Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information

Nature Mental Health volume 1, pages 25–35 (2023)Cite this article

Subjects

Abstract

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia, but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for treatment-resistant schizophrenia of routinely collected, objective biomedical predictors at FEP onset, and to validate the model externally in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction model for clozapine treatment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index and random glucose levels. The models were developed using data from two United Kingdom (UK) psychosis early intervention services and externally validated in another UK early intervention service. Model performance was assessed by discrimination and calibration. We developed the models in 785 patients and validated them externally in 1,110 patients. Both models predicted clozapine use well during internal validation (MOZART: C statistic, 0.70 (95% confidence interval, 0.63–0.76); LASSO: 0.69 (0.63–0.77)). At external validation, discrimination performance reduced (MOZART: 0.63 (0.58–0.69); LASSO: 0.64 (0.58–0.69)) but recovered after re-estimation of the lymphocyte predictor (0.67 (0.62–0.73)). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualization tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Patient selection flow charts, by cohort.
Fig. 2: Calibration plots for the main models based on the external validation sample.
Fig. 3: Decision curve analysis plot for forced-entry original and recalibrated models.

Similar content being viewed by others

Data availability

The source data for this work is anonymized patient records from three UK NHS Trusts: the CPFT Research Database, SLaM CRIS and the Birmingham EIP. Data from these Trusts are only available to clinicians and clinical researchers with clinical contracts with the Trusts. The data are securely held on clinical systems and available following ethical approval to preserve patient confidentiality. Therefore, the raw data cannot be shared. However, we developed an online data visualization tool (https://eosimo.shinyapps.io/trs_app/) for both the original and recalibrated MOZART models, which allows interactive exploration of the effect of each predictor and their combinations on the risk of clozapine use based on the predictors included in this study.

Code availability

R code for data extraction and analysis is available upon request to the corresponding author.

References

  1. Menezes, N., Arenovich, T. & Zipursky, R. A systematic review of longitudinal outcome studies of first-episode psychosis. Psychol. Med. 36, 1349–1362 (2006).

    Article  Google Scholar 

  2. Osimo, E. F. et al. Inflammatory and cardiometabolic markers at presentation with first episode psychosis and long-term clinical outcomes: A longitudinal study using electronic health records. Brain Behav. Immun. 91, 117–127 (2021).

    Article  Google Scholar 

  3. Siskind, D. et al. Rates of treatment-resistant schizophrenia from first-episode cohorts: systematic review and meta-analysis. Br. J. Psychiatry 220, 115–120 (2022).

  4. Howes, O. D., Thase, M. E., & Pillinger, T. Treatment resistance in psychiatry: state of the art and new directions. Mol. Psychiatry 27, 58–72 (2021).

  5. Kennedy, J. L., Altar, C. A., Taylor, D. L., Degtiar, I. & Hornberger, J. C. The social and economic burden of treatment-resistant schizophrenia: a systematic literature review. Int. Clin. Psychopharmacol. 29, 63–76 (2014).

    Article  Google Scholar 

  6. Mizuno, Y., McCutcheon, R. A., Brugger, S. P. & Howes, O. D. Heterogeneity and efficacy of antipsychotic treatment for schizophrenia with or without treatment resistance: a meta-analysis. Neuropsychopharmacology 45, 622–631 (2020).

    Article  Google Scholar 

  7. 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  Google Scholar 

  8. Barnes, T. R. et al. Evidence-based guidelines for the pharmacological treatment of schizophrenia: updated recommendations from the British Association for Psychopharmacology. J. Psychopharmacol. 34, 3–78 (2020).

    Article  Google Scholar 

  9. McGuire, P. & Dazzan, P. Does neuroimaging have a role in predicting outcomes in psychosis? World Psychiatry 16, 209–210 (2017).

    Article  Google Scholar 

  10. Wimberley, T. et al. Predictors of treatment resistance in patients with schizophrenia: a population-based cohort study. Lancet Psychiatry 3, 358–366 (2016).

    Article  Google Scholar 

  11. Demjaha, A. et al. Antipsychotic treatment resistance in first-episode psychosis: prevalence, subtypes and predictors. Psychol. Med. 47, 1981–1989 (2017).

    Article  Google Scholar 

  12. Chan, S. et al. Predictors of treatment resistant schizophrenia-spectrum disorder: 10-year retrospective study of first-episode psychosis (A56). Early Interv. Psychiatry 8, 78 (2014).

    Google Scholar 

  13. Bozzatello, P., Bellino, S. & Rocca, P. Predictive factors of treatment resistance in first episode of psychosis: a systematic review. Front. Psychiatry 10, 67 (2019).

    Article  Google Scholar 

  14. 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  Google Scholar 

  15. Üçok, A. et al. Correlates of clozapine use after a first episode of schizophrenia: results from a long-term prospective study. CNS Drugs 30, 997–1006 (2016).

    Article  Google Scholar 

  16. Smart, S., Kępińska, A., Murray, R. & MacCabe, J. Predictors of treatment resistant schizophrenia: a systematic review of prospective observational studies. Psychol. Med. 51, 44–53 (2021).

    Article  Google Scholar 

  17. Dwyer, D. B., Falkai, P. & Koutsouleris, N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev. Clin. Psychol. 14, 91–118 (2018).

    Article  Google Scholar 

  18. 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).

  19. Perry, B. I. et al. Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis. Lancet Psychiatry 8, 589–598 (2021).

  20. Perry, B. I., McIntosh, G., Weich, S., Singh, S. & Rees, K. The association between first-episode psychosis and abnormal glycaemic control: systematic review and meta-analysis. Lancet Psychiatry 3, 1049–1058 (2016).

    Article  Google Scholar 

  21. Pillinger, T., Beck, K., Stubbs, B. & Howes, O. D. Cholesterol and triglyceride levels in first-episode psychosis: systematic review and meta-analysis. Br. J. Psychiatry 211, 339–349 (2017).

    Article  Google Scholar 

  22. Pillinger, T. et al. A meta-analysis of immune parameters, variability, and assessment of modal distribution in psychosis and test of the immune subgroup hypothesis. Schizophr. Bull. 45, 1120–1133 (2019).

    Article  Google Scholar 

  23. 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  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  Google Scholar 

  27. Pardiñas, A. F. et al. Interaction testing and polygenic risk scoring to estimate the association of common genetic variants with treatment resistance in schizophrenia. JAMA Psychiatry 79, 260–269 (2022).

    Article  Google Scholar 

  28. Steyerberg, E. W. et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 10, e1001381 (2013).

    Article  Google Scholar 

  29. Wolff, R. F. et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann. Intern. Med. 170, 51–58 (2019).

    Article  Google Scholar 

  30. Morlán-Coarasa, M. J. et al. Incidence of non-alcoholic fatty liver disease and metabolic dysfunction in first episode schizophrenia and related psychotic disorders: a 3-year prospective randomized interventional study. Psychopharmacology 233, 3947–3952 (2016).

    Article  Google Scholar 

  31. Perry, B. I. et al. Dysglycaemia, inflammation and psychosis: findings from the UK ALSPAC birth cohort. Schizophr. Bull. 45, 330–338 (2019).

    Article  Google Scholar 

  32. Pillinger, T. et al. Impaired glucose homeostasis in first-episode schizophrenia: a systematic review and meta-analysis. JAMA Psychiatry 74, 261–269 (2017).

    Article  Google Scholar 

  33. Machado, M. V. & Diehl, A. M. Pathogenesis of nonalcoholic steatohepatitis. Gastroenterology 150, 1769–1777 (2016).

    Article  Google Scholar 

  34. Dix, H. M., Robinson, E. M. & Dillon, J. F. in Textbook of Addiction Treatment (eds. el-Guebaly, N., et al.) 1099–1111 (Springer, 2021).

  35. Van de Mortel, T. F. Faking it: social desirability response bias in self-report research. Aust. J. Adv. Nurs. 25, 40–48 (2008).

    Google Scholar 

  36. Moody, G. & Miller, B. J. Total and differential white blood cell counts and hemodynamic parameters in first-episode psychosis. Psychiatry Res. 260, 307–312 (2018).

    Article  Google Scholar 

  37. Garcia‐Rizo, C. et al. Blood cell count in antipsychotic‐naive patients with non‐affective psychosis. Early Interv. Psychiatry 13, 95–100 (2019).

    Article  Google Scholar 

  38. Perry, B. I. et al. Associations of immunological proteins/traits with schizophrenia, major depression and bipolar disorder: a bi-directional two-sample Mendelian randomization study. Brain Behav. Immun. 97, 176–185 (2021).

  39. Bunders, M., Cortina-Borja, M. & Newell, M.-L. Age-related standards for total lymphocyte, CD4+ and CD8+ T cell counts in children born in Europe. Pediatr. Infect. Dis. J. 24, 595–600 (2005).

    Article  Google Scholar 

  40. Lang, X. et al. Differences in patterns of metabolic abnormality and metabolic syndrome between early-onset and adult-onset first-episode drug-naive schizophrenia patients. Psychoneuroendocrinology 132, 105344 (2021).

  41. Psychosis and Schizophrenia in Adults: Prevention and Management CG178 (National Institute for Health and Care Excellence, 2014). https://www.nice.org.uk/guidance/cg178

  42. National Clinical Audit of Psychosis – National Report for the Early Intervention in Psychosis Audit 2019/2020. London (Royal College of Psychiatrists, 2020). www.rcpsych.ac.uk/NCAP

  43. Psychosis and Schizophrenia in Adults QS80 (National Institute for Health and Care Excellence, 2015). https://www.nice.org.uk/guidance/qs80

  44. Haw, C. & Stubbs, J. Off-label use of antipsychotics: are we mad? Expert Opin. Drug Saf. 6, 533–545 (2007).

    Article  Google Scholar 

  45. Hodgson, R. & Belgamwar, R. Off-label prescribing by psychiatrists. Psychiatric Bull. 30, 55–57 (2006).

    Article  Google Scholar 

  46. Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    Article  Google Scholar 

  47. Chan, S. K. W. et al. Predictors of treatment-resistant and clozapine-resistant schizophrenia: a 12-year follow-up study of first-episode schizophrenia-spectrum disorders. Schizophrenia Bull. 47, 485–494 (2021).

    Article  Google Scholar 

  48. Riley, R. D. et al. Minimum sample size for developing a multivariable prediction model: PART II—binary and time‐to‐event outcomes. Stat. Med. 38, 1276–1296 (2019).

    Article  Google Scholar 

  49. Cardinal, R. N. Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Med. Inf. Decis. Making 17, 50 (2017).

    Article  Google Scholar 

  50. McGorry, P. D. Early intervention in psychosis: obvious, effective, overdue. J. Nerv. Ment. Dis. 203, 310–318 (2015).

    Article  Google Scholar 

  51. Meltzer, H. Y. Treatment-resistant schizophrenia-the role of clozapine. Curr. Med. Res. Opin. 14, 1–20 (1997).

    Article  Google Scholar 

  52. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

  53. Ensor, J., Martin, E. C. & Riley, R. D. pmsampsize (2021). https://cran.r-project.org/web/packages/pmsampsize/index.html

  54. Van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67. (2011).

    Article  Google Scholar 

  55. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 12, 77 (2011).

    Article  Google Scholar 

  56. Van Calster, B. et al. A calibration hierarchy for risk models was defined: from utopia to empirical data. J. Clin. Epidemiol. 74, 167–176 (2016).

    Article  Google Scholar 

  57. Schomaker, M. & Heumann, C. Model selection and model averaging after multiple imputation. Comput. Stat. Data Anal. 71, 758–770 (2014).

    Article  Google Scholar 

  58. Eekhout, I., Van De Wiel, M. A. & Heymans, M. W. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med. Res. Method. 17, 129 (2017).

    Article  Google Scholar 

  59. Radchenko, P. & James, G. M. Variable inclusion and shrinkage algorithms. J. Am. Stat. Assoc. 103, 1304–1315 (2008).

    Article  Google Scholar 

  60. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  Google Scholar 

  61. Steyerberg, E. W. Clinical Prediction Models (Springer, 2019).

  62. Steyerberg, E. W., Borsboom, G. J., van Houwelingen, H. C., Eijkemans, M. J. & Habbema, J. D. F. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat. Med. 23, 2567–2586 (2004).

    Article  Google Scholar 

  63. Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Making 26, 565–574 (2006).

    Article  Google Scholar 

  64. Vickers, A. J., van Calster, B. & Steyerberg, E. W. A simple, step-by-step guide to interpreting decision curve analysis. Diagn. Progn. Res. 3, 18 (2019).

    Article  Google Scholar 

  65. Chang, W. et al. shiny: Web Application Framework for R v.1.7.2 (2022). https://cran.r-project.org/web/packages/shiny/index.html

Download references

Acknowledgements

This work was funded by a Clinical PhD Fellowship to E.F.O. jointly funded by the NIHR Imperial BRC and the UK Research and Innovation Medical Research Council London Institute of Medical Sciences. B.I.P. acknowledges funding support from the NIHR (doctoral research fellowship, DRF-2018-11-ST2-018). R.U. received funding support from the NIHR (HTA grant 127700) and Medical Research Council (Therapeutic Target Validation in Mental Health grant MR/S037675/1). G.M.K. received funding support from the Wellcome Trust (grant 201486/Z/16/Z), the MQ: Transforming Mental Health (grant MQDS17/40), the Medical Research Council UK (grants MC_PC_17213; MR/S037675/1; and MR/W014416/1), and the British Medical Association Foundation (J Moulton grant 2019). R.N.C. acknowledges support from the Medical Research Council (grants MC_PC_17213, MR/W014386/1). This research was supported in part by the NIHR Imperial BRC and NIHR Cambridge BRC (BRC-1215-20014); J.P. and P.B.J. acknowledge funding from the NIHR ARC EoE; the views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funding bodies had no role in design or conduct of the study; collection, management, analysis or interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Author notes

  1. These authors contributed equally: Emanuele F. Osimo, Benjamin I. Perry.

  2. These authors jointly supervised this work: Rachel Upthegrove, Golam M. Khandaker.

Authors and Affiliations

  1. MRC London Institute of Medical Sciences and Institute of Clinical Sciences, Hammersmith Campus, Imperial College London, London, UK

    Emanuele F. Osimo & Oliver D. Howes

  2. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Emanuele F. Osimo, Benjamin I. Perry, Graham K. Murray, Jesus Perez, Peter B. Jones, Rudolf N. Cardinal & Golam M. Khandaker

  3. Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK

    Emanuele F. Osimo, Benjamin I. Perry, Jonathan Lewis, Graham K. Murray, Jesus Perez, Peter B. Jones, Rudolf N. Cardinal & Golam M. Khandaker

  4. South London and Maudsley NHS Foundation Trust, London, UK

    Emanuele F. Osimo, Megan Pritchard & Oliver D. Howes

  5. Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK

    Pavan Mallikarjun & Rachel Upthegrove

  6. Birmingham Early Intervention Service, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK

    Pavan Mallikarjun, Asia Katunda & Rachel Upthegrove

  7. Norwich Medical School, University of East Anglia, Norwich, UK

    Jesus Perez

  8. Applied Research Collaboration East of England, National Institute for Health Research, Cambridge, UK

    Jesus Perez & Peter B. Jones

  9. Department of Medicine, Institute of Biomedical Research (IBSAL), University of Salamanca, Salamanca, Spain

    Jesus Perez

  10. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK

    Oliver D. Howes

  11. MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England

    Golam M. Khandaker

  12. Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England

    Golam M. Khandaker

Authors
  1. Emanuele F. Osimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin I. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pavan Mallikarjun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Megan Pritchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jonathan Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Asia Katunda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Graham K. Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jesus Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peter B. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rudolf N. Cardinal
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Oliver D. Howes
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rachel Upthegrove
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Golam M. Khandaker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.F.O. and B.I.P. designed the study and selected the predictors and outcome variables under the supervision of R.U. and G.M.K., and with input from P.M., G.K.M., J.P., P.B.J., R.N.C. and O.D.H.; E.F.O. had access to all datasets, collected the data and performed the statistical analyses, in close discussion with B.I.P. and the wider supervisory team. M.P. and O.D.H. supported E.F.O. in data collection and analysis for the SLaM cohort. J.L. and R.N.C. supported E.F.O. in data collection and analysis for the Cambridge cohort. A.K. and R.U. supported E.F.O. in data collection and analysis for the Birmingham cohort. E.F.O. wrote the first draft of the manuscript, with constant support from B.I.P. All other authors contributed to the drafting, re-drafting and perfecting of the manuscript, including responses to reviewers’ comments.

Corresponding author

Correspondence to Emanuele F. Osimo.

Ethics declarations

Competing interests

O.D.H. is a part-time employee of H. Lundbeck A/S. He has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by Angelini, Autifony, Biogen, Boehringer Ingelheim, Eli Lilly, Heptares, Global Medical Education, Invicro, Janssen, H. Lundbeck A/S, Neurocrine, Otsuka, Sunovion, Recordati, Roche and Viatris/Mylan. O.D.H. has a patent for the use of dopaminergic imaging. R.N.C. consults for Campden Instruments and receives royalties from Cambridge Enterprise, Routledge and Cambridge University Press. The other authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 PubMed results for ‘risk AND prediction AND psychosis’ by year.

Generated with PubMed by Year. Available from http://esperr.github.io/pubmed-by-year/.

Extended Data Fig. 2 Internal validation: distribution of predicted probabilities for MOZART.

X axis: predicted probability.

Extended Data Fig. 3 Internal validation: distribution of predicted probabilities for the LASSO model.

X axis: predicted probability.

Extended Data Fig. 4 Internal validation: calibration plot for MOZART.

Model calibration is the extent to which outcomes predicted by the model are similar to those observed in the validation dataset. Calibration plots illustrate agreement between observed proportion (y axis) and predicted risk (x axis). Perfect agreement would trace the red line. Model calibration is shown by the continuous black line. Triangles denote grouped observations for participants at deciles of predicted risk, with 95% CIs indicated by the vertical black lines. Axes range between 0 and 0.3 since very few individuals received predicted probabilities greater than 0.3. N=785 participants in pooled development sample.

Extended Data Fig. 5 Internal validation: calibration plot for the LASSO model.

Model calibration is the extent to which outcomes predicted by the model are similar to those observed in the validation dataset. Calibration plots illustrate agreement between observed proportion (y axis) and predicted risk (x axis). Perfect agreement would trace the red line. Model calibration is shown by the continuous black line. Triangles denote grouped observations for participants at deciles of predicted risk, with 95% CIs indicated by the vertical black lines. Axes range between 0 and 0.3 since very few individuals received predicted probabilities greater than 0.3. N=785 participants in pooled development sample.

Extended Data Fig. 6 Internal validation: distribution of predicted probabilities for M3.

X axis: predicted probability.

Extended Data Fig. 7 Internal validation: calibration plot for M3.

Calibration plots illustrate agreement between observed proportion (y axis) and predicted risk (x axis). Perfect agreement would trace the red line. Model calibration is shown by the continuous black line. Triangles denote grouped observations for participants at deciles of predicted risk, with 95% CIs indicated by the vertical black lines. Axes range between 0 and 0.3 since very few individuals received predicted probabilities greater than 0.3. N=785 participants in pooled development sample.

Extended Data Fig. 8 Internal validation: distribution of predicted probabilities for M4.

X axis: predicted probability.

Extended Data Fig. 9 Internal validation: calibration plot for M4.

Calibration plots illustrate agreement between observed proportion (y axis) and predicted risk (x axis). Perfect agreement would trace the red line. Model calibration is shown by the continuous black line. Triangles denote grouped observations for participants at deciles of predicted risk, with 95% CIs indicated by the vertical black lines. Axes range between 0 and 0.3 since very few individuals received predicted probabilities greater than 0.3. N=785 participants in pooled development sample.

Supplementary information

Supplementary Information

Supplementary notes and Tables 1–5.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Osimo, E.F., Perry, B.I., Mallikarjun, P. et al. Predicting treatment resistance from first-episode psychosis using routinely collected clinical information. Nat. Mental Health 1, 25–35 (2023). https://doi.org/10.1038/s44220-022-00001-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44220-022-00001-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing