Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T20:42:44.492Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  28 June 2018

Adam J. Ciarleglio Open the ORCID record for Adam J. Ciarleglio [Opens in a new window] ,
Gary Brucato ,
Michael D. Masucci ,
Rebecca Altschuler ,
Tiziano Colibazzi ,
Cheryl M. Corcoran ,
Francesca M. Crump ,
Guillermo Horga ,
Eugénie Lehembre-Shiah  and
Wei Leong
...Show all authors
Adam J. Ciarleglio*
Affiliation:
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA
Gary Brucato
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Michael D. Masucci
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Rebecca Altschuler
Affiliation:
The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Tiziano Colibazzi
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Cheryl M. Corcoran
Affiliation:
Icahn School of Medicine at Mt. Sinai, New York, NY, USA
Francesca M. Crump
Affiliation:
The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Guillermo Horga
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Eugénie Lehembre-Shiah
Affiliation:
The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Wei Leong
Affiliation:
The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Scott A. Schobel
Affiliation:
F. Hoffman-LaRoche A.G., Basel, Switzerland
Melanie M. Wall
Affiliation:
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA
Lawrence H. Yang
Affiliation:
College of Global Public Health, New York University, New York, NY, USA
Jeffrey A. Lieberman
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
Ragy R. Girgis
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA New York State Psychiatric Institute, New York, NY, USA Research Foundation for Mental Hygiene, New York, NY, USA The Center of Prevention and Evaluation, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA
*
Author for correspondence: Adam J. Ciarleglio, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Clinical high riskprediction modelpsychosis predictionschizophrenia
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 7 , May 2019 , pp. 1128 - 1137
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Addington, J, Cadenhead, KS, Cannon, TD, Cornblatt, B, McGlashan, TH, Perkins, DO, Seidman, LJ, Tsuang, M, Walker, EF, Woods, SW and Heinssen, R (2007) North American Prodrome Longitudinal Study: a collaborative multisite approach to prodromal schizophrenia research. Schizophrenia Bulletin 33, 665–672.Google Scholar
Addington, J, Liu, L, Buchy, L, Cadenhead, KS, Cannon, TD, Cornblatt, BA, Perkins, DO, Seidman, LJ, Tsuang, MT, Walker, EF, Woods, SW, Bearden, CE, Mathalon, DH and McGlashan, TH (2015) North American Prodrome Longitudinal Study (NAPLS 2): the prodromal symptoms. Journal of Nervous and Mental Disease 203, 328–335.Google Scholar
American Psychiatric Association (2000) Diagnostic and Statistical Manual of Mental Disorders, 4th Edn. Text Revision. Washington DC: American Psychiatric Association.Google Scholar
Andreasen, NC, Endicott, J, Spitzer, RL and Winokur, G (1977) The family history method using diagnostic criteria: reliability and validity. Archives of General Psychiatry 34, 1229–1235.Google Scholar
Brucato, G, Appelbaum, PS, Lieberman, JA, Wall, MM, Feng, T, Masucci, MD, Altschuler, R and Girgis, RR (2018) A longitudinal study of violent behavior in a psychosis-risk cohort. Neuropsychopharmacology 43, 264–271.Google Scholar
Brucato, G, Masucci, MD, Arndt, LY, Ben-David, S, Colibazzi, T, Corcoran, CM, Crumbley, AH, Crump, FM, Gill, KE, Kimhy, D, Lister, A, Schobel, SA, Yang, LH, Lieberman, JA and Girgis, RR (2017) Baseline demographics, clinical features and predictors of conversion among 200 individuals in a longitudinal prospective psychosis-risk cohort. Psychological Medicine 47, 1923–1935.Google Scholar
Cannon, TD, Yu, CH, Addington, J, Bearden, CE, Cadenhead, KS, Cornblatt, BA, Heinssen, R, Jeffries, CD, Mathalon, DH, McGlashan, TH, Perkins, DO, Seidman, LJ, Tsuang, MT, Walker, EF, Woods, SW and Kattan, MW (2016) An individualized risk calculator for research in prodromal psychosis. American Journal of Psychiatry 173, 980–988.Google Scholar
Carrión, RE, Cornblatt, BA, Burton, CZ, Tso, IF, Auther, AM, Adelsheim, S, Calkins, R, Carter, CS, Niendam, T, Sale, TG, Taylor, SF and McFarlane, WR (2016) Personalized prediction of psychosis: external validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project. The American Journal of Psychiatry 173, 989–996.Google Scholar
Corcoran, CM, First, MB and Cornblatt, B (2010) The psychosis risk syndrome and its proposed inclusion in the DSM-V: a risk-benefit analysis. Schizophrenia Research 120, 16–22.Google Scholar
Cornblatt, BA, Auther, AM, Niendam, T, Smith, CW, Zinberg, J, Bearden, CE and Cannon, TD (2007) Preliminary findings for two new measures of social and role functioning in the prodromal phase of schizophrenia. Schizophrenia Bulletin 33, 688–702.Google Scholar
Correll, CU, Hauser, MH, Auther, AM and Cornblatt, BA (2010) Research in people with the psychosis risk syndrome: a review of the current evidence and future directions. Journal of Child Psychology and Psychiatry 51, 390–431.Google Scholar
Crump, FM, Arndt, L, Grivel, M, Horga, G, Corcoran, CM, Brucato, G and Girgis, RR (2017) Attenuated first-rank symptoms and conversion to psychosis in a clinical high-risk cohort. Early Intervention in Psychiatry (in press).Google Scholar
D'Agostino, RB, Vasan, RS, Pencina, MJ, Wolf, PA, Cobain, M, Massaro, JM and Kannel, WB (2008) General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117, 743–753.Google Scholar
First, MB, Spitzer, RL, Gibbon, M and Williams, JBW (2002) Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). Biometric Section, New York: New York State Psychiatric Institute.Google Scholar
Friedman, J, Hastie, T and Tibshirani, R (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22.Google Scholar
Fusar-Poli, P, Borgwardt, S, Bechdolf, A, Addington, J, Richer-Rossler, A, Schultze-Lutter, F, Keshavan, M, Wood, S, Ruhrmann, S, Seidman, LJ, Valmaggia, L, Cannon, T, Velthorst, E, De Haan, L, Cornblatt, B, Bonoldi, I, Birchwood, M, McGlashan, T, Carpenter, W, McGorry, P, Klosterkotter, J, McGuire, P and Yung, A (2013) The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70, 107–120.Google Scholar
Fusar-Poli, P, Cappucciati, M, Rutigliano, G, Schultze-Lutter, F, Bonoldi, I, Borgwardt, S, Riecher-Rössler, A, Addington, J, Perkins, D, Woods, SW, McGlashan, TH, Lee, J, Klosterkötter, J, Yung, AR and McGuire, P (2015) At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction. World Psychiatry 14, 322–332.Google Scholar
Fusar-Poli, P, Rutigliano, G, Stahl, D, Davies, C, Bonoldi, I, Reilly, T and McGuire, P (2017) Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry 74, 493–500.Google Scholar
Grivel, MM, Leong, W, Masucci, MD, Altschuler, RA, Arndt, LY, Redman, SL, Yang, LH, Brucato, G and Girgis, RR (2017) Impact of lifetime traumatic experiences on suicidality and likelihood of conversion in a cohort of individuals at clinical high-risk for psychosis. Schizophrenia Research 195, 549–553.Google Scholar
Hall, R (1995) Global assessment of functioning: a modified scale. Psychosomatics 36, 267–275.Google Scholar
Haroun, N, Dunn, L, Haroun, A and Cadenhead, KS (2006) Risk and protection in prodromal schizophrenia: ethical implications for clinical practice and future research. Schizophrenia Bulletin 32, 166–178.Google Scholar
Harrell, FE, Lee, KL and Mark, DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15, 361–387.Google Scholar
Hartmann, JA, Yuen, HP, McGorry, PD, Yung, AR, Lin, A, Wood, SJ, Lavoie, S and Nelson, B (2016) Declining transition rates to psychotic disorder in ‘ultra-high risk’ clients: investigation of a dilution effect. Schizophrenia Research 170, 130–136.Google Scholar
Kaufman, J, Birmaher, B, Brent, D, Rao, U and Ryan, N (1996) Kiddie-Sads-Present and Lifetime Version (K-SADS-PL). Pittsburgh: University of Pittsburgh Medical Center.Google Scholar
Kowarik, A and Templ, M (2016) Imputation with the R package VIM. Journal of Statistical Software 74, 1–16.Google Scholar
Lehembre-Shiah, E, Leong, W, Brucato, G, Abi-Dargham, A, Lieberman, JA, Horga, G and Girgis, RR (2017) Distinct relationships between visual and auditory perceptual abnormalities and conversion to psychosis in a clinical high-risk population. JAMA Psychiatry 74, 104–106.Google Scholar
Lu, F and Petkova, E (2014) A comparative study of variable selection methods in the context of developing psychiatric screening instruments. Statistics in Medicine 33, 401–421.Google Scholar
Marshall, C, Deighton, S, Cadenhead, KS, Cannon, TD, Cornblatt, BA, McGlashan, TH, Perkins, DO, Seidman, LJ, Tsuang, MT, Walker, EF, Woods, SW, Bearden, CE, Mathalon, D and Addington, J (2016) The violent content in attenuated psychotic symptoms. Psychiatry Research 242, 61–66.Google Scholar
Marshall, M, Lewis, S, Lockwood, A, Drake, R, Jones, P and Croudace, T (2005) Association between duration of untreated psychosis and outcome in cohorts of first-episode patients: a systematic review. Archives of General Psychiatry 62, 975–983.Google Scholar
McGlashan, TH, Miller, TJ, Woods, SW, Hoffman, RE and Davidson, LA (2001) Scale for the assessment of prodromal symptoms and states. In Miller, TJ (ed.), Early Intervention in Psychotic Disorders. Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 135–149.Google Scholar
McGorry, PD, Nelson, B, Markulev, C, Yuen, HP, Schäfer, MR, Mossaheb, N, Schlögelhofer, M, Smesny, S, Hickie, IB, Berger, GE, Chen, EYH, de Haan, L, Nieman, DH, Nordentoft, M, Riecher-Rössler, A, Verma, S, Thompson, A, Yung, AR and Amminger, GP (2017) Effect of ω-3 polyunsaturated fatty acids in young people at ultrahigh risk for psychotic disorders: the NEURAPRO randomized clinical trial. JAMA Psychiatry 74, 19–27.Google Scholar
Miller, TJ, McGlashan, TH, Rosen, JL, Somjee, L, Markovich, PJ, Stein, K and Woods, SW (2002) Prospective diagnosis of the prodrome for schizophrenia: Preliminary evidence of interrater reliability and predictive validity using operational criteria and a structured interview. American Journal of Psychiatry 159, 863–865.Google Scholar
Nam, RK, Kattan, MW, Chin, JL, Trachtenberg, J, Singal, R, Rendon, R, Klotz, LH, Sugar, L, Sherman, C, Izawa, J, Bell, D, Stanimirovic, A, Venkateswaran, V, Diamandis, EP, Yu, C, Loblaw, DA and Narod, SA (2011) Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators. Journal of Clinical Oncology 29, 2959–2964.Google Scholar
Nurnberger, JI, Blehar, MC, Kaufmann, CA, York-Cooler, C, Simpson, SG, Harkavy-Friedman, J, Severe, JB, Malaspina, D and Reich, T (1994) Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH genetics initiative. Archives of General Psychiatry 51, 849–859; discussion 863–864.Google Scholar
Perkins, DO, Gu, H, Boteva, K and Lieberman, JA (2005) Relationship between duration of untreated psychosis and outcome in first-episode schizophrenia: a critical review and meta-analysis. American Journal of Psychiatry 162, 1785–1804.Google Scholar
Perkins, DO, Jeffries, CD, Cornblatt, BA, Woods, SW, Addington, J, Bearden, CE, Cadenhead, KS, Cannon, TD, Heinssen, R, Mathalon, DH, Seidman, LJ, Tsuang, MT, Walker, EF and McGlashan, TH (2015) Severity of thought disorder predicts psychosis in persons at clinical high-risk. Schizophrenia Research 169, 169–177.Google Scholar
Rosen, JL, Woods, SW, Miller, TJ and McGlashan, TH (2002) Prospective observations of emerging psychosis. Journal of Nervous and Mental Disease 190, 133–141.Google Scholar
Simon, AE, Umbricht, D, Lang, UE and Borgwardt, S (2014) Declining transition rates to psychosis: the role of diagnostic spectra and symptom overlaps in individuals with attenuated psychosis syndrome. Schizophrenia Research 159, 292–298.Google Scholar
Tibshirani, R (1996) Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B-Methodological 58, 267–288.Google Scholar
Van der Gaag, M, Smit, F, Bechdolf, A, French, P, Linszen, DH, Yung, AR, McGorry, P and Cuijpers, P (2013) Preventing a first episode of psychosis: meta-analysis of randomized controlled prevention trials of 12 months and longer-term follow-ups. Schizophrenia Research 149, 56–62.Google Scholar
Woods, SW, Addington, J, Cadenhead, K, Cannon, TD, Cornblatt, BA, Heinssen, R, Perkins, DO, Seidman, LJ, Tsuang, MT, Walker, EF and McGlashan, TH (2009) Validity of the prodromal risk syndrome for first psychosis: findings from the North American Prodrome Longitudinal Study. Schizophrenia Bulletin 35, 894–908.Google Scholar
Yung, AR, Yuen, HP, Berger, G, Francey, S, Hung, TC, Nelson, B, Phillips, L and McGorry, P (2007) Declining transition rate in ultra high risk (prodromal) services: dilution or reduction of risk? Schizophrenia Bulletin 33, 673–681.Google Scholar