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Psychotic disorders as a framework for precision psychiatry

Nature Reviews Neurology volume 19, pages 221–234 (2023)Cite this article

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Abstract

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Key points

  • People with psychosis can have very different clinical outcomes but are offered a broadly similar type of care.

  • Precision psychiatry is an approach that has the potential to support the delivery of more personalized care.

  • Promising targets for this approach to psychosis include the prediction of illness onset, response to treatment and relapse.

  • Early studies have generated encouraging results but will require independent validation.

  • The implementation of precision psychiatry in a clinical setting will depend on its practicability as well as on the accuracy of predictive models.

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Fig. 1: Promising areas for the application of prediction models.
Fig. 2: Simultaneous and sequential machine learning.

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References

  1. Charlson, F. J. et al. Global epidemiology and burden of schizophrenia: findings from the global burden of disease study 2016. Schizophr. Bull. 44, 1195–1203 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  2. Walker, E. R., McGee, R. E. & Druss, B. G. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry 72, 334–341 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  3. Harvey, P. D. et al. Functional impairment in people with schizophrenia: focus on employability and eligibility for disability compensation. Schizophrenia Res. 140, 1–8 (2012).

    Article  Google Scholar 

  4. Jongsma, H. E., Turner, C., Kirkbride, J. B. & Jones, P. B. International incidence of psychotic disorders, 2002-17: a systematic review and meta-analysis. Lancet Public Health 4, e229–e244 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association, 2013).

  6. Carbon, M. & Correll, C. U. Thinking and acting beyond the positive: the role of the cognitive and negative symptoms in schizophrenia. CNS Spectr. 19, 35–53 (2014).

    Article  Google Scholar 

  7. Jääskeläinen, E. et al. A systematic review and meta-analysis of recovery in schizophrenia. Schizophrenia Bull. 39, 1296–1306 (2013).

    Article  Google Scholar 

  8. Haddad, P. M. & Correll, C. U. The acute efficacy of antipsychotics in schizophrenia: a review of recent meta-analyses. Ther. Adv. Psychopharmacol. 8, 303–318 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  9. Howes, O. D. et al. Adherence to treatment guidelines in clinical practice: study of antipsychotic treatment prior to clozapine initiation. Br. J. Psychiatry 201, 481–485 (2012).

    Article  PubMed  Google Scholar 

  10. Allsopp, K., Read, J., Corcoran, R. & Kinderman, P. Heterogeneity in psychiatric diagnostic classification. Psychiatry Res. 279, 15–22 (2019).

    Article  PubMed  Google Scholar 

  11. Strauss, G. P. et al. Deconstructing negative symptoms of schizophrenia: Avolition–apathy and diminished expression clusters predict clinical presentation and functional outcome. J. Psychiatr. Res. 47, 783–790 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  12. Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  Google Scholar 

  13. Zhang, T., Koutsouleris, N., Meisenzahl, E. & Davatzikos, C. Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis. Schizophr. Bull. 41, 74–84 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Bora, E. Differences in cognitive impairment between schizophrenia and bipolar disorder: considering the role of heterogeneity. Psychiatry Clin. Neurosci. 70, 424–433 (2016).

    Article  PubMed  Google Scholar 

  15. Egerton, A. et al. Response to initial antipsychotic treatment in first episode psychosis is related to anterior cingulate glutamate levels: a multicentre 1H-MRS study (OPTiMiSE). Mol. Psychiatry 23, 2145–2155 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Miller, B. J. & Goldsmith, D. R. Inflammatory biomarkers in schizophrenia: implications for heterogeneity and neurobiology. Biomark. Neuropsychiatry 1, 100006 (2019).

    Article  Google Scholar 

  17. van Os, J., Kenis, G. & Rutten, B. P. F. The environment and schizophrenia. Nature 468, 203 (2010).

    Article  PubMed  Google Scholar 

  18. Zwicker, A., Denovan-Wright, E. M. & Uher, R. Gene-environment interplay in the etiology of psychosis. Psychol. Med. 48, 1925–1936 (2018).

    Article  PubMed  Google Scholar 

  19. World Health Organization. International Statistical Classification of Diseases and Related Health Problems 11th Revision https://icd.who.int/ (WHO, 2019).

  20. Blennow, K. & Zetterberg, H. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J. Intern. Med. 284, 643–663 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Paul, A., Comabella, M. & Gandhi, R. Biomarkers in multiple sclerosis. Cold Spring Harb. Perspect. Med. 9, a029058 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Demjaha, A., Murray, R. M., McGuire, P. K., Kapur, S. & Howes, O. D. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am. J. Psychiatry 169, 1203–1210 (2012).

    Article  PubMed  Google Scholar 

  23. Richards, A. L. et al. The relationship between polygenic risk scores and cognition in schizophrenia. Schizophrenia Bull. 46, 336–344 (2020).

    Google Scholar 

  24. Egerton, A. et al. Dopamine and glutamate in antipsychotic-responsive compared with antipsychotic-nonresponsive psychosis: a multicenter positron emission tomography and magnetic resonance spectroscopy study (STRATA). Schizophr. Bull. 47, 505–516 (2021).

    Article  PubMed  Google Scholar 

  25. Baker, R. E., Peña, J.-M., Jayamohan, J. & Jérusalem, A. Mechanistic models versus machine learning, a fight worth fighting for the biological community? Biol. Lett. 14, 20170660 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  26. Siren, R., Eriksson, J. G. & Vanhanen, H. Waist circumference a good indicator of future risk for type 2 diabetes and cardiovascular disease. BMC Public Health 12, 631 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  27. Häfner, H., Löffler, W., Maurer, K., Hambrecht, M. & an der Heiden, W. Depression, negative symptoms, social stagnation and social decline in the early course of schizophrenia. Acta Psychiatr. Scand. 100, 105–118 (1999).

    Article  PubMed  Google Scholar 

  28. Addington, J. The prodromal stage of psychotic illness: observation, detection or intervention? J. Psychiatry Neurosci. 28, 93–97 (2003).

    PubMed Central  PubMed  Google Scholar 

  29. Rosengard, R. J. et al. Association of pre-onset subthreshold psychotic symptoms with longitudinal outcomes during treatment of a first episode of psychosis. JAMA Psychiatry 76, 61–70 (2019).

    Article  PubMed  Google Scholar 

  30. Yung, A. R. et al. Mapping the onset of psychosis: the comprehensive assessment of at-risk mental states. Aust. N. Z. J. Psychiatry 39, 964–971 (2005).

    Article  PubMed  Google Scholar 

  31. Fusar-Poli, P. et al. Prevention of psychosis: advances in detection, prognosis, and intervention. JAMA Psychiatry 77, 755–765 (2020).

    Article  PubMed  Google Scholar 

  32. Demjaha, A., Valmaggia, L., Stahl, D., Byrne, M. & McGuire, P. Disorganization/cognitive and negative symptom dimensions in the at-risk mental state predict subsequent transition to psychosis. Schizophr. Bull. 38, 351–359 (2012).

    Article  PubMed  Google Scholar 

  33. Alderman, T. et al. Negative symptoms and impaired social functioning predict later psychosis in Latino youth at clinical high risk in the North American prodromal longitudinal studies consortium. Early Interv. Psychiatry 9, 467–475 (2015).

    Article  PubMed  Google Scholar 

  34. Brucato, G. et al. Baseline demographics, clinical features and predictors of conversion among 200 individuals in a longitudinal prospective psychosis-risk cohort. Psychol. Med. 47, 1923–1935 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Ku, B. S. et al. Association between residential instability at individual and area levels and future psychosis in adolescents at clinical high risk from the North American Prodrome Longitudinal Study (NAPLS) consortium. Schizophr. Res. 238, 137–144 (2021).

    Article  PubMed  Google Scholar 

  36. Walder, D. J. et al. Sexual dimorphisms and prediction of conversion in the NAPLS psychosis prodrome. Schizophr. Res. 144, 43–50 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  37. Varese, F. et al. Childhood adversities increase the risk of psychosis: a meta-analysis of patient-control, prospective- and cross-sectional cohort studies. Schizophr. Bull. 38, 661–671 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  38. Oliver, D. et al. What causes the onset of psychosis in individuals at clinical high risk? A meta-analysis of risk and protective factors. Schizophr. Bull. 46, 110–120 (2020).

    Article  PubMed  Google Scholar 

  39. Fusar-Poli, P. et al. Cognitive functioning in prodromal psychosis: a meta-analysis. Arch. Gen. Psychiatry 69, 562–571 (2012).

    Article  PubMed  Google Scholar 

  40. Catalan, A. et al. Neurocognitive functioning in individuals at clinical high risk for psychosis: a systematic review and meta-analysis. JAMA Psychiatry 78, 859–867 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  41. Pantelis, C. et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 361, 281–288 (2003).

    Article  PubMed  Google Scholar 

  42. Cannon, T. D. et al. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol. Psychiatry 77, 147–157 (2015).

    Article  PubMed  Google Scholar 

  43. Fortea, A. et al. Cortical gray matter reduction precedes transition to psychosis in individuals at clinical high-risk for psychosis: a voxel-based meta-analysis. Schizophr. Res. 232, 98–106 (2021).

    Article  PubMed  Google Scholar 

  44. Carletti, F. et al. Alterations in white matter evident before the onset of psychosis. Schizophr. Bull. 38, 1170–1179 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  45. Schobel, S. A. et al. Differential targeting of the CA1 subfield of the hippocampal formation by schizophrenia and related psychotic disorders. Arch. Gen. Psychiatry 66, 938–946 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  46. Bossong, M. G. et al. Association of hippocampal glutamate levels with adverse outcomes in individuals at clinical high risk for psychosis. JAMA Psychiatry 76, 199–207 (2019).

    Article  PubMed  Google Scholar 

  47. Howes, O. D. et al. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch. Gen. Psychiatry 66, 13–20 (2009).

    Article  PubMed  Google Scholar 

  48. Howes, O. D. et al. Dopamine synthesis capacity before onset of psychosis: a prospective [18F]-DOPA PET imaging study. Am. J. Psychiatry 168, 1311–1317 (2011).

    Article  PubMed Central  PubMed  Google Scholar 

  49. Allen, P. et al. Transition to psychosis associated with prefrontal and subcortical dysfunction in ultra high-risk individuals. Schizophr. Bull. 38, 1268–1276 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  50. Cao, H. et al. Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization. Nat. Commun. 9, 3836 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  51. Modinos, G. et al. Neural circuitry of novelty salience processing in psychosis risk: association with clinical outcome. Schizophr. Bull. 46, 670–679 (2020).

    Article  PubMed  Google Scholar 

  52. Smigielski, L. et al. White matter microstructure and the clinical risk for psychosis: a diffusion tensor imaging study of individuals with basic symptoms and at ultra-high risk. Neuroimage Clin. 35, 103067 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  53. Khoury, R. & Nasrallah, H. A. Inflammatory biomarkers in individuals at clinical high risk for psychosis (CHR-P): state or trait? Schizophr. Res. 199, 31–38 (2018).

    Article  PubMed  Google Scholar 

  54. Kebir, O., Chaumette, B. & Krebs, M.-O. Epigenetic variability in conversion to psychosis: novel findings from an innovative longitudinal methylomic analysis. Transl. Psychiatry 8, 93 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  55. Cannon, T. D. et al. Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch. Gen. Psychiatry 65, 28–37 (2008).

    Article  PubMed Central  PubMed  Google Scholar 

  56. Thompson, A., Nelson, B. & Yung, A. Predictive validity of clinical variables in the “at risk” for psychosis population: international comparison with results from the North American Prodrome Longitudinal Study. Schizophr. Res. 126, 51–57 (2011).

    Article  PubMed  Google Scholar 

  57. Tarbox, S. I. et al. Premorbid functional development and conversion to psychosis in clinical high-risk youths. Dev. Psychopathol. 25, 1171–1186 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  58. Fusar-Poli, P. et al. Diagnostic and prognostic significance of brief limited intermittent psychotic symptoms (BLIPS) in individuals at ultra high risk. Schizophr. Bull. 43, 48–56 (2017).

    Article  PubMed  Google Scholar 

  59. Mechelli, A. et al. Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis. Schizophr. Res. 184, 32–38 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  60. Bourgin, J. et al. Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms. Eur. Child Adolesc. Psychiatry 29, 1525–1535 (2020).

    Article  PubMed  Google Scholar 

  61. Malda, A. et al. Individualized prediction of transition to psychosis in 1676 individuals at clinical high risk: development and validation of a multivariable prediction model based on individual patient data meta-analysis. Front. Psychiatry 10, 345 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  62. Fusar-Poli, P. et al. Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry 74, 493–500 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  63. Oliver, D. et al. Real-world implementation of precision psychiatry: transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis. Schizophr. Res. 227, 52–60 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  64. Pawełczyk, A., Łojek, E., Żurner, N., Kotlicka-Antczak, M. & Pawełczyk, T. Higher order language impairments can predict the transition of ultrahigh risk state to psychosis — an empirical study. Early Interv. Psychiatry 15, 314–327 (2021).

    Article  PubMed  Google Scholar 

  65. Rezaii, N., Walker, E. & Wolff, P. A machine learning approach to predicting psychosis using semantic density and latent content analysis. NPJ Schizophr. 5, 9 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  66. Zhang, D. et al. Eye movement indices as predictors of conversion to psychosis in individuals at clinical high risk. Eur. Arch. Psychiatry Clin. Neurosci. https://doi.org/10.1007/s00406-022-01463-z (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  67. Kristensen, T. D. et al. Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis. Acta Psychiatr. Scand. 144, 448–463 (2021).

    Article  PubMed  Google Scholar 

  68. Ramyead, A. et al. Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients. World J. Biol. Psychiatry 17, 285–295 (2016).

    Article  PubMed  Google Scholar 

  69. Das, T. et al. Disorganized gyrification network properties during the transition to psychosis. JAMA Psychiatry 75, 613–622 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  70. Perkins, D. O. et al. Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS project. Schizophr. Bull. 41, 419–428 (2015).

    Article  PubMed  Google Scholar 

  71. Dickens, A. M. et al. Dysregulated lipid metabolism precedes onset of psychosis. Biol. Psychiatry 89, 288–297 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. Mongan, D. et al. Development of proteomic prediction models for transition to psychotic disorder in the clinical high-risk state and psychotic experiences in adolescence. JAMA Psychiatry 78, 77–90 (2021).

    Article  PubMed  Google Scholar 

  73. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Hunter, S. A. & Lawrie, S. M. Imaging and genetic biomarkers predicting transition to psychosis. Curr. Top. Behav. Neurosci. 40, 353–388 (2018).

    Article  PubMed  Google Scholar 

  75. Dunleavy, C., Elsworthy, R. J., Upthegrove, R., Wood, S. J. & Aldred, S. Inflammation in first-episode psychosis: the contribution of inflammatory biomarkers to the emergence of negative symptoms, a systematic review and meta-analysis. Acta Psychiatr. Scand. 146, 6–20 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  76. Pettersson-Yeo, W. et al. Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychol. Med. 43, 2547–2562 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  77. Kegeles, L. S. et al. An imaging-based risk calculator for prediction of conversion to psychosis in clinical high-risk individuals using glutamate 1H MRS. Schizophr. Res. 226, 70–73 (2020).

    Article  PubMed  Google Scholar 

  78. Cannon, T. D. et al. An individualized risk calculator for research in prodromal psychosis. Am. J. Psychiatry 173, 980–988 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  79. Zhang, T. et al. 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. Am. J. Psychiatry 175, 906–908 (2018).

    Article  PubMed  Google Scholar 

  80. Osborne, K. J. & Mittal, V. A. External validation and extension of the NAPLS-2 and SIPS-RC personalized risk calculators in an independent clinical high-risk sample. Psychiatry Res. 279, 9–14 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  81. Hippisley-Cox, J., Coupland, C. & Brindle, P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357, j2099 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  82. Worthington, M. A. et al. Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis. Schizophr. Res. 227, 95–100 (2021).

    Article  CAS  PubMed  Google Scholar 

  83. Koutsouleris, N. et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry 78, 195–209 (2021).

    Article  PubMed  Google Scholar 

  84. Elkis, H. Treatment-resistant schizophrenia. Psychiatr. Clin. North Am. 30, 511–533 (2007).

    Article  PubMed  Google Scholar 

  85. Islam, F. et al. Pharmacogenomics of clozapine-induced agranulocytosis: a systematic review and meta-analysis. Pharmacogenomics J. 22, 230–240 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  86. García, S. et al. Adherence to antipsychotic medication in bipolar disorder and schizophrenic patients: a systematic review. J. Clin. Psychopharmacol. 36, 355–371 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  87. Howes, O. D. & Kapur, S. The dopamine hypothesis of schizophrenia: version III — the final common pathway. Schizophr. Bull. 35, 549–562 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  88. Egerton, A. et al. Anterior cingulate glutamate levels related to clinical status following treatment in first-episode schizophrenia. Neuropsychopharmacology 37, 2515–2521 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  89. Demjaha, A. et al. Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biol. Psychiatry 75, e11–e13 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Mouchlianitis, E. et al. Treatment-resistant schizophrenia patients show elevated anterior cingulate cortex glutamate compared to treatment-responsive. Schizophr. Bull. 42, 744–752 (2016).

    Article  PubMed  Google Scholar 

  91. Jauhar, S. et al. Determinants of treatment response in first-episode psychosis: an 18F-DOPA PET study. Mol. Psychiatry 24, 1502–1512 (2019).

    Article  PubMed  Google Scholar 

  92. Nakahara, T. et al. Glutamatergic and GABAergic metabolite levels in schizophrenia-spectrum disorders: a meta-analysis of 1H-magnetic resonance spectroscopy studies. Mol. Psychiatry 27, 744–757 (2022).

    Article  CAS  PubMed  Google Scholar 

  93. Sarpal, D. K. et al. Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am. J. Psychiatry 173, 69–77 (2016).

    Article  PubMed  Google Scholar 

  94. Sarpal, D. K. et al. Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia. JAMA Psychiatry 72, 5–13 (2016).

    Article  Google Scholar 

  95. Dazzan, P. et al. Symptom remission and brain cortical networks at first clinical presentation of psychosis: the OPTiMiSE study. Schizophr. Bull. 47, 444–455 (2021).

    Article  PubMed  Google Scholar 

  96. Lally, J. et al. Two distinct patterns of treatment resistance: clinical predictors of treatment resistance in first-episode schizophrenia spectrum psychoses. Psychol. Med. 46, 3231–3240 (2016).

    Article  CAS  PubMed  Google Scholar 

  97. Legge, S. E. et al. Clinical indicators of treatment-resistant psychosis. Br. J. Psychiatry 216, 259–266 (2020).

    Article  PubMed  Google Scholar 

  98. Horsdal, H. T., Wimberley, T., Köhler-Forsberg, O., Baandrup, L. & Gasse, C. Association between global functioning at first schizophrenia diagnosis and treatment resistance. Early Interv. Psychiatry 12, 1198–1202 (2018).

    Article  PubMed  Google Scholar 

  99. Mørkved, N. et al. Impact of childhood trauma on antipsychotic effectiveness in schizophrenia spectrum disorders: a prospective, pragmatic, semi-randomized trial. Schizophr. Res. 246, 49–59 (2022).

    Article  PubMed  Google Scholar 

  100. Arsalan, A. et al. Association of smoked cannabis with treatment resistance in schizophrenia. Psychiatry Res. 278, 242–247 (2019).

    Article  CAS  PubMed  Google Scholar 

  101. Martinez-Cengotitabengoa, M. et al. BDNF and NGF signalling in early phases of psychosis: relationship with inflammation and response to antipsychotics after 1 year. Schizophr. Bull. 42, 142–151 (2016).

    CAS  PubMed  Google Scholar 

  102. Lin, Y. et al. Pretreatment serum MCP-1 level predicts response to risperidone in schizophrenia. Shanghai Arch. Psychiatry 29, 287–294 (2017).

    CAS  PubMed  Google Scholar 

  103. Nettis, M. A. et al. Metabolic-inflammatory status as predictor of clinical outcome at 1-year follow-up in patients with first episode psychosis. Psychoneuroendocrinology 99, 145–153 (2019).

    Article  CAS  PubMed  Google Scholar 

  104. Enache, D. et al. Peripheral immune markers and antipsychotic non-response in psychosis. Schizophr. Res. 230, 1–8 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  105. Wimberley, T. et al. Polygenic risk score for schizophrenia and treatment-resistant schizophrenia. Schizophr. Bull. 43, 1064–1069 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  106. Santoro, M. L. et al. Polygenic risk score analyses of symptoms and treatment response in an antipsychotic-naive first episode of psychosis cohort. Transl. Psychiatry 8, 174 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  107. Zhang, J. P. et al. Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis. Am. J. Psychiatry 176, 21–28 (2019).

    Article  PubMed  Google Scholar 

  108. Hannon, E. et al. DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia. eLife 10, e58430 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  109. Ortiz, B. B. et al. A symptom combination predicting treatment-resistant schizophrenia — a strategy for real-world clinical practice. Schizophr. Res. 218, 195–200 (2020).

    Article  PubMed  Google Scholar 

  110. Rasmussen, S. A., Rosebush, P. I. & Mazurek, M. F. Does early antipsychotic response predict long-term treatment outcome? Hum. Psychopharmacol. 32, e2633 (2017).

    Article  Google Scholar 

  111. Subeesh, V., Maheswari, E., Singh, H., Neha, R. & Mazhar, F. Finding early improvement threshold to predict response after 8 weeks of treatment using risperidone in first-episode psychosis. J. Clin. Psychopharmacol. 41, 58–61 (2021).

    Article  CAS  PubMed  Google Scholar 

  112. Pagsberg, A. K. et al. Early antipsychotic nonresponse as a predictor of nonresponse and nonremission in adolescents with psychosis treated with aripiprazole or quetiapine: results from the TEA trial. J. Am. Acad. Child Adolesc. Psychiatry 61, 997–1009 (2022).

    Article  PubMed  Google Scholar 

  113. Mehta, U. M. et al. Resting-state functional connectivity predictors of treatment response in schizophrenia — a systematic review and meta-analysis. Schizophr. Res. 237, 153–165 (2021).

    Article  PubMed  Google Scholar 

  114. Troudet, R. et al. Gene expression and response prediction to amisulpride in the OPTiMiSE first episode psychoses. Neuropsychopharmacology 45, 1637–1644 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  115. Martinuzzi, E. et al. Stratification and prediction of remission in first-episode psychosis patients: the OPTiMiSE cohort study. Transl. Psychiatry 9, 20 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  116. Wang, M. et al. Predicting treatment response in schizophrenia with magnetic resonance imaging and polygenic risk score. Front. Genet. 13, 848205 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  117. Ambrosen, K. S. et al. A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data. Transl. Psychiatry 10, 276 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  118. Bergé, D. et al. Predictors of relapse and functioning in first-episode psychosis: a two-year follow-up study. Psychiatr. Serv. 67, 227–233 (2016).

    Article  PubMed  Google Scholar 

  119. Chi, M. H. et al. The readmission rate and medical cost of patients with schizophrenia after first hospitalization — a 10-year follow-up population-based study. Schizophr. Res. 170, 184–190 (2016).

    Article  PubMed  Google Scholar 

  120. Bighelli, I. et al. Psychosocial and psychological interventions for relapse prevention in schizophrenia: a systematic review and network meta-analysis. Lancet Psychiatry 8, 969–980 (2021).

    Article  PubMed  Google Scholar 

  121. Chen, E. Y. H. et al. Maintenance treatment with quetiapine versus discontinuation after one year of treatment in patients with remitted first episode psychosis: randomised controlled trial. BMJ 341, c4024 (2010).

    Article  PubMed Central  PubMed  Google Scholar 

  122. Patel, V. et al. In Mental, Neurological, and Substance Use Disorders: Disease Control Priorities 3rd edn (eds Patel, V. et al.) vol. 4 (The International Bank for Reconstruction and Development/The World Bank, 2016).

  123. Alvarez-Jimenez, M. et al. Risk factors for relapse following treatment for first episode psychosis: a systematic review and meta-analysis of longitudinal studies. Schizophr. Res. 139, 116–128 (2012).

    Article  CAS  PubMed  Google Scholar 

  124. Pourmand, D., Kavanagh, D. J. & Vaughan, K. Expressed emotion as predictor of relapse in patients with comorbid psychoses and substance use disorder. Aust. N. Z. J. Psychiatry 39, 473–478 (2005).

    Article  PubMed  Google Scholar 

  125. Rubio, J. M. et al. Striatal functional connectivity in psychosis relapse: a hypothesis generating study. Schizophr. Res. 243, 342–348 (2022).

    Article  PubMed  Google Scholar 

  126. Sasabayashi, D. et al. Increased brain gyrification and subsequent relapse in patients with first-episode schizophrenia. Front. Psychiatry 13, 937605 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  127. Gassó, P. et al. A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood. Transl. Psychiatry 11, 539 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  128. Adler, D. A. et al. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth uHealth 8, e19962 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  129. Hui, C. L.-M. et al. ReMind, a smartphone application for psychotic relapse prediction: a longitudinal study protocol. Early Inter. Psychiatry 15, 1659–1666 (2021).

    Article  Google Scholar 

  130. Henson, P., D’Mello, R., Vaidyam, A., Keshavan, M. & Torous, J. Anomaly detection to predict relapse risk in schizophrenia. Transl. Psychiatry 11, 28 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  131. Lee, D. Y. et al. Psychosis relapse prediction leveraging electronic health records data and natural language processing enrichment methods. Front. Psychiatry 13, 844442 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  132. Bhattacharyya, S. et al. Individualized prediction of 2-year risk of relapse as indexed by psychiatric hospitalization following psychosis onset: model development in two first episode samples. Schizophr. Res. 228, 483–492 (2021).

    Article  PubMed  Google Scholar 

  133. Miller, T. J. et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr. Bull. 29, 703–715 (2003).

    Article  PubMed  Google Scholar 

  134. Leucht, S., Davis, J. M., Engel, R. R., Kane, J. M. & Wagenpfeil, S. Defining ‘response’ in antipsychotic drug trials: recommendations for the use of scale-derived cutoffs. Neuropsychopharmacology 32, 1903–1910 (2007).

    Article  CAS  PubMed  Google Scholar 

  135. Suzuki, T. et al. Defining treatment-resistant schizophrenia and response to antipsychotics: a review and recommendation. Psychiatry Res. 197, 1–6 (2012).

    Article  PubMed  Google Scholar 

  136. Kahn, R. S. et al. Amisulpride and olanzapine followed by open-label treatment with clozapine in first-episode schizophrenia and schizophreniform disorder (OPTiMiSE): a three-phase switching study. Lancet Psychiatry 5, 797–807 (2018).

    Article  PubMed  Google Scholar 

  137. Basaraba, C. N. et al. Prediction tool for individual outcome trajectories across the next year in first-episode psychosis in coordinated specialty care. JAMA Psychiatry 80, 49–56 (2022).

    Article  Google Scholar 

  138. Petros, N., Cullen, A. E., Fusar-Poli, P., Mechelli, A. & McGuire, P. Towards standardising the assessment of good outcome in people at clinical high risk for psychosis: a collaborative approach. Schizophr. Res. 223, 361–362 (2020).

    Article  PubMed  Google Scholar 

  139. Chue, P. The relationship between patient satisfaction and treatment outcomes in schizophrenia. J. Psychopharmacol. 20, 38–56 (2006).

    Article  PubMed  Google Scholar 

  140. Fusar-Poli, P. et al. The lived experience of psychosis: a bottom-up review co-written by experts by experience and academics. World Psychiatry 21, 168–188 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  141. Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 77, 534–540 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  142. Lee, E. E. et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 6, 856–864 (2021).

    PubMed Central  PubMed  Google Scholar 

  143. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  144. Binder, E. B. Polygenic risk scores in schizophrenia: ready for the real world? Am. J. Psychiatry 176, 783–784 (2019).

    Article  PubMed  Google Scholar 

  145. Yuen, H. P. et al. Dynamic prediction of transition to psychosis using joint modelling. Schizophr. Res. 202, 333–340 (2018).

    Article  CAS  PubMed  Google Scholar 

  146. Egerton, A. et al. Effects of antipsychotic administration on brain glutamate in schizophrenia: a systematic review of longitudinal 1H-MRS studies. Front. Psychiatry 8, 66 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  147. Park, J. I. et al. The advantage of using 3-week data to predict response to aripiprazole at week 6 in first-episode psychosis. Int. Clin. Psychopharmacol. 29, 77–85 (2014).

    Article  PubMed  Google Scholar 

  148. Davies, C. & Bhattacharyya, S. Cannabidiol as a potential treatment for psychosis. Ther. Adv. Psychopharmacol. 9, 2045125319881916 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  149. Dell’Osso, B., Glick, I. D., Baldwin, D. S. & Altamura, A. C. Can long-term outcomes be improved by shortening the duration of untreated illness in psychiatric disorders? A conceptual framework. Psychopathology 46, 14–21 (2013).

    Article  PubMed  Google Scholar 

  150. Cougnard, A., Rachid Salmi, L., Salamon, R. & Verdoux, H. A decision analysis model to assess the feasibility of the early detection of psychosis in the general population. Schizophr. Res. 74, 27–36 (2005).

    Article  PubMed  Google Scholar 

  151. Vickers, A. J. & Holland, F. Decision curve analysis to evaluate the clinical benefit of prediction models. Spine J. 21, 1643–1648 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  152. Allyn, J. et al. A comparison of a machine learning model with Euroscore II in predicting mortality after elective cardiac surgery: a decision curve analysis. PLoS One 12, e0169772 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  153. Deniffel, D. et al. Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment. Eur. Radiol. 30, 6867–6876 (2020).

    Article  PubMed  Google Scholar 

  154. Jin, H., McCrone, P. & MacCabe, J. H. Stratified medicine in schizophrenia: how accurate would a test of drug response need to be to achieve cost-effective improvements in quality of life? Eur. J. Health Econ. 20, 1425–1435 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  155. Aref-Adib, G. et al. Factors affecting implementation of digital health interventions for people with psychosis or bipolar disorder, and their family and friends: a systematic review. Lancet Psychiatry 6, 257–266 (2019).

    Article  PubMed  Google Scholar 

  156. Lakhan, S. E., Vieira, K. & Hamlat, E. Biomarkers in psychiatry: drawbacks and potential for misuse. Int. Arch. Med. 3, 1 (2010).

    Article  PubMed Central  PubMed  Google Scholar 

  157. Hamby, T. & Taylor, W. Survey satisficing inflates reliability and validity measures: an experimental comparison of college and Amazon Mechanical Turk samples. Educ. Psychol. Meas. 76, 912–932 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  158. Radanovic, M., Facco, G. & Forlenza, O. V. Sensitivity and specificity of a briefer version of the Cambridge Cognitive Examination (CAMCog-Short) in the detection of cognitive decline in the elderly: an exploratory study. Int. J. Geriatr. Psychiatry 33, 769–778 (2018).

    Article  PubMed  Google Scholar 

  159. Bakolis, I. et al. Urban mind: using smartphone technologies to investigate the impact of nature on mental well-being in real time. Bioscience 68, 134–145 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  160. Vesel, C. et al. Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: a BiAffect iOS study. J. Am. Med. Inf. Assoc. 27, 1007–1018 (2020).

    Article  Google Scholar 

  161. Atkins, M., McGuire, P., Balgobin, B., Patel, P. & Taylor, D. Using a fingerstick test for haematological monitoring in patients treated with clozapine. Ther. Adv. Psychopharmacol. 11, 20451253211000865 (2021).

    Article  PubMed Central  PubMed  Google Scholar 

  162. Leaverton, P. E. et al. Representativeness of the Framingham Risk Model for coronary heart disease mortality: a comparison with a national cohort study. J. Chronic Dis. 40, 775–784 (1987).

    Article  CAS  PubMed  Google Scholar 

  163. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  PubMed  Google Scholar 

  164. Petrucelli N. et al. GeneReviews (University of Washington, 1993–2022).

  165. Eroles, P., Bosch, A., Pérez-Fidalgo, J. A. & Lluch, A. Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Treat. Rev. 38, 698–707 (2012).

    Article  CAS  PubMed  Google Scholar 

  166. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).

    Article  CAS  PubMed  Google Scholar 

  167. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Article  PubMed Central  Google Scholar 

  168. Lindström, J. & Tuomilehto, J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26, 725–731 (2003).

    Article  PubMed  Google Scholar 

  169. Flueckiger, P., Longstreth, W., Herrington, D. & Yeboah, J. Revised Framingham Stroke Risk Score, nontraditional risk markers, and incident stroke in a multiethnic cohort. Stroke 49, 363–369 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  170. Garcia-Closas, M. & Chatterjee, N. Assessment of breast cancer risk: which tools to use? Lancet Oncol. 20, 463–464 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  171. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 12, e0174944 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  172. Scirica, B. M. Use of biomarkers in predicting the onset, monitoring the progression, and risk stratification for patients with type 2 diabetes mellitus. Clin. Chem. 63, 186–195 (2017).

    Article  CAS  PubMed  Google Scholar 

  173. Subeh, G. K., Lajber, M., Patel, T. & Mostafa, J. A. Anti-N-methyl-D-aspartate receptor encephalitis: a detailed review of the different psychiatric presentations and red flags to look for in suspected cases. Cureus 13, e15188 (2021).

    PubMed Central  PubMed  Google Scholar 

  174. Coyle, J. T. NMDA receptor and schizophrenia: a brief history. Schizophr. Bull. 38, 920–926 (2012).

    Article  PubMed Central  PubMed  Google Scholar 

  175. Pollak, T. A. et al. Autoimmune psychosis: an international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin. Lancet Psychiatry 7, 93–108 (2020).

    Article  PubMed  Google Scholar 

  176. Zandi, M. S. et al. Immunotherapy for patients with acute psychosis and serum N-Methyl D-Aspartate receptor (NMDAR) antibodies: a description of a treated case series. Schizophr. Res. 160, 193–195 (2014).

    Article  PubMed  Google Scholar 

  177. Lennox, B. et al. Intravenous immunoglobulin and rituximab versus placebo treatment of antibody-associated psychosis: study protocol of a randomised phase IIa double-blinded placebo-controlled trial (SINAPPS2). Trials 20, 331 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  178. Kelleher, E. et al. Prevalence of N-methyl-D-aspartate receptor antibody (NMDAR-Ab) encephalitis in patients with first episode psychosis and treatment resistant schizophrenia on clozapine, a population based study. Schizophr. Res. 222, 455–461 (2020).

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Fiona Coutts & Nikolaos Koutsouleris

  2. Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany

    Nikolaos Koutsouleris

  3. Max Planck Institute of Psychiatry, Munich, Germany

    Nikolaos Koutsouleris

  4. Department of Psychiatry, University of Oxford, Oxford, UK

    Philip McGuire

  5. NIHR Oxford Health Biomedical Research Centre, Oxford, UK

    Philip McGuire

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All authors researched data for the article. All authors contributed substantially to the discussion of content. P.M. and F.C. wrote the article. P.M. and N.K. reviewed and/or edited the manuscript before submission.

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Glossary

Antipsychotic treatment resistance

Non-response to at least two different antipsychotic medications, most often treated with clozapine.

Area under the curve

(AUC). Measure of the ability of a model to distinguish between classes, for example, individuals who respond to medication and those who do not, where AUC 0.5 is no better than chance, and AUC 1 is a perfect prediction. Generally, AUC >0.9 is considered excellent, AUC >0.8 is good, AUC >0.7 is fair, AUC >0.6 is poor, and AUC <0.6 is a fail.

Balanced accuracies

Averages of sensitivity and specificity where 50% is no better than chance for a binary outcome and 100% is perfect prediction; over 90% is considered very good, between 70% and 89% is considered good, between 60% and 69% is fair, and below 60% is poor.

Harrel C index (concordance index)

Measure of goodness-of-fit of binary logistic regression models, equivalent to area under the curve.

Sensitivity

Percentage of the sample correctly identified as having the outcome of interest.

Specificity

Percentage of the sample correctly identified as not having the outcome of interest.

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Coutts, F., Koutsouleris, N. & McGuire, P. Psychotic disorders as a framework for precision psychiatry. Nat Rev Neurol 19, 221–234 (2023). https://doi.org/10.1038/s41582-023-00779-1

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