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Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): when things are not what they seem

Published online by Cambridge University Press:  17 November 2020

Andrea Raballo*
Affiliation:
Department of Medicine, Section of Psychiatry, Clinical Psychology and Rehabilitation, University of Perugia, Perugia, Italy Center for Translational, Phenomenological and Developmental Psychopathology (CTPDP), Perugia University Hospital, Perugia, Italy
Michele Poletti
Affiliation:
Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
Antonio Preti
Affiliation:
Centro Medico ‘Genneruxi’, Cagliari, Italy Center of Liaison Psychiatry and Psychosomatics, University Hospital, University of Cagliari, Cagliari, Italy
*
Author for correspondence: Andrea Raballo, E-mail: [email protected]

Abstract

Background

The clinical high-risk (CHR) for psychosis paradigm is changing psychiatric practice. However, a widespread confounder, i.e. baseline exposure to antipsychotics (AP) in CHR samples, is systematically overlooked. Such exposure might mitigate the initial clinical presentation, increase the heterogeneity within CHR populations, and confound the evaluation of transition to psychosis at follow-up. This is the first meta-analysis examining the prevalence and the prognostic impact on transition to psychosis of ongoing AP treatment at baseline in CHR cohorts.

Methods

Major databases were searched for articles published until 20 April 2020. The variance-stabilizing Freeman-Tukey double arcsine transformation was used to estimate prevalence. The binary outcome of transition to psychosis by group was estimated with risk ratio (RR) and the inverse variance method was used for pooling.

Results

Fourteen studies were eligible for qualitative synthesis, including 1588 CHR individuals. Out of the pooled CHR sample, 370 individuals (i.e. 23.3%) were already exposed to AP at the time of CHR status ascription. Transition toward full-blown psychosis at follow-up intervened in 112 (29%; 95% CI 24–34%) of the AP-exposed CHR as compared to 235 (16%; 14–19%) of the AP-naïve CHR participants. AP-exposed CHR had higher RR of transition to psychosis (RR = 1.47; 95% CI 1.18–1.83; z = 3.48; p = 0.0005), without influence by age, gender ratio, overall sample size, duration of the follow-up, or quality of the studies.

Conclusions

Baseline AP exposure in CHR samples is substantial and is associated with a higher imminent risk of transition to psychosis. Therefore, such exposure should be regarded as a non-negligible red flag for clinical risk management.

Type
Review Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Ajnakina, O., David, A. S., & Murray, R. M. (2018). ‘At risk mental state’ clinics for psychosis – an idea whose time has come - and gone!. Psychological Medicine, 26, 16.Google Scholar
Bang, M., Park, J. Y., Kim, K. R., Lee, S. Y., Song, Y. Y., Kang, J. I., … An, S. K. (2019). Psychotic conversion of individuals at ultra-high risk for psychosis: The potential roles of schizotypy and basic symptoms. Early Intervention in Psychiatry, 13, 546555.CrossRefGoogle ScholarPubMed
Barendregt, J. J., Doi, S. A., Lee, Y. Y., Norman, R. E., & Vos, T. (2013). Meta-analysis of prevalence. Journal of Epidemiology and Community Health, 67, 974978.CrossRefGoogle Scholar
Bedi, G., Carrillo, F., Cecchi, G., Slezak, D. F., Sigman, M., Mota, N. B., … Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia, 1, 15030.CrossRefGoogle ScholarPubMed
Borenstein, M. (2020). Research note: In a meta-analysis, the I2 index does not tell us how much the effect size varies across studies. Journal of Physiotherapy, 66, 135139.CrossRefGoogle Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1, 97111.CrossRefGoogle ScholarPubMed
Brucato, G., Masucci, M. D., Arndt, L. Y., Ben-David, S., Colibazzi, T., Corcoran, C. M., … Girgis, R. R. (2017). Baseline demographics, clinical features and predictors of conversion among 200 individuals in a longitudinal prospective psychosis-risk cohort. Psychological Medicine, 47, 19231935.CrossRefGoogle Scholar
Chouinard, G., Samaha, A. N., Chouinard, V. A., Peretti, C. S., Kanahara, N., Takase, M., & Iyo, M. (2017). Antipsychotic-induced supersensitivity psychosis: Pharmacological criteria and therapy. Psychotherapy and Psychosomatics, 86, 189219.CrossRefGoogle ScholarPubMed
Ciarleglio, A. J., Brucato, G., Masucci, M. D., Altschuler, R., Colibazzi, T., Corcoran, C. M., … Girgis, R. R. (2019). A predictive model for conversion to psychosis in clinical high-risk patients. Psychological Medicine, 49, 11281137.CrossRefGoogle ScholarPubMed
Collin, G., Seidman, L. J., Keshavan, M. S., Stone, W. S., Qi, Z., Zhang, T., … Whitfield-Gabrieli, S. (2020). Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program. Molecular Psychiatry, 25, 24312440.CrossRefGoogle ScholarPubMed
DeVylder, J. E., Muchomba, F. M., Gill, K. E., Ben-David, S., Walder, D. J., Malaspina, D., & Corcoran, C. M. (2014). Symptom trajectories and psychosis onset in a clinical high-risk cohort: The relevance of subthreshold thought disorder. Schizophrenia Research, 159, 278283.CrossRefGoogle Scholar
Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315, 629634.CrossRefGoogle ScholarPubMed
Fleiss, J. L. (1993). The statistical basis of meta-analysis. Statistical Methods in Medical Research, 2, 121145.CrossRefGoogle ScholarPubMed
Freeman, M. F., & Tukey, J. W. (1950). Transformations related to the angular and the square root. Annals of Mathematical Statistics, 4, 607611.CrossRefGoogle Scholar
Fusar-Poli, P., Rutigliano, G., Stahl, D., Schmidt, A., Ramella-Cravaro, V., Hitesh, S., & McGuire, P. (2016). Deconstructing pretest risk enrichment to optimize prediction of psychosis in individuals at clinical high risk. JAMA Psychiatry, 73, 12601267.CrossRefGoogle ScholarPubMed
Galbraith, R. (1994). Some applications of radial plots. Journal of the American Statistical Association, 89, 12321242.CrossRefGoogle Scholar
Galletly, C., Castle, D., Dark, F., Humberstone, V., Jablensky, A., Killackey, E., … Tran, N. (2016). Royal Australian and New Zealand college of psychiatrists clinical practice guidelines for the management of schizophrenia and related disorders. Australian and New Zealand Journal of Psychiatry, 50, 410472.CrossRefGoogle ScholarPubMed
Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3, 486504.CrossRefGoogle Scholar
Huang, Y., Tang, J., Tam, W. W., Mao, C., Yuan, J., Di, M., & Yang, Z. (2016). Comparing the overall result and interaction in aggregate data meta-analysis and individual patient data meta-analysis. Medicine (Baltimore, 95, e3312.CrossRefGoogle ScholarPubMed
Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11, 193206.CrossRefGoogle ScholarPubMed
Jackson, D., & Turner, R. (2017). Power analysis for random-effects meta-analysis. Research Synthesis Methods, 8, 290302.CrossRefGoogle ScholarPubMed
Katagiri, N., Pantelis, C., Nemoto, T., Zalesky, A., Hori, M., Shimoji, K., … Mizuno, M. (2015). A longitudinal study investigating sub-threshold symptoms and white matter changes in individuals with an ‘at risk mental state’ (ARMS). Schizophrenia Research, 162, 713.CrossRefGoogle Scholar
Katsura, M., Ohmuro, N., Obara, C., Kikuchi, T., Ito, F., Miyakoshi, T., … Matsumoto, K. (2014). A naturalistic longitudinal study of at-risk mental state with a 2.4 year follow-up at a specialized clinic setting in Japan. Schizophrenia Research, 158, 3238.CrossRefGoogle Scholar
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13, 261276.CrossRefGoogle Scholar
Knapp, G., & Hartung, J. (2003). Improved tests for a random effects meta-regression with a single-covariate. Statistics in Medicine, 22, 26932710.CrossRefGoogle ScholarPubMed
Labad, J., Stojanovic-Perez, A., Montalvo, I., Solé, M., Cabezoa, A., Ortega, L., … Gutierrez-Zotes, A. (2015). Stress biomarkers as predictors of transition to psychosis in at-risk mental states: Roles for cortisol, prolactin and albumin. Journal of Psychiatric Research, 60, 163169.CrossRefGoogle ScholarPubMed
Liu, C. C., Lai, M. C., Liu, C. M., Chiu, Y. N., Hsieh, M. H., Hwang, T. J., … Hwu, H. G. (2011). Follow-up of subjects with suspected prepsychotic state in Taiwan. Schizophrenia Research, 126, 6570.CrossRefGoogle Scholar
McGlashan, T. H. (2001). Structured Interview for Prodromal Symptoms (SIPS). Yale University.Google Scholar
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535.CrossRefGoogle ScholarPubMed
Moritz, S., Gawęda, Ł., Heinz, A., & Gallinat, J. (2019). Four reasons why early detection centers for psychosis should be renamed and their treatment targets reconsidered: We should not catastrophize a future we can neither reliably predict nor change. Psychological Medicine, 49, 21342140.CrossRefGoogle Scholar
National Institute for Health and Care Excellence. (2014). Psychosis and schizophrenia in adults: prevention and management. Clinical guideline. nice.org.uk/guidance/cg178/chapter/1-recommendations.Google Scholar
Olfson, M., King, M., & Schoenbaum, M. (2015). Treatment of young people with antipsychotic medications in the United States. JAMA Psychiatry, 72, 867874.CrossRefGoogle ScholarPubMed
Penttilä, M., Jääskeläinen, E., Hirvonen, N., Isohanni, M., & Miettunen, J. (2014). Duration of untreated psychosis as predictor of long-term outcome in schizophrenia: Systematic review and meta-analysis. British Journal of Psychiatry, 205, 8894.CrossRefGoogle ScholarPubMed
Perez, V. B., Woods, S. W., Roach, B. J., Ford, J. M., McGlashan, T. H., Shirari, V. H., & Mathalon, D. H. (2014). Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: Forecasting psychosis risk with mismatch negativity. Biological Psychiatry, 75, 459469.CrossRefGoogle ScholarPubMed
Raballo, A., & Poletti, M. (2019). Overlooking the transition elephant in the ultra-high-risk room: Are we missing functional equivalents of transition to psychosis?. Psychological Medicine, 29, 14. doi:10.1017/S0033291719003337.CrossRefGoogle Scholar
Raballo, A., Poletti, M., & Carpenter, W. (2019). Rethinking the psychosis threshold in clinical high risk. Schizophrenia Bulletin, 45, 12.CrossRefGoogle ScholarPubMed
Raballo, A., Poletti, M., & Preti, A. (2020). Attenuated psychosis syndrome of pharmacologically attenuated first episode psychosis? An undesirably widespread confounder. JAMA Psychiatry, Epub ahead of print 8 July, doi: 10.1001/jamapsychiatry.2020.1634.CrossRefGoogle ScholarPubMed
R Core Team, . (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.Google Scholar
Rüsch, N., Heekeren, K., Theodoridou, A., Müller, M., Corrigan, P. W., Mayer, B., … Rössler, W. (2015). Stigma as a stressor and transition to schizophrenia after one year among young people at risk of psychosis. Schizophrenia Research, 166, 4348.CrossRefGoogle ScholarPubMed
Salazar de Pablo, S., Catalan, A., & Fusar-Poli, P. (2020). Clinical validity of DSM-5 attenuated psychosis syndrome. Advances in diagnosis, prognosis and treatment. JAMA Psychiatry, 77, 311320.CrossRefGoogle Scholar
Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020). Individualized diagnostic and prognostic models for patients with psychosis risk syndromes: A meta-analytic view on the state of the art. Biological Psychiatry, 88(4), 349360. doi:10.1016/j.biopsych.2020.02.009.CrossRefGoogle ScholarPubMed
Schlosser, D. A., Jacobson, S., Chen, Q., Sugar, C. A., Niendam, T. A., Li, G., … Cannon, T. D. (2012). Recovery from an at-risk state: Clinical and functional outcomes of putatively prodromal youth who do not develop psychosis. Schizophrenia Bulletin, 38, 12251233.CrossRefGoogle Scholar
Schmidt, S. J., Schultze-Lutter, F., Schimmelman, B. G., Maric, N. P., Salokangas, R. K. R., Riecher-Rössler, A., … Ruhrmann, S. (2015). EPA guidance on the early intervention in clinical high risk states of psychoses. European Psychiatry, 30, 388404.CrossRefGoogle ScholarPubMed
Schultze-Lutter, F., Klösterkotter, J., & Ruhrmann, S. (2014). Improving the clinical prediction of psychosis by combining ultra-high risk criteria and cognitive basic symptoms. Schizophrenia Research, 154, 100106.CrossRefGoogle ScholarPubMed
Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). Meta-analysis with R. Cham, Switzerland: Springer.CrossRefGoogle Scholar
Van Os, J., & Guloksuz, S. A. (2017). Critique of the ‘ultra-high risk’ and ‘transition’ paradigm. World Psychiatry, 16, 200206.CrossRefGoogle Scholar
Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G., … Salanti, G. (2016). Methods to estimate the between-study variance and its uncertainty in meta-analysis. Research Synthesis Methods, 7, 5579.CrossRefGoogle ScholarPubMed
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metaphor package. Journal of Statistical Software, 36, 148.CrossRefGoogle Scholar
Viechtbauer, W., & Cheung, M. W. L. (2010). Outlier and influence diagnostics for meta-analyses. Research Synthesis Methods, 1, 112125.CrossRefGoogle Scholar
Yoviene Sykes, L. A., Ferrara, M., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., … Woods, S. W. (2020). Predictive validity of conversion from the clinical high risk syndrome to frank psychosis. Schizophrenia Research, 216, 184191.CrossRefGoogle ScholarPubMed
Yung, A. R., Phillips, L. J., Yuen, H. P., Francey, S. M., McFarlane, C. A., Hallgren, M., & McGorry, P. D. (2003). Psychosis prediction: 12-month follow up of a high-risk (‘prodromal’) group. Schizophrenia Research, 60, 2132.CrossRefGoogle Scholar
Yung, A. R., Yuen, H. P., McGorry, P. D., Phillips, L. J., Kelly, D., Dell'Olio, M., … Buckby, J. (2005). Mapping the onset of psychosis: The comprehensive assessment of at-risk mental states. Australian and New Zealand Journal of Psychiatry, 39, 964971.CrossRefGoogle ScholarPubMed
Zhang, T., Li, H., Tang, Y., Niznikiewic, M. A., Shenton, M. E., Keshavan, M. S., … Wang, J. (2018). Validating the predictive accuracy of the NAPLS-2 psychosis risk calculator in a clinical high-risk sample from the SHARP (ShangHai At Risk for Psychosis) program. American Journal of Psychiatry, 175, 906908.CrossRefGoogle Scholar
Ziermans, T. B., Schothorst, P. F., Sprong, M., & van Engeland, H. (2011). Transition and remission in adolescents at ultra-high risk for psychosis. Schizophrenia Research, 126, 5864.CrossRefGoogle ScholarPubMed
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