgis, Ragy R. / A predictive model for conversion to psychosis in clinical high-risk patients. In: Psychological Medicine. 2018 ; pp. 1-10.
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AU - Corcoran, Cheryl M.

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AU - Lehembre-Shiah, Eugénie

AU - Leong, Wei

AU - Schobel, Scott A.

AU - Wall, Melanie M.

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AU - Lieberman, Jeffrey A.

AU - Girgis, Ragy R.

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N2 - 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.

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