Skip to main content Accessibility help
×
Home
Hostname: page-component-55597f9d44-jzjqj Total loading time: 0.434 Render date: 2022-08-15T22:27:41.033Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression

Published online by Cambridge University Press:  14 February 2022

Linda A. Antonucci*
Affiliation:
Department of Education Science, Psychology and Communication Science, University of Bari Aldo Moro, Italy; and Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Nora Penzel
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
Rachele Sanfelici
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Institute for Psychiatry, Max Planck School of Cognition, Germany
Alessandro Pigoni
Affiliation:
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Italy; and Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Italy
Lana Kambeitz-Ilankovic
Affiliation:
Department of Education Science, Psychology and Communication Science, University of Bari Aldo Moro, Italy; and Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Dominic Dwyer
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Anne Ruef
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Mark Sen Dong
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Ömer Faruk Öztürk
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Institute for Psychiatry, International Max Planck Research School for Translational Psychiatry, Germany
Katharine Chisholm
Affiliation:
Institute for Mental Health, University of Birmingham, UK; and Department of Psychology, Aston University, UK
Theresa Haidl
Affiliation:
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
Marlene Rosen
Affiliation:
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
Adele Ferro
Affiliation:
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Italy
Giulio Pergola
Affiliation:
Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
Ileana Andriola
Affiliation:
Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
Giuseppe Blasi
Affiliation:
Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
Stephan Ruhrmann
Affiliation:
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
Frauke Schultze-Lutter
Affiliation:
Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Germany; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Indonesia; and University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
Peter Falkai
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
Joseph Kambeitz
Affiliation:
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
Rebekka Lencer
Affiliation:
Institute for Translational Psychiatry, University of Münster, UK; and Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
Udo Dannlowski
Affiliation:
Institute for Translational Psychiatry, University of Münster, UK
Rachel Upthegrove
Affiliation:
Institute for Mental Health, University of Birmingham, UK; and Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK
Raimo K. R. Salokangas
Affiliation:
Department of Psychiatry, University of Turku, UK
Christos Pantelis
Affiliation:
Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia
Eva Meisenzahl
Affiliation:
Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Germany
Stephen J. Wood
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; Orygen, Australia; Centre for Youth Mental Health, University of Melbourne, Australia; and School of Psychology, University of Birmingham, UK
Paolo Brambilla
Affiliation:
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Italy; and Department of Pathophysiology and Transplantation, University of Milan, Italy
Stefan Borgwardt
Affiliation:
Institute for Translational Psychiatry, University of Münster, UK; and Department of Psychiatry (Psychiatric University Hospital, University Psychiatric Clinics Basel), University of Basel, Switzerland
Alessandro Bertolino
Affiliation:
Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
Nikolaos Koutsouleris
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
*
Correspondence: Linda A. Antonucci. Email: linda.antonucci@uniba.it

Abstract

Background

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

Aims

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

Method

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

Results

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

Conclusions

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

Type
Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists

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

Bora, E, Harrison, BJ, Yucel, M, Pantelis, C. Cognitive impairment in euthymic major depressive disorder: a meta-analysis. Psychol Med 2013; 43(10): 2017–26.10.1017/S0033291712002085CrossRefGoogle ScholarPubMed
Ruhrmann, S, Paruch, J, Bechdolf, A, Pukrop, R, Wagner, M, Berning, J, et al. Reduced subjective quality of life in persons at risk for psychosis. Acta Psychiatr Scand 2008; 117(5): 357–68.10.1111/j.1600-0447.2008.01152.xCrossRefGoogle ScholarPubMed
Schultze-Lutter, F, Michel, C, Ruhrmann, S, Schimmelmann, BG. Prevalence and clinical relevance of interview-assessed psychosis-risk symptoms in the young adult community. Psychol Med 2018; 48(7): 1167–78.10.1017/S0033291717002586CrossRefGoogle ScholarPubMed
Harvey, PD, Strassnig, M. Predicting the severity of everyday functional disability in people with schizophrenia: cognitive deficits, functional capacity, symptoms, and health status. World Psychiatry 2012; 11(2): 73–9.10.1016/j.wpsyc.2012.05.004CrossRefGoogle ScholarPubMed
Yung, AR, Yuen, HP, McGorry, PD, Phillips, LJ, Kelly, D, Dell'Olio, M, et al. Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States. Aust N Z J Psychiatry 2005; 39(11–12): 964–71.10.1080/j.1440-1614.2005.01714.xCrossRefGoogle ScholarPubMed
Dwyer, DB, Falkai, P, Koutsouleris, N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018; 14: 91118.10.1146/annurev-clinpsy-032816-045037CrossRefGoogle ScholarPubMed
Koutsouleris, N, Kambeitz-Ilankovic, L, Ruhrmann, S, Rosen, M, Ruef, A, Dwyer, DB, et al. Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry 2018; 75(11): 1156–72.10.1001/jamapsychiatry.2018.2165CrossRefGoogle ScholarPubMed
Burton, CZ, Tso, IF, Carrion, RE, Niendam, T, Adelsheim, S, Auther, AM, et al. Baseline psychopathology and relationship to longitudinal functional outcome in attenuated and early first episode psychosis. Schizophr Res 2019; 212: 157–62.10.1016/j.schres.2019.07.048CrossRefGoogle ScholarPubMed
Carrion, RE, Goldberg, TE, McLaughlin, D, Auther, AM, Correll, CU, Cornblatt, BA. Impact of neurocognition on social and role functioning in individuals at clinical high risk for psychosis. Am J Psychiatry 2011; 168(8): 806–13.10.1176/appi.ajp.2011.10081209CrossRefGoogle ScholarPubMed
Carrion, RE, Auther, AM, McLaughlin, D, Addington, J, Bearden, CE, Cadenhead, KS, et al. Social decline in the psychosis prodrome: predictor potential and heterogeneity of outcome. Schizophr Res 2021; 227: 4451.10.1016/j.schres.2020.09.006CrossRefGoogle Scholar
Velthorst, E, Zinberg, J, Addington, J, Cadenhead, KS, Cannon, TD, Carrion, RE, et al. Potentially important periods of change in the development of social and role functioning in youth at clinical high risk for psychosis. Dev Psychopathol 2018; 30(1): 3947.10.1017/S0954579417000451CrossRefGoogle ScholarPubMed
Cornblatt, BA, Carrion, RE, Addington, J, Seidman, L, Walker, EF, Cannon, TD, et al. Risk factors for psychosis: impaired social and role functioning. Schizophr Bull 2012; 38(6): 1247–57.10.1093/schbul/sbr136CrossRefGoogle ScholarPubMed
Evert, H, Harvey, C, Trauer, T, Herrman, H. The relationship between social networks and occupational and self-care functioning in people with psychosis. Soc Psychiatry Psychiatr Epidemiol 2003; 38(4): 180–8.Google ScholarPubMed
Howes, OD, Murray, RM. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet 2014; 383(9929): 1677–87.10.1016/S0140-6736(13)62036-XCrossRefGoogle ScholarPubMed
Kwong, ASF, Lopez-Lopez, JA, Hammerton, G, Manley, D, Timpson, NJ, Leckie, G, et al. Genetic and environmental risk factors associated with trajectories of depression symptoms from adolescence to young adulthood. JAMA Netw Open 2019; 2(6): e196587.10.1001/jamanetworkopen.2019.6587CrossRefGoogle ScholarPubMed
Schmitt, A, Malchow, B, Hasan, A, Falkai, P. The impact of environmental factors in severe psychiatric disorders. Front Neurosci 2014; 8: 19.10.3389/fnins.2014.00019CrossRefGoogle ScholarPubMed
Baker, LM, Williams, LM, Korgaonkar, MS, Cohen, RA, Heaps, JM, Paul, RH. Impact of early vs. late childhood early life stress on brain morphometrics. Brain Imaging Behav 2013; 7(2): 196203.10.1007/s11682-012-9215-yCrossRefGoogle ScholarPubMed
Carballedo, A, Lisiecka, D, Fagan, A, Saleh, K, Ferguson, Y, Connolly, G, et al. Early life adversity is associated with brain changes in subjects at family risk for depression. World J Biol Psychiatry 2012; 13(8): 569–78.10.3109/15622975.2012.661079CrossRefGoogle ScholarPubMed
Popovic, D, Ruef, A, Dwyer, DB, Antonucci, LA, Eder, J, Sanfelici, R, et al. Traces of trauma: a multivariate pattern analysis of childhood trauma, brain structure, and clinical phenotypes. Biol Psychiatry 2020; 88(11): 829–42.10.1016/j.biopsych.2020.05.020CrossRefGoogle ScholarPubMed
Schultze-Lutter, F, Schimmelmann, BG, Michel, C. Clinical high-risk of and conversion to psychosis in the community: a 3-year follow-up of a cohort study. Schizophr Res 2021; 228: 616–8.10.1016/j.schres.2020.11.032CrossRefGoogle ScholarPubMed
Fusar-Poli, P, Nelson, B, Valmaggia, L, Yung, AR, McGuire, PK. Comorbid depressive and anxiety disorders in 509 individuals with an at-risk mental state: impact on psychopathology and transition to psychosis. Schizophr Bull 2014; 40(1): 120–31.10.1093/schbul/sbs136CrossRefGoogle ScholarPubMed
Fusar-Poli, P, Salazar de Pablo, G, Correll, CU, Meyer-Lindenberg, A, Millan, MJ, Borgwardt, S, et al. Prevention of psychosis: advances in detection, prognosis, and intervention. JAMA Psychiatry 2020; 77(7): 755–65.10.1001/jamapsychiatry.2019.4779CrossRefGoogle Scholar
Fusar-Poli, P, Tantardini, M, De Simone, S, Ramella-Cravaro, V, Oliver, D, Kingdon, J, et al. Deconstructing vulnerability for psychosis: meta-analysis of environmental risk factors for psychosis in subjects at ultra high-risk. Eur Psychiatry 2017; 40: 6575.10.1016/j.eurpsy.2016.09.003CrossRefGoogle ScholarPubMed
Lee, TY, Lee, J, Kim, M, Choe, E, Kwon, JS. Can we predict psychosis outside the clinical high-risk state? A systematic review of non-psychotic risk syndromes for mental disorders. Schizophr Bull 2018; 44(2): 276–85.10.1093/schbul/sbx173CrossRefGoogle Scholar
Koutsouleris, N, Worthington, M, Dwyer, DB, Kambeitz-Ilankovic, L, Sanfelici, R, Fusar-Poli, P, et al. Toward generalizable and transdiagnostic tools for psychosis prediction: an independent validation and improvement of the NAPLS-2 risk calculator in the multisite PRONIA cohort. Biol Psychiatry 2021; 90(9): 632–42.10.1016/j.biopsych.2021.06.023CrossRefGoogle ScholarPubMed
Rosen, M, Betz, LT, Schultze-Lutter, F, Chisholm, K, Haidl, TK, Kambeitz-Ilankovic, L, et al. Towards clinical application of prediction models for transition to psychosis: a systematic review and external validation study in the PRONIA sample. Neurosci Biobehav Rev 2021; 125: 478–92.10.1016/j.neubiorev.2021.02.032CrossRefGoogle ScholarPubMed
Antonucci, LA, Pergola, G, Pigoni, A, Dwyer, D, Kambeitz-Ilankovic, L, Penzel, N, et al. A pattern of cognitive deficits stratified for genetic and environmental risk reliably classifies patients with schizophrenia from healthy control subjects. Biological Psychiatry 2019; 87: 697707.10.1016/j.biopsych.2019.11.007CrossRefGoogle ScholarPubMed
Cannon, TD, Yu, C, Addington, J, Bearden, CE, Cadenhead, KS, Cornblatt, BA, et al. An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry 2016; 173(10): 980–8.10.1176/appi.ajp.2016.15070890CrossRefGoogle ScholarPubMed
Chekroud, AM, Zotti, RJ, Shehzad, Z, Gueorguieva, R, Johnson, MK, Trivedi, MH, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2016; 3(3): 243–50.10.1016/S2215-0366(15)00471-XCrossRefGoogle ScholarPubMed
Kessler, RC, van Loo, HM, Wardenaar, KJ, Bossarte, RM, Brenner, LA, Cai, T, et al. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry 2016; 21(10): 1366–71.10.1038/mp.2015.198CrossRefGoogle ScholarPubMed
Grassi, M, Perna, G, Caldirola, D, Schruers, K, Duara, R, Loewenstein, DA. A clinically-translatable machine learning algorithm for the prediction of Alzheimer's disease conversion in individuals with mild and premild cognitive impairment. J Alzheimers Dis 2018; 61(4): 1555–73.10.3233/JAD-170547CrossRefGoogle ScholarPubMed
Feng, R, Badgeley, M, Mocco, J, Oermann, EK. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg 2018; 10(4): 358–62.10.1136/neurintsurg-2017-013355CrossRefGoogle ScholarPubMed
Bodatsch, M, Ruhrmann, S, Wagner, M, Muller, R, Schultze-Lutter, F, Frommann, I, et al. Prediction of psychosis by mismatch negativity. Biol Psychiatry 2011; 69(10): 959–66.10.1016/j.biopsych.2010.09.057CrossRefGoogle ScholarPubMed
Koutsouleris, N, Wobrock, T, Guse, B, Langguth, B, Landgrebe, M, Eichhammer, P, et al. Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis. Schizophr Bull 2018; 44(5): 1021–34.10.1093/schbul/sbx114CrossRefGoogle ScholarPubMed
Koutsouleris, N, Upthegrove, R, Wood, SJ. Importance of variable selection in multimodal prediction models in patients at clinical high risk for psychosis and recent onset depression-reply. JAMA Psychiatry 2019; 76(3): 339–40.10.1001/jamapsychiatry.2018.4237CrossRefGoogle ScholarPubMed
Koutsouleris, N, Kahn, RS, Chekroud, AM, Leucht, S, Falkai, P, Wobrock, T, et al. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry 2016; 3(10): 935–46.10.1016/S2215-0366(16)30171-7CrossRefGoogle ScholarPubMed
Kambeitz-Ilankovic, L, Meisenzahl, EM, Cabral, C, von Saldern, S, Kambeitz, J, Falkai, P, et al. Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophr Res 2016; 173(3): 159–65.10.1016/j.schres.2015.03.005CrossRefGoogle ScholarPubMed
Lin, A, Wood, SJ, Nelson, B, Beavan, A, McGorry, P, Yung, AR. Outcomes of nontransitioned cases in a sample at ultra-high risk for psychosis. Am J Psychiatry 2015; 172(3): 249–58.10.1176/appi.ajp.2014.13030418CrossRefGoogle Scholar
Loewy, RL, Corey, S, Amirfathi, F, Dabit, S, Fulford, D, Pearson, R, et al. Childhood trauma and clinical high risk for psychosis. Schizophr Res 2019; 205: 10–4.10.1016/j.schres.2018.05.003CrossRefGoogle ScholarPubMed
Pergola, G, Papalino, M, Gelao, B, Sportelli, L, Vollerbergh, W, Grattagliano, I, et al. Evocative gene-environment correlation between genetic risk for schizophrenia and bullying victimization. World Psychiatry 2019; 18(3): 366–7.10.1002/wps.20685CrossRefGoogle ScholarPubMed
Tarbox, SI, Addington, J, Cadenhead, KS, Cannon, TD, Cornblatt, BA, Perkins, DO, et al. Premorbid functional development and conversion to psychosis in clinical high-risk youths. Dev Psychopathol 2013; 25(4 Pt 1): 1171–86.10.1017/S0954579413000448CrossRefGoogle ScholarPubMed
Upthegrove, R. Bullying, victimisation, and psychosis. Lancet Psychiatry 2015; 2(7): 574–6.10.1016/S2215-0366(15)00176-5CrossRefGoogle ScholarPubMed
Hill, RM, Mellick, W, Temple, JR, Sharp, C. The role of bullying in depressive symptoms from adolescence to emerging adulthood: a growth mixture model. J Affect Disord 2017; 207: 18.10.1016/j.jad.2016.09.007CrossRefGoogle ScholarPubMed
Negele, A, Kaufhold, J, Kallenbach, L, Leuzinger-Bohleber, M. Childhood trauma and its relation to chronic depression in adulthood. Depress Res Treat 2015; 2015: 650804.Google ScholarPubMed
Scher, CD, Stein, MB, Asmundson, GJ, McCreary, DR, Forde, DR. The childhood trauma questionnaire in a community sample: psychometric properties and normative data. J Trauma Stress 2001; 14(4): 843–57.10.1023/A:1013058625719CrossRefGoogle Scholar
Vocisano, C, Klein, DN, Keefe, RS, Dienst, ER, Kincaid, MM. Demographics, family history, premorbid functioning, developmental characteristics, and course of patients with deteriorated affective disorder. Am J Psychiatry 1996; 153(2): 248–55.Google ScholarPubMed
Cornblatt, BA, Auther, AM, Niendam, T, Smith, CW, Zinberg, J, Bearden, CE, et al. Preliminary findings for two new measures of social and role functioning in the prodromal phase of schizophrenia. Schizophr Bull 2007; 33(3): 688702.10.1093/schbul/sbm029CrossRefGoogle ScholarPubMed
Haidl, T, Schneider, N, Dickmann, K, Ruhrmann, S, Kaiser, N, Rosen, M, et al. Validation of the Bullying Scale for Adults - results of the PRONIA-study. J Psychiatr Res 2020; 129: 8897.10.1016/j.jpsychires.2020.04.004CrossRefGoogle ScholarPubMed
Shapiro, DI, Marenco, S, Spoor, EH, Egan, MF, Weinberger, DR, Gold, JM. The Premorbid Adjustment Scale as a measure of developmental compromise in patients with schizophrenia and their healthy siblings. Schizophr Res 2009; 112(1–3): 136–42.10.1016/j.schres.2009.04.007CrossRefGoogle ScholarPubMed
Garcia, D, Al Nima, A, Kjell, ON. The affective profiles, psychological well-being, and harmony: environmental mastery and self-acceptance predict the sense of a harmonious life. PeerJ 2014; 2: e259.10.7717/peerj.259CrossRefGoogle ScholarPubMed
Rhemtulla, M, Brosseau-Liard, PE, Savalei, V. When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychol Methods 2012; 17(3): 354–73.10.1037/a0029315CrossRefGoogle ScholarPubMed
Wolpert, DH. Stacked generalization. Neural Networks 1992; 5: 241–59.10.1016/S0893-6080(05)80023-1CrossRefGoogle Scholar
Guo, C, Pleiss, G, Sun, Y, Weinberger, KQ. On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning (Sydney, Australia, 2017). PMLR 70, 2017.Google Scholar
Miller, TJ, McGlashan, TH, Rosen, JL, Cadenhead, K, Cannon, T, Ventura, 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 2003; 29(4): 703–15.10.1093/oxfordjournals.schbul.a007040CrossRefGoogle ScholarPubMed
Skevington, SM, Lotfy, M, O'Connell, KA, Group, W. The World Health Organization's WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res 2004; 13(2): 299310.10.1023/B:QURE.0000018486.91360.00CrossRefGoogle Scholar
Woods, SW, Powers, AR 3rd, Taylor, JH, Davidson, CA, Johannesen, JK, Addington, J, et al. Lack of diagnostic pluripotentiality in patients at clinical high risk for psychosis: specificity of comorbidity persistence and search for pluripotential subgroups. Schizophr Bull 2018; 44(2): 254–63.10.1093/schbul/sbx138CrossRefGoogle ScholarPubMed
Thaipisuttikul, P, Ittasakul, P, Waleeprakhon, P, Wisajun, P, Jullagate, S. Psychiatric comorbidities in patients with major depressive disorder. Neuropsychiatr Dis Treat 2014; 10: 2097–103.Google ScholarPubMed
Hasin, DS, Sarvet, AL, Meyers, JL, Saha, TD, Ruan, WJ, Stohl, M, et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 2018; 75(4): 336–46.10.1001/jamapsychiatry.2017.4602CrossRefGoogle ScholarPubMed
Dwyer, DB, Cabral, C, Kambeitz-Ilankovic, L, Sanfelici, R, Kambeitz, J, Calhoun, V, et al. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia. Schizophr Bull 2018; 44(5): 1060–9.10.1093/schbul/sby008CrossRefGoogle ScholarPubMed
Vieira, S, Gong, Q, Scarpazza, C, Lui, S, Huang, X, Crespo-Facorro, B, et al. Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis. Psychol Med 2021; 51(2): 340–50.10.1017/S0033291719003568CrossRefGoogle ScholarPubMed
Koutsouleris, N, Dwyer, DB, Degenhardt, F, Maj, C, Urquijo-Castro, MF, Sanfelici, R, et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry 2021; 78(2): 195209.10.1001/jamapsychiatry.2020.3604CrossRefGoogle ScholarPubMed
Cannon-Spoor, HE, Potkin, SG, Wyatt, RJ. Measurement of premorbid adjustment in chronic schizophrenia. Schizophr Bull 1982; 8(3): 470–84.10.1093/schbul/8.3.470CrossRefGoogle ScholarPubMed
Tyborowska, A, Volman, I, Niermann, HCM, Pouwels, JL, Smeekens, S, Cillessen, AHN, et al. Early-life and pubertal stress differentially modulate grey matter development in human adolescents. Sci Rep 2018; 8(1): 9201.10.1038/s41598-018-27439-5CrossRefGoogle ScholarPubMed
Oliver, D, Radua, J, Reichenberg, A, Uher, R, Fusar-Poli, P. Psychosis Polyrisk Score (PPS) for the detection of individuals at-risk and the prediction of their outcomes. Front Psychiatry 2019; 10: 174.10.3389/fpsyt.2019.00174CrossRefGoogle ScholarPubMed
Bhavsar, V, Boydell, J, McGuire, P, Harris, V, Hotopf, M, Hatch, SL, et al. Childhood abuse and psychotic experiences - evidence for mediation by adulthood adverse life events. Epidemiol Psychiatr Sci 2019; 28(3): 300–9.10.1017/S2045796017000518CrossRefGoogle ScholarPubMed
Morgan, C, Reininghaus, U, Fearon, P, Hutchinson, G, Morgan, K, Dazzan, P, et al. Modelling the interplay between childhood and adult adversity in pathways to psychosis: initial evidence from the AESOP study. Psychol Med 2014; 44(2): 407–19.10.1017/S0033291713000767CrossRefGoogle ScholarPubMed
Hafeman, DM, Schwartz, S. Opening the black box: a motivation for the assessment of mediation. Int J Epidemiol 2009; 38(3): 838–45.10.1093/ije/dyn372CrossRefGoogle ScholarPubMed
Evans, EA, Grella, CE, Upchurch, DM. Gender differences in the effects of childhood adversity on alcohol, drug, and polysubstance-related disorders. Soc Psychiatry Psychiatr Epidemiol 2017; 52(7): 901–12.10.1007/s00127-017-1355-3CrossRefGoogle ScholarPubMed
Huang, Y, Li, W, Macheret, F, Gabriel, RA, Ohno-Machado, L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc 2020; 27(4): 621–33.10.1093/jamia/ocz228CrossRefGoogle ScholarPubMed
Naeini, MP, Cooper, GF, Hauskrecht, M. Obtaining well calibrated probabilities using Bayesian binning. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (Austin, Texas, 25–30 Jan 2015). Association for the Advancement of Artificial Intelligence, 2015.Google ScholarPubMed
Stilo, SA, Murray, RM. Non-genetic factors in schizophrenia. Curr Psychiatry Rep 2019; 21(10): 100.10.1007/s11920-019-1091-3CrossRefGoogle Scholar
Holz, NE, Tost, H, Meyer-Lindenberg, A. Resilience and the brain: a key role for regulatory circuits linked to social stress and support. Mol Psychiatry 2020; 25(2): 379–96.10.1038/s41380-019-0551-9CrossRefGoogle ScholarPubMed
Haining, K, Brunner, G, Gajwani, R, Gross, J, Gumley, AI, Lawrie, SM, et al. The relationship between cognitive deficits and impaired short-term functional outcome in clinical high-risk for psychosis participants: a machine learning and modelling approach. Schizophr Res 2021; 231: 2431.10.1016/j.schres.2021.02.019CrossRefGoogle ScholarPubMed
Mongan, D, Focking, M, Healy, C, Susai, SR, Heurich, M, Wynne, K, 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 2021; 78(1): 7790.10.1001/jamapsychiatry.2020.2459CrossRefGoogle ScholarPubMed
van der Ploeg, T, Austin, PC, Steyerberg, EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol 2014; 14: 137.10.1186/1471-2288-14-137CrossRefGoogle ScholarPubMed
Riley, RD, Snell, KIE, Ensor, J, Burke, DL, Harrell, FE Jr, Moons, KGM, et al. Minimum sample size for developing a multivariable prediction model: part I - continuous outcomes. Stat Med 2019; 38(7): 1262–75.10.1002/sim.7993CrossRefGoogle ScholarPubMed
Sanfelici, R, Dwyer, DB, Antonucci, LA, Koutsouleris, N. Individualized diagnostic and prognostic models for patients with psychosis risk syndromes: a meta-analytic view on the state of the art. Biol Psychiatry 2020; 88: 349–60.10.1016/j.biopsych.2020.02.009CrossRefGoogle ScholarPubMed
Supplementary material: File

Antonucci et al. supplementary material

Antonucci et al. supplementary material

Download Antonucci et al. supplementary material(File)
File 3 MB
Submit a response

eLetters

No eLetters have been published for this article.

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *