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Oliver D, Arribas M, Perry BI, Whiting D, Blackman G, Krakowski K, Seyedsalehi A, Osimo EF, Griffiths SL, Stahl D, Cipriani A, Fazel S, Fusar-Poli P, McGuire P. Using Electronic Health Records to Facilitate Precision Psychiatry. Biol Psychiatry 2024; 96:532-542. [PMID: 38408535 DOI: 10.1016/j.biopsych.2024.02.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.
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Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Daniel Whiting
- Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Graham Blackman
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Kamil Krakowski
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Aida Seyedsalehi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom; Imperial College London Institute of Clinical Sciences and UK Research and Innovation MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, United Kingdom; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Siân Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Andrea Cipriani
- NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom
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2
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Hao J, Tiles-Sar N, Habtewold TD, Liemburg EJ, Bruggeman R, van der Meer L, Alizadeh BZ. Shaping tomorrow's support: baseline clinical characteristics predict later social functioning and quality of life in schizophrenia spectrum disorder. Soc Psychiatry Psychiatr Epidemiol 2024:10.1007/s00127-024-02630-4. [PMID: 38456932 DOI: 10.1007/s00127-024-02630-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 01/28/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE We aimed to explore the multidimensional nature of social inclusion (mSI) among patients diagnosed with schizophrenia spectrum disorder (SSD), and to identify the predictors of 3-year mSI and the mSI prediction using traditional and data-driven approaches. METHODS We used the baseline and 3-year follow-up data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort in the Netherlands. The outcome mSI was defined as clusters derived from combined analyses of thirteen subscales from the Social Functioning Scale and the brief version of World Health Organization Quality of Life questionnaires through K-means clustering. Prediction models were built through multinomial logistic regression (ModelMLR) and random forest (ModelRF), internally validated via bootstrapping and compared by accuracy and the discriminability of mSI subgroups. RESULTS We identified five mSI subgroups: "very low (social functioning)/very low (quality of life)" (8.58%), "low/low" (12.87%), "high/low" (49.24%), "medium/high" (18.05%), and "high/high" (11.26%). The mSI was robustly predicted by a genetic predisposition for SSD, premorbid adjustment, positive, negative, and depressive symptoms, number of met needs, and baseline satisfaction with the environment and social life. The ModelRF (61.61% [54.90%, 68.01%]; P =0.013) was cautiously considered outperform the ModelMLR (59.16% [55.75%, 62.58%]; P =0.994). CONCLUSION We introduced and distinguished meaningful subgroups of mSI, which were modestly predictable from baseline clinical characteristics. A possibility for early prediction of mSI at the clinical stage may unlock the potential for faster and more impactful social support that is specifically tailored to the unique characteristics of the mSI subgroup to which a given patient belongs.
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Affiliation(s)
- Jiasi Hao
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Natalia Tiles-Sar
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Tesfa Dejenie Habtewold
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Edith J Liemburg
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Richard Bruggeman
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Lisette van der Meer
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands.
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Hatzimanolis A, Tosato S, Ruggeri M, Cristofalo D, Mantonakis L, Xenaki LA, Dimitrakopoulos S, Selakovic M, Foteli S, Kosteletos I, Vlachos I, Soldatos RF, Nianiakas N, Ralli I, Kollias K, Ntigrintaki AA, Stefanatou P, Murray RM, Vassos E, Stefanis NC. Diminished social motivation in early psychosis is associated with polygenic liability for low vitamin D. Transl Psychiatry 2024; 14:36. [PMID: 38238289 PMCID: PMC10796745 DOI: 10.1038/s41398-024-02750-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Insufficiency of vitamin D levels often occur in individuals with schizophrenia and first-episode psychosis (FEP). However, it is unknown whether this represents a biological predisposition, or it is essentially driven by illness-related alterations in lifestyle habits. Lower vitamin D has also been associated with adverse neurodevelopmental outcomes and predominant negative psychotic symptoms. This study aimed to investigate the contribution of polygenic risk score for circulating 25-hydroxyvitamin D concentration (PRS-vitD) to symptom presentation among individuals with FEP enrolled in the Athens First-Episode Psychosis Research Study (AthensFEP n = 205) and the Psychosis Incident Cohort Outcome Study (PICOS n = 123). The severity of psychopathology was evaluated using the Positive and Negative Syndrome Scale at baseline and follow-up assessments (AthensFEP: 4-weeks follow-up, PICOS: 1-year follow-up). Premorbid intelligence and adjustment domains were also examined as proxy measures of neurodevelopmental deviations. An inverse association between PRS-vitD and severity of negative symptoms, in particular lack of social motivation, was detected in the AthensFEP at baseline (adjusted R2 = 0.04, p < 0.001) and follow-up (adjusted R2 = 0.03, p < 0.01). The above observation was independently validated in PICOS at follow-up (adjusted R2 = 0.06, p < 0.01). No evidence emerged for a relationship between PRS-vitD and premorbid measures of intelligence and adjustment, likely not supporting an impact of lower PRS-vitD on developmental trajectories related to psychotic illness. These findings suggest that polygenic vulnerability to reduced vitamin D impairs motivation and social interaction in individuals with FEP, thereby interventions that encourage outdoor activities and social engagement in this patient group might attenuate enduring negative symptoms.
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Affiliation(s)
- Alex Hatzimanolis
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece.
- Neurobiology Research Institute, Theodore-Theohari Cozzika Foundation, Athens, Greece.
| | - Sarah Tosato
- Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Mirella Ruggeri
- Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Doriana Cristofalo
- Section of Psychiatry, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Leonidas Mantonakis
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Lida-Alkisti Xenaki
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Stefanos Dimitrakopoulos
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Mirjana Selakovic
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Stefania Foteli
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Ioannis Kosteletos
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Ilias Vlachos
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Rigas-Filippos Soldatos
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Nikos Nianiakas
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Irene Ralli
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Konstantinos Kollias
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Angeliki-Aikaterini Ntigrintaki
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Pentagiotissa Stefanatou
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Institute for Health Research, Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College, London, UK
| | - Evangelos Vassos
- National Institute for Health Research, Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College, London, UK
- Department of Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nikos C Stefanis
- Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition University Hospital, Athens, Greece
- Neurobiology Research Institute, Theodore-Theohari Cozzika Foundation, Athens, Greece
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Vlachos II, Selakovic M, Ralli I, Hatzimanolis A, Xenaki LA, Dimitrakopoulos S, Soldatos RF, Foteli S, Nianiakas N, Kosteletos I, Stefanatou P, Ntigrintaki AA, Triantafyllou TF, Voulgaraki M, Ermiliou V, Mantonakis L, Kollias K, Stefanis NC. Role of Clinical Insight at First Month in Predicting Relapse at the Year in First Episode of Psychosis (FEP) Patients. J Clin Med 2023; 12:4261. [PMID: 37445295 DOI: 10.3390/jcm12134261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
INTRODUCTION Clinical insight constitutes a useful marker of the progress and outcome of the First Episode of Psychosis (FEP), and lack of insight has been associated with more severe psychopathology, treatment non-adherence, and rehospitalization/relapse. In this study, we aimed to further investigate the possible role of insight as a predictor of relapse, its relation to diagnosis, and other parameters of positive psychotic symptomatology (delusions, hallucinations, and suspiciousness). METHODS The Athens FEP study employed a prospective, longitudinal cohort design in which consecutive newly diagnosed patients with psychosis were interviewed and asked to voluntarily participate after completing informed consent. A total of 88/225 patients were examined at three different time points (baseline, month, and year). Their scores in the relevant items of the Positive and Negative Syndrome Scale (PANSS) were compared (G12 for insight, P1 for delusions, P3 for hallucinations, and P6 for suspiciousness), and they were further associated to diagnosis and the outcome at the end of the year (remission/relapse). RESULTS In total, 22/88 patients with relapse at the year had greater scores in G12 for both the month and the year, and this finding was corroborated after adjusting the statistical analysis for demographics, diagnosis, social environment, and depression via multiple logistic regression analysis. Moreover, delusions and suspiciousness were significantly higher in patients diagnosed with non-affective psychosis compared to those diagnosed with affective psychosis (p < 0.001) at the first month. CONCLUSIONS Lack of insight at the first month may serve as a predictor of relapse at the year.
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Affiliation(s)
- Ilias I Vlachos
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Mirjana Selakovic
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
- Department of Psychiatry, Sismanoglion General Hospital, 15126 Attica, Greece
| | - Irene Ralli
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Alexandros Hatzimanolis
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Lida-Alkisti Xenaki
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Stefanos Dimitrakopoulos
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
- Psychiatric Clinic, 414 Military Hospital of Athens, 15236 Palea Penteli, Greece
| | - Rigas-Filippos Soldatos
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Stefania Foteli
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Nikos Nianiakas
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Ioannis Kosteletos
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Pentagiotissa Stefanatou
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | | | | | - Marina Voulgaraki
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Vassiliki Ermiliou
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Leonidas Mantonakis
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Konstantinos Kollias
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
| | - Nikos C Stefanis
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece
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Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023; 13:75. [PMID: 36864017 PMCID: PMC9981732 DOI: 10.1038/s41398-023-02371-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
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Brandt L, Ritter K, Schneider-Thoma J, Siafis S, Montag C, Ayrilmaz H, Bermpohl F, Hasan A, Heinz A, Leucht S, Gutwinski S, Stuke H. Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis. Lancet Psychiatry 2023; 10:184-196. [PMID: 36804071 DOI: 10.1016/s2215-0366(23)00008-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 02/17/2023]
Abstract
BACKGROUND Predicting relapse for individuals with psychotic disorders is not well established, especially after discontinuation of antipsychotic treatment. We aimed to identify general prognostic factors of relapse for all participants (irrespective of treatment continuation or discontinuation) and specific predictors of relapse for treatment discontinuation, using machine learning. METHODS For this individual participant data analysis, we searched the Yale University Open Data Access Project's database for placebo-controlled, randomised antipsychotic discontinuation trials with participants with schizophrenia or schizoaffective disorder (aged ≥18 years). We included studies in which participants were treated with any antipsychotic study drug and randomly assigned to continue the same antipsychotic drug or to discontinue it and receive placebo. We assessed 36 prespecified baseline variables at randomisation to predict time to relapse, using univariate and multivariate proportional hazard regression models (including multivariate treatment group by variable interactions) with machine learning to categorise the variables as general prognostic factors of relapse, specific predictors of relapse, or both. FINDINGS We identified 414 trials, of which five trials with 700 participants (304 [43%] women and 396 [57%] men) were eligible for the continuation group and 692 participants (292 [42%] women and 400 [58%] men) were eligible for the discontinuation group (median age 37 [IQR 28-47] years for continuation group and 38 [28-47] years for discontinuation group). Out of the 36 baseline variables, general prognostic factors of increased risk of relapse for all participants were drug-positive urine; paranoid, disorganised, and undifferentiated types of schizophrenia (lower risk for schizoaffective disorder); psychiatric and neurological adverse events; higher severity of akathisia (ie, difficulty or inability to sit still); antipsychotic discontinuation; lower social performance; younger age; lower glomerular filtration rate; benzodiazepine comedication (lower risk for anti-epileptic comedication). Out of the 36 baseline variables, predictors of increased risk specifically after antipsychotic discontinuation were increased prolactin concentration, higher number of hospitalisations, and smoking. Both prognostic factors and predictors with increased risk after discontinuation were oral antipsychotic treatment (lower risk for long-acting injectables), higher last dosage of the antipsychotic study drug, shorter duration of antipsychotic treatment, and higher score on the Clinical Global Impression (CGI) severity scale The predictive performance (concordance index) for participants who were not used to train the model was 0·707 (chance level is 0·5). INTERPRETATION Routinely available general prognostic factors of psychotic relapse and predictors specific for treatment discontinuation could be used to support personalised treatment. Abrupt discontinuation of higher dosages of oral antipsychotics, especially for individuals with recurring hospitalisations, higher scores on the CGI severity scale, and increased prolactin concentrations, should be avoided to reduce the risk of relapse. FUNDING German Research Foundation and Berlin Institute of Health.
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Affiliation(s)
- Lasse Brandt
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
| | - Kerstin Ritter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Bernstein Center of Computational Neuroscience Berlin, Berlin, Germany
| | - Johannes Schneider-Thoma
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University Munich, Munich, Germany
| | - Spyridon Siafis
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University Munich, Munich, Germany
| | - Christiane Montag
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Hakan Ayrilmaz
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Berlin School of Mind and Brain, Berlin, Germany
| | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Augsburg, Medical Faculty, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Bernstein Center of Computational Neuroscience Berlin, Berlin, Germany; Berlin School of Mind and Brain, Berlin, Germany
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University Munich, Munich, Germany
| | - Stefan Gutwinski
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Heiner Stuke
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Berlin Institute of Health, Berlin, Germany
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