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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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Krakowski K, Oliver D, Arribas M, Stahl D, Fusar-Poli P. Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study. Biol Psychiatry 2024; 96:604-614. [PMID: 38852896 DOI: 10.1016/j.biopsych.2024.05.022] [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: 11/24/2023] [Revised: 04/05/2024] [Accepted: 05/10/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.
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Affiliation(s)
- Kamil Krakowski
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health National Health Service Foundation Trust, Oxford, United Kingdom
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Paolo Fusar-Poli
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; OASIS Service, South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany.
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3
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Sprüngli-Toffel E, Studerus E, Curtis L, Conchon C, Alameda L, Bailey B, Caron C, Haase C, Gros J, Herbrecht E, Huber CG, Riecher-Rössler A, Conus P, Solida A, Armando M, Kapsaridi A, Ducommun MM, Klauser P, Plessen KJ, Urben S, Edan A, Nanzer N, Navarro AL, Schneider M, Genoud D, Michel C, Kindler J, Kaess M, Oliver D, Fusar-Poli P, Borgwardt S, Andreou C. Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders. SPANISH JOURNAL OF PSYCHIATRY AND MENTAL HEALTH 2024:S2950-2853(24)00052-8. [PMID: 39303874 DOI: 10.1016/j.sjpmh.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/30/2024] [Accepted: 09/12/2024] [Indexed: 09/22/2024]
Abstract
INTRODUCTION Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population. METHOD The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis. RESULTS Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years. CONCLUSION Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.
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Affiliation(s)
- Elodie Sprüngli-Toffel
- General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Geneva, Geneva, Switzerland.
| | - Erich Studerus
- Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
| | - Logos Curtis
- Department of Psychiatry, University of Geneva, Geneva, Switzerland; Department of Adult Psychiatry, Department of Psychiatry, Geneva University Hospital, University of Geneva, Geneva, Switzerland
| | - Caroline Conchon
- General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland
| | - Luis Alameda
- General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland; King's College of London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom; Centro Investigacion Biomedica en Red de Salud Mental (CIBERSAM), Instituto de Biomedicina de Sevilla (IBIS), Hospital Universitario Virgen del Rocio, Departamento de Psiquiatria, Universidad de Sevilla, Sevilla, Spain
| | - Barbara Bailey
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Camille Caron
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Carmina Haase
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Julia Gros
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Evelyn Herbrecht
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Christian G Huber
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | | | - Philippe Conus
- General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland
| | - Alessandra Solida
- General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland; Center of Psychiatry of Neuchâtel (CNP), Neuchâtel, Switzerland
| | - Marco Armando
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Afroditi Kapsaridi
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Mathieu Mercapide Ducommun
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Paul Klauser
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; Center for Psychiatric Neuroscience, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Kerstin Jessica Plessen
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Sébastien Urben
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anne Edan
- Child and Adolescent Psychiatric Service, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | - Nathalie Nanzer
- Child and Adolescent Psychiatric Service, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | | | - Maude Schneider
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Davina Genoud
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Paolo Fusar-Poli
- King's College of London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom; OASIS Service, South London and the Maudsley NHS Foundation Trust, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, Translational Psychiatry Unit, University of Lübeck, Lübeck, Germany
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, Translational Psychiatry Unit, University of Lübeck, Lübeck, Germany
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Arribas M, Oliver D, Patel R, Kornblum D, Shetty H, Damiani S, Krakowski K, Provenzani U, Stahl D, Koutsouleris N, McGuire P, Fusar-Poli P. A transdiagnostic prodrome for severe mental disorders: an electronic health record study. Mol Psychiatry 2024:10.1038/s41380-024-02533-5. [PMID: 38710907 DOI: 10.1038/s41380-024-02533-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 05/08/2024]
Abstract
Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.
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Affiliation(s)
- Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, OX3 7JX, UK
- OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, OX3 7JX, UK
| | - Rashmi Patel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | | | - Hitesh Shetty
- NIHR Maudsley Biomedical Research Centre, London, UK
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Kamil Krakowski
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Umberto Provenzani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Daniel Stahl
- NIHR Maudsley Biomedical Research Centre, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 8AF, UK
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, OX3 7JX, UK
- OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, OX3 7JX, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Outreach and Support in South-London (OASIS) Service, South London and Maudsley (SLaM) NHS Foundation Trust, London, SE11 5DL, UK
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Smucny J, Davidson I, Carter CS. Are We There Yet? Predicting Conversion to Psychosis Using Machine Learning. Am J Psychiatry 2023; 180:836-840. [PMID: 37789742 PMCID: PMC11200311 DOI: 10.1176/appi.ajp.20220973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Affiliation(s)
- Jason Smucny
- Department of Psychiatry, University of California, Davis (Smucny, Carter); Department of Computer Science, University of California, Davis (Davidson)
| | - Ian Davidson
- Department of Psychiatry, University of California, Davis (Smucny, Carter); Department of Computer Science, University of California, Davis (Davidson)
| | - Cameron S Carter
- Department of Psychiatry, University of California, Davis (Smucny, Carter); Department of Computer Science, University of California, Davis (Davidson)
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Zhao M, He Y, Tang Q, Wang N, Zheng H, Feng Z. Risk factors and prediction model for mental health in Chinese soldiers. Front Psychiatry 2023; 14:1125411. [PMID: 37215678 PMCID: PMC10196266 DOI: 10.3389/fpsyt.2023.1125411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/27/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. Methods This cross-sectional study was performed on soldiers under direct command from Gansu, Sichuan, and Chongqing in China, and the soldiers were selected by cluster convenient sampling from 16 October 2018 to 10 December 2018. The Symptom Checklist-90 (SCL-90) and three questionnaires (Military Mental Health Status Questionnaire, Military Mental Health Ability Questionnaire, and Mental Quality Questionnaire for Army Men) were administered, including demographics, military careers, and 18 factors. Results Of 1,430 Chinese soldiers, 162 soldiers presented mental disorders, with a prevalence of 11.33%. A total of five risk factors were identified, including serving place (Sichuan vs. Gansu: OR, 1.846, 95% CI: 1.028-3.315, P = 0.038; Chongqing vs. Gansu: OR, 3.129, 95% CI, 1.669-5.869, P = 0.003), psychosis (OR, 1.491, 95% CI, 1.152-1.928, P = 0.002), depression (OR, 1.482, 95% CI, 1.349-1.629, P < 0.001), sleep problems (OR, 1.235, 95% CI, 1.162-1.311, P < 0.001), and frustration (OR, 1.050, 95% CI, 1.015-1.087, P = 0.005). The area under the ROC curve by combining these factors was 0.930 (95% CI: 0.907-0.952) for predicting mental disorders in Chinese soldiers. Conclusion The findings of this study demonstrate that mental disorders and onset in Chinese soldiers can be predicted on the basis of these three questionnaires, and the predictive value of the combined model was high.
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Affiliation(s)
- Mengxue Zhao
- Department of Military Psychology, Faculty of Medical Psychology, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ying He
- Department of Psychiatry, Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Quan Tang
- Medical Psychology Department, No. 984 Hospital of PLA, Beijing, China
| | - Ni Wang
- General Hospital of Xinjiang Military Region, Wulumuqi, China
| | - Haoxin Zheng
- The 33rd Company of the 11th Battalion, College of Command and Control Engineering, Army Engineering University of PLA, Nanjing, China
| | - Zhengzhi Feng
- Faculty of Medical Psychology, Army Medical University (Third Military Medical University), Chongqing, China
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7
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Oliver D, Arribas M, Radua J, Salazar de Pablo G, De Micheli A, Spada G, Mensi MM, Kotlicka-Antczak M, Borgatti R, Solmi M, Shin JI, Woods SW, Addington J, McGuire P, Fusar-Poli P. Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis. Mol Psychiatry 2022; 27:3670-3678. [PMID: 35665763 PMCID: PMC9708585 DOI: 10.1038/s41380-022-01611-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/08/2023]
Abstract
Accurate prognostication of individuals at clinical high-risk for psychosis (CHR-P) is an essential initial step for effective primary indicated prevention. We aimed to summarise the prognostic accuracy and clinical utility of CHR-P assessments for primary indicated psychosis prevention. Web of Knowledge databases were searched until 1st January 2022 for longitudinal studies following-up individuals undergoing a psychometric or diagnostic CHR-P assessment, reporting transition to psychotic disorders in both those who meet CHR-P criteria (CHR-P + ) or not (CHR-P-). Prognostic accuracy meta-analysis was conducted following relevant guidelines. Primary outcome was prognostic accuracy, indexed by area-under-the-curve (AUC), sensitivity and specificity, estimated by the number of true positives, false positives, false negatives and true negatives at the longest available follow-up time. Clinical utility analyses included: likelihood ratios, Fagan's nomogram, and population-level preventive capacity (Population Attributable Fraction, PAF). A total of 22 studies (n = 4 966, 47.5% female, age range 12-40) were included. There were not enough meta-analysable studies on CHR-P diagnostic criteria (DSM-5 Attenuated Psychosis Syndrome) or non-clinical samples. Prognostic accuracy of CHR-P psychometric instruments in clinical samples (individuals referred to CHR-P services or diagnosed with 22q.11.2 deletion syndrome) was excellent: AUC = 0.85 (95% CI: 0.81-0.88) at a mean follow-up time of 34 months. This result was driven by outstanding sensitivity (0.93, 95% CI: 0.87-0.96) and poor specificity (0.58, 95% CI: 0.50-0.66). Being CHR-P + was associated with a small likelihood ratio LR + (2.17, 95% CI: 1.81-2.60) for developing psychosis. Being CHR-P- was associated with a large LR- (0.11, 95%CI: 0.06-0.21) for developing psychosis. Fagan's nomogram indicated a low positive (0.0017%) and negative (0.0001%) post-test risk in non-clinical general population samples. The PAF of the CHR-P state is 10.9% (95% CI: 4.1-25.5%). These findings consolidate the use of psychometric instruments for CHR-P in clinical samples for primary indicated prevention of psychosis. Future research should improve the ability to rule in psychosis risk.
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Affiliation(s)
- Dominic Oliver
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Joaquim Radua
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute, Stockholm, Sweden
| | - Gonzalo Salazar de Pablo
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Child and Adolescent Mental Health Services, South London & Maudsley NHS Trust, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrea De Micheli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, UK
| | - Giulia Spada
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Martina Maria Mensi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Childhood and Adolescent Neuropsychiatry Unit, Pavia, Italy
| | - Magdalena Kotlicka-Antczak
- Early Psychosis Diagnosis and Treatment Lab, Department of Affective and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Renato Borgatti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Childhood and Adolescent Neuropsychiatry Unit, Pavia, Italy
| | - Marco Solmi
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), University of Ottawa, Ottawa, ON, Canada
| | - Jae Il Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Philip McGuire
- OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, UK
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8
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Suhas S, Mehta UM. A redux of schizophrenia research in 2021. Schizophr Res 2022; 243:458-461. [PMID: 35300898 PMCID: PMC8919807 DOI: 10.1016/j.schres.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/06/2022] [Indexed: 11/19/2022]
Affiliation(s)
- Satish Suhas
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore 560029, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore 560029, India.
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9
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Damiani S, Rutigliano G, Fazia T, Merlino S, Berzuini C, Bernardinelli L, Politi P, Fusar-Poli P. Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study. Schizophr Bull 2021; 47:1695-1705. [PMID: 34172999 PMCID: PMC8530399 DOI: 10.1093/schbul/sbab070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Acute and transient psychotic disorders (ATPDs) include short-lived psychotic episodes with a high probability of developing psychotic recurrences. Clinical care for ATPD is currently limited by the inability to predict outcomes. Real-world electronic health record (EHR)-based retrospective cohort study STROBE/RECORD compliant included all individuals accessing the South London and Maudsley NHS Trust between 2006 and 2017 and receiving a first diagnosis of ATPD (F23, ICD-10). After imputing missing data, stepwise and LASSO Cox regression methods employing a priori predictors (n = 23) were compared to develop and internally validate an individualized risk prediction model to forecast the risk of psychotic recurrences following TRIPOD guidelines. The primary outcome was prognostic accuracy (area under the curve [AUC]). 3018 ATPD individuals were included (average age = 33.75 years, 52.7% females). Over follow-up (average 1042 ± 1011 days, up to 8 years) there were 1160 psychotic recurrences (events). Stepwise (n = 12 predictors) and LASSO (n = 17 predictors) regression methods yielded comparable prognostic accuracy, with an events per variable ratio >100 for both models. Both models showed an internally validated adequate prognostic accuracy from 4 years follow-up (AUC 0.70 for both models) and good calibration. A refined model was adapted in view of the new ICD-11 criteria on 307 subjects with polymorphic ATPD, showing fair prognostic accuracy at 4 years (AUC: stepwise 0.68; LASSO 0.70). This study presents the first clinically based prediction model internally validated to adequately predict long-term psychotic recurrence in individuals with ATPD. The model can be automatable in EHRs, supporting further external validations and refinements to improve its prognostic accuracy.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Grazia Rutigliano
- Department of Pathology, University of Pisa, Pisa, Italy
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Teresa Fazia
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Sergio Merlino
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- Institute of Psychiatry Psychology and Neuroscience, London, UK
| | - Carlo Berzuini
- Center for Biostatistics, The University of Manchester, Manchester, UK
| | - Luisa Bernardinelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
- OASIS Service, South London and Maudsley NHS Foundation Trust, London, UK
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10
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Fusar‐Poli P, Correll CU, Arango C, Berk M, Patel V, Ioannidis JP. Preventive psychiatry: a blueprint for improving the mental health of young people. World Psychiatry 2021; 20:200-221. [PMID: 34002494 PMCID: PMC8129854 DOI: 10.1002/wps.20869] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.
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Affiliation(s)
- Paolo Fusar‐Poli
- Early Psychosis: Interventions and Clinical‐detection (EPIC) Lab, Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK,OASIS Service, South London and Maudsley NHS Foundation TrustLondonUK,Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - Christoph U. Correll
- Department of PsychiatryZucker Hillside Hospital, Northwell HealthGlen OaksNYUSA,Department of Psychiatry and Molecular MedicineZucker School of Medicine at Hofstra/NorthwellHempsteadNYUSA,Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNYUSA,Department of Child and Adolescent PsychiatryCharité Universitätsmedizin BerlinBerlinGermany
| | - Celso Arango
- Department of Child and Adolescent PsychiatryInstitute of Psychiatry and Mental Health, Hospital General Universitario Gregorio MarañónMadridSpain,Health Research Institute (IiGSM), School of MedicineUniversidad Complutense de MadridMadridSpain,Biomedical Research Center for Mental Health (CIBERSAM)MadridSpain
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin UniversityBarwon HealthGeelongVICAustralia,Department of PsychiatryUniversity of MelbourneMelbourneVICAustralia,Orygen Youth HealthUniversity of MelbourneMelbourneVICAustralia,Florey Institute for Neuroscience and Mental HealthUniversity of MelbourneMelbourneVICAustralia
| | - Vikram Patel
- Department of Global Health and Social MedicineHarvard University T.H. Chan School of Public HealthBostonMAUSA,Department of Global Health and PopulationHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - John P.A. Ioannidis
- Stanford Prevention Research Center, Department of MedicineStanford UniversityStanfordCAUSA,Department of Biomedical Data ScienceStanford UniversityStanfordCAUSA,Department of Epidemiology and Population HealthStanford UniversityStanfordCAUSA
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