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Zhang TT, Buckman JEJ, Suh JW, Stott J, Singh S, Jena R, Naqvi SA, Pilling S, Cape J, Saunders R. Identifying trajectories of change in sleep disturbance during psychological treatment for depression. J Affect Disord 2024; 365:659-668. [PMID: 39142574 DOI: 10.1016/j.jad.2024.08.027] [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: 01/12/2024] [Revised: 07/02/2024] [Accepted: 08/09/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND Sleep disturbance may impact response to psychological treatment for depression. Understanding how sleep disturbance changes during the course of psychological treatment, and identifying the risk factors for sleep disturbance response may inform clinical decision-making. METHOD This analysis included 18,915 patients receiving high-intensity psychological therapy for depression from one of eight London-based Improving Access to Psychological Therapies (IAPT) services between 2011 and 2020. Distinct trajectories of change in sleep disturbance were identified using growth mixture modelling. The study also investigated associations between identified trajectory classes, pre-treatment patient characteristics, and eventual treatment outcomes from combined PHQ-9 and GAD-7 metrics used by the services. RESULTS Six distinct trajectories of sleep disturbance were identified: two demonstrated improvement, while one showed initial deterioration and the other three groups displayed only limited change in sleep disturbance, each with varying baseline sleep disturbance. Associations with trajectory class membership were found based on: gender, ethnicity, employment status, psychotropic medication use, long-term health condition status, severity of depressive symptoms, and functional impairment. Groups that showed improvement in sleep had the best eventual outcomes from depression treatment, followed by groups that consistently slept well. LIMITATION Single item on sleep disturbance used, no data on treatment adherence. CONCLUSIONS These findings reveal heterogeneity in the course of sleep disturbance during psychological treatment for depression. Closer monitoring of changes in sleep disturbance during treatment might inform treatment planning. This includes decisions about when to incorporate sleep management interventions, and whether to change or augment therapy with interventions to reduce sleep disturbance.
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Affiliation(s)
- T T Zhang
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom
| | - J E J Buckman
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom; iCope -Camden and Islington Psychological Therapies Services - Camden & Islington NHS Foundation Trust, London, United Kingdom
| | - J W Suh
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom
| | - J Stott
- Adapt Lab, Research Department of Clinical Educational and Health Psychology, UCL, London, United Kingdom
| | - S Singh
- Waltham Forest Talking Therapies - North East London NHS Foundation Trust, London, United Kingdom
| | - R Jena
- Waltham Forest Talking Therapies - North East London NHS Foundation Trust, London, United Kingdom
| | - S A Naqvi
- Barking & Dagenham and Havering IAPT services - North East London NHS Foundation Trust, London, United Kingdom
| | - S Pilling
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom; Camden and Islington NHS Foundation Trust, London, United Kingdom
| | - J Cape
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom
| | - R Saunders
- CORE Data Lab, Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational, and Health Psychology, UCL, London, United Kingdom.
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Bonnet U. Ten years of maintenance treatment of severe melancholic depression in an adult woman including discontinuation experiences. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2024. [PMID: 38901434 DOI: 10.1055/a-2332-6107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
BACKGROUND There are only few publications on long-term treatments for major depressive disorder (MDD) lasting 5 years or longer. Most clinical controlled trials lasted no longer than 2 years and some recent studies suggested an advantage of cognitive behavioral therapy (CBT) over antidepressants in relapse prevention of MDD. METHODS Exclusively outpatient "real world" treatment of severe melancholia, prospectively documented over 10 years with different serial treatment strategies, discontinuation phenomena and complications. METHODS Compared to CBT, agomelatine, mirtazapine, bupropion and high-dose milnacipran, high-dose venlafaxine (extended-release form, XR) was effective, even sustainably. Asymptomatic premature ventricular contractions (PVCs) were found at the beginning of the treatment of the MDD, which initially led to the discontinuation of high-dose venlafaxine (300 mg daily). Even the various treatment strategies mentioned above were unable to compensate for or prevent the subsequent severe deterioration in MDD (2 rebounds, 1 recurrence). Only the renewed use of high-dose venlafaxine was successful. PVC no longer occurred and the treatment was also well tolerated over the years, with venlafaxine serum levels at times exceeding 5 times the recommended upper therapeutic reference level (known bupropion-venlafaxine interaction, otherwise 2.5 to 3-fold increase with high-dose venlafaxine alone). During dose reduction or after gradual discontinuation of high-dose venlafaxine, rather mild withdrawal symptoms occurred, but as described above, also two severe rebounds and one severe recurrence happened. DISCUSSION This long-term observation supports critical reflections on the discontinuation of successful long-term treatment with antidepressants in severe MDD, even if it should be under "the protection" of CBT. The PVC seemed to be more related to the duration of the severe major depressive episode than to the venlafaxine treatment itself. A particular prospective observation of this longitudinal case study is that relapses (in the sense of rebounds) during or after previous venlafaxine tapering seemed to herald the recurrence after complete recovery. Remarkably, neither relapses nor recurrence could be prevented by CBT. CONCLUSION In this case, high-dose venlafaxine has a particular relapse-preventive (and "recurrence-preventive") effect with good long-term tolerability.
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Affiliation(s)
- Udo Bonnet
- Department of Mental Health, Evangelisches Krankenhaus Castrop-Rauxel, Academic Teaching Hospital of the University of Duisburg-Essen, D-44577 Castrop-Rauxel, Germany
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Duisburg-Essen, D-45147 Essen, Germany
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3
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Prieto-Vila M, González-Blanch C, Estupiñá Puig FJ, Buckman JE, Saunders R, Muñoz-Navarro R, Moriana JA, Rodríguez-Ruiz P, Barrio-Martínez S, Carpallo-González M, Cano-Vindel A. Long-term depressive symptom trajectories and related baseline characteristics in primary care patients: Analysis of the PsicAP clinical trial. Eur Psychiatry 2024; 67:e32. [PMID: 38532731 PMCID: PMC11059253 DOI: 10.1192/j.eurpsy.2024.27] [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: 12/07/2023] [Revised: 03/13/2024] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND There is heterogeneity in the long-term trajectories of depressive symptoms among patients. To date, there has been little effort to inform the long-term trajectory of symptom change and the factors associated with different trajectories. Such knowledge is key to treatment decision-making in primary care, where depression is a common reason for consultation. We aimed to identify distinct long-term trajectories of depressive symptoms and explore pre-treatment characteristics associated with them. METHODS A total of 483 patients from the PsicAP clinical trial were included. Growth mixture modeling was used to identify long-term distinct trajectories of depressive symptoms, and multinomial logistic regression models to explore associations between pre-treatment characteristics and trajectories. RESULTS Four trajectories were identified that best explained the observed response patterns: "recovery" (64.18%), "late recovery" (10.15%), "relapse" (13.67%), and "chronicity" (12%). There was a higher likelihood of following the recovery trajectory for patients who had received psychological treatment in addition to the treatment as usual. Chronicity was associated with higher depressive severity, comorbidity (generalized anxiety, panic, and somatic symptoms), taking antidepressants, higher emotional suppression, lower levels on life quality, and being older. Relapse was associated with higher depressive severity, somatic symptoms, and having basic education, and late recovery was associated with higher depressive severity, generalized anxiety symptoms, greater disability, and rumination. CONCLUSIONS There were different trajectories of depressive course and related prognostic factors among the patients. However, further research is needed before these findings can significantly influence care decisions.
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Affiliation(s)
- Maider Prieto-Vila
- Department of Experimental Psychology, Cognitive Processes and Logopedics, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
| | - César González-Blanch
- Mental Health Centre, University Hospital “Marqués de Valdecilla” – IDIVAL, Santander, Spain
| | - Francisco J. Estupiñá Puig
- Department of Personality, Assessment and Clinical Psychology, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
| | - Joshua E.J. Buckman
- Research Department of Clinical, Centre for Outcomes and Research Effectiveness, Educational and Health Psychology, UCL, London, UK
- iCope – Camden and Islington Psychological Therapies Services, Camden & Islington NHS Foundation Trust, London, UK
| | - Rob Saunders
- Research Department of Clinical, Centre for Outcomes and Research Effectiveness, Educational and Health Psychology, UCL, London, UK
| | - Roger Muñoz-Navarro
- Department of Personality, Assessment and Psychological Treatments, Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Juan A. Moriana
- Department of Psychology, University of Cordoba, Cordoba, Spain
| | | | - Sara Barrio-Martínez
- Department of Experimental Psychology, Cognitive Processes and Logopedics, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
- Mental Health Centre, University Hospital “Marqués de Valdecilla” – IDIVAL, Santander, Spain
| | - María Carpallo-González
- Department of Personality, Assessment and Psychological Treatments, Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Antonio Cano-Vindel
- Department of Experimental Psychology, Cognitive Processes and Logopedics, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
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Gliske K, Berry KR, Ballard J, Schmidt C, Kroll E, Kohlmeier J, Killian M, Fenkel C. Predicting Youth and Young Adult Treatment Engagement in a Transdiagnostic Remote Intensive Outpatient Program: Latent Profile Analysis. JMIR Form Res 2023; 7:e47917. [PMID: 37676700 PMCID: PMC10514771 DOI: 10.2196/47917] [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: 04/05/2023] [Revised: 06/20/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The youth mental health crisis in the United States continues to worsen, and research has shown poor mental health treatment engagement. Despite the need for personalized engagement strategies, there is a lack of research involving youth. Due to complex youth developmental milestones, there is a need to better understand clinical presentation and factors associated with treatment engagement to effectively identify and tailor beneficial treatments. OBJECTIVE This quality improvement investigation sought to identify subgroups of clients attending a remote intensive outpatient program (IOP) based on clinical acuity data at intake, to determine the factors associated with engagement outcomes for clients who present in complex developmental periods and with cooccurring conditions. The identification of these subgroups was used to inform programmatic decisions within this remote IOP system. METHODS Data were collected as part of ongoing quality improvement initiatives at a remote IOP for youth and young adults. Participants included clients (N=2924) discharged between July 2021 and February 2023. A latent profile analysis was conducted using 5 indicators of clinical acuity at treatment entry, and the resulting profiles were assessed for associations with demographic factors and treatment engagement outcomes. RESULTS Among the 2924 participants, 4 profiles of clinical acuity were identified: a low-acuity profile (n=943, 32.25%), characterized by minimal anxiety, depression, and self-harm, and 3 high-acuity profiles defined by moderately severe depression and anxiety but differentiated by rates of self-harm (high acuity+low self-harm: n=1452, 49.66%; high acuity+moderate self-harm: n=203, 6.94%; high acuity+high self-harm: n=326, 11.15%). Age, gender, transgender identity, and sexual orientation were significantly associated with profile membership. Clients identified as sexually and gender-marginalized populations were more likely to be classified into high-acuity profiles than into the low-acuity profile (eg, for clients who identified as transgender, high acuity+low self-harm: odds ratio [OR] 2.07, 95% CI 1.35-3.18; P<.001; high acuity+moderate self-harm: OR 2.85, 95% CI 1.66-4.90; P<.001; high acuity+high self-harm: OR 3.67, 95% CI 2.45-5.51; P<.001). Race was unrelated to the profile membership. Profile membership was significantly associated with treatment engagement: youth and young adults in the low-acuity and high-acuity+low-self-harm profiles attended an average of 4 fewer treatment sessions compared with youth in the high-acuity+moderate-self-harm and high-acuity+high-self-harm profiles (ꭓ23=27.6, P<.001). Individuals in the high-acuity+low-self-harm profile completed treatment at a significantly lower rate relative to the other 2 high-acuity profiles (ꭓ23=13.4, P=.004). Finally, those in the high-acuity+high-self-harm profile were significantly less likely to disengage early relative to youth in all other profiles (ꭓ23=71.12, P<.001). CONCLUSIONS This investigation represents a novel application for identifying subgroups of adolescents and young adults based on clinical acuity data at intake to identify patterns in treatment engagement outcomes. Identifying subgroups that differentially engage in treatment is a critical first step toward targeting engagement strategies for complex populations.
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Affiliation(s)
- Kate Gliske
- Charlie Health Inc, Bozeman, MT, United States
| | | | - Jaime Ballard
- Center For Applied Research and Educational Improvement, University of Minnesota, St. Paul, MN, United States
| | | | | | | | - Michael Killian
- College of Social Work, Florida State University, Tallahassee, FL, United States
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Ziobrowski HN, Cui R, Ross EL, Liu H, Puac-Polanco V, Turner B, Leung LB, Bossarte RM, Bryant C, Pigeon WR, Oslin DW, Post EP, Zaslavsky AM, Zubizarreta JR, Nierenberg AA, Luedtke A, Kennedy CJ, Kessler RC. Development of a model to predict psychotherapy response for depression among Veterans. Psychol Med 2023; 53:3591-3600. [PMID: 35144713 PMCID: PMC9365879 DOI: 10.1017/s0033291722000228] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
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Affiliation(s)
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | | | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Andrew A. Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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6
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Carr CE, Millard E, Dilgul M, Bent C, Wetherick D, French J, Priebe S. Group music therapy with songwriting for adult patients with long-term depression (SYNCHRONY study): a feasibility and acceptability study of the intervention and parallel randomised controlled trial design with wait-list control and nested process evaluation. Pilot Feasibility Stud 2023; 9:75. [PMID: 37147699 PMCID: PMC10161457 DOI: 10.1186/s40814-023-01285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 03/30/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND Despite effective treatments, one fifth of patients develop chronic depression. Music therapy may offer a different approach. This study aimed to assess feasibility and acceptability of a music therapy intervention and trial methodology. METHODS A parallel two-arm randomised controlled trial with wait-list control, mixed feasibility/acceptability measures and nested process evaluation. Adults with long-term depression (symptom duration > 1 year) were recruited from community mental health services and computer randomised to 42 sessions of group music therapy with songwriting three times per week or wait-list control. Depression, social functioning, distress, quality of life, satisfaction and service use were assessed by blinded researchers at enrolment, 1 week and 3 and 6 months post-therapy. Outcomes were analysed descriptively, controlling for baseline covariates. Recruitment (number eligible, participation and retention rates) and intervention (fidelity, adherence) feasibility were assessed using pre-defined stop-go criteria. Attendance, adverse events, mood, relationship satisfaction and semi-structured interviews were analysed in a nested process evaluation. RESULTS Recruitment processes were feasible with 421 eligible, 12.7% participation and 60% (18/30) retention. Thirty participants were randomised to intervention (N = 20) and control (N = 10). Session attendance was low (mean 10.5) with four withdrawals. Music therapist adherence was good but changes to session frequency were suggested. Outcomes were available for 10/20 treatment and 9/10 wait-list participants. Depression increased in both arms post-therapy. Treatment depression scores fell below baseline 3 and 6 months post-therapy indicating improvement. Wait-list depression scores increased from baseline 3 and 6 months post-therapy. At 3 months, the treatment arm improved from baseline on all measures except satisfaction and functioning. At 6 months, quality of life, distress and functioning improved with reduction in health service contacts. High-attending participants improved more than low-attending. Seven adverse events (one serious) were reported. LIMITATIONS As this was a feasibility study, clinical outcomes should be interpreted cautiously. CONCLUSION A randomised controlled trial of group music therapy using songwriting is feasible with inclusion criteria and session frequency modifications, but further intervention development is required. TRIAL REGISTRATION ISRCTN18164037 on 26.09.2016.
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Affiliation(s)
- Catherine Elizabeth Carr
- Unit for Social and Community Psychiatry, WHO Collaborating Centre for Mental Health Services Development, Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, Newham Centre for Mental Health, Glen Road, London, E13 8SP, UK.
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK.
| | - Emma Millard
- Unit for Social and Community Psychiatry, WHO Collaborating Centre for Mental Health Services Development, Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, Newham Centre for Mental Health, Glen Road, London, E13 8SP, UK
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
| | - Merve Dilgul
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
| | - Cornelia Bent
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
| | - Donald Wetherick
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
| | - Jennifer French
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
| | - Stefan Priebe
- Unit for Social and Community Psychiatry, WHO Collaborating Centre for Mental Health Services Development, Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, Newham Centre for Mental Health, Glen Road, London, E13 8SP, UK
- East London NHS Foundation Trust, Trust Headquarters, Robert Dolan House, 9 Alie Street, London, E1 8DE, UK
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Xu Z, Vekaria V, Wang F, Cukor J, Su C, Adekkanattu P, Brandt P, Jiang G, Kiefer RC, Luo Y, Rasmussen LV, Xu J, Xiao Y, Alexopoulos G, Pathak J. Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records. PSYCHIATRIC RESEARCH AND CLINICAL PRACTICE 2023; 5:118-125. [PMID: 38077277 PMCID: PMC10698704 DOI: 10.1176/appi.prcp.20220015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/28/2023] Open
Abstract
Objective To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. Conclusions The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.
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Affiliation(s)
| | | | - Fei Wang
- Weill Cornell MedicineNew YorkNew YorkUSA
| | | | - Chang Su
- Temple UniversityPhiladelphiaPennsylvaniaUSA
| | | | | | | | | | - Yuan Luo
- Northwestern UniversityChicagoIllinoisUSA
| | | | - Jie Xu
- University of FloridaGainesvilleFloridaUSA
| | - Yunyu Xiao
- Weill Cornell MedicineNew YorkNew YorkUSA
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Stålner O, Nordin S, Madison G. Six-year prognosis of anxiety and depression caseness and their comorbidity in a prospective population-based adult sample. BMC Public Health 2022; 22:1554. [PMID: 35971092 PMCID: PMC9380370 DOI: 10.1186/s12889-022-13966-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Anxiety and depression are amongst the most prevalent mental health problems. Their pattern of comorbidity may inform about their etiology and effective treatment, but such research is sparse. Here, we document long-term prognosis of affective caseness (high probability of being a clinical case) of anxiety and depression, their comorbidity, and a no-caseness condition at three time-points across six years, and identify the most common prognoses of these four conditions. METHODS Longitudinal population-based data were collected from 1,837 participants in 2010, 2013 and 2016. Based on the Hospital Anxiety and Depression Scale they formed the four groups of anxiety, depression and comorbidity caseness, and no caseness at baseline. RESULTS The three-year associations show that it was most common to recover when being an anxiety, depression or comorbidity caseness (36.8 - 59.4%), and when not being a caseness to remain so (89.2%). It was also rather common to remain in the same caseness condition after three years (18.7 - 39.1%). In comorbidity it was more likely to recover from depression (21.1%) than from anxiety (5.4%), and being no caseness it was more likely to develop anxiety (5.9%) than depression (1.7%). The most common six-year prognoses were recovering from the affective caseness conditions at 3-year follow-up (YFU), and remain recovered at 6-YFU, and as no caseness to remain so across the six years. The second most common prognoses in the affective conditions were to remain as caseness at both 3-YFU and 6-YFU, and in no caseness to remain so at 3-YFU, but develop anxiety at 6-YFU. CONCLUSIONS The results suggest that only 37 - 60% of individuals in the general population with high probability of being a clinical case with anxiety, depression, and their comorbidity will recover within a three-year period, and that it is rather common to remain with these affective conditions after 6 years. These poor prognoses, for comorbidity in particular, highlight the need for intensified alertness of their prevalence and enabling treatment in the general population.
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Affiliation(s)
- Olivia Stålner
- Department of Psychology, Umeå University, 90187, Umeå, Sweden
| | - Steven Nordin
- Department of Psychology, Umeå University, 90187, Umeå, Sweden.
| | - Guy Madison
- Department of Psychology, Umeå University, 90187, Umeå, Sweden
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9
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Psychological interventions to prevent relapse in anxiety and depression: A systematic review and meta-analysis. PLoS One 2022; 17:e0272200. [PMID: 35960783 PMCID: PMC9374222 DOI: 10.1371/journal.pone.0272200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 07/14/2022] [Indexed: 11/19/2022] Open
Abstract
Objectives
The aim of this review is to establish the effectiveness of psychological relapse prevention interventions, as stand-alone interventions and in combination with maintenance antidepressant treatment (M-ADM) or antidepressant medication (ADM) discontinuation for patients with remitted anxiety disorders or major depressive disorders (MDD).
Methods
A systematic review and a meta-analysis were conducted. A literature search was conducted in PubMed, PsycINFO and Embase for randomised controlled trials (RCTs) comparing psychological relapse prevention interventions to treatment as usual (TAU), with the proportion of relapse/recurrence and/or time to relapse/recurrence as outcome measure.
Results
Thirty-six RCTs were included. During a 24-month period, psychological interventions significantly reduced risk of relapse/recurrence for patients with remitted MDD (RR 0.76, 95% CI: 0.68–0.86, p<0.001). This effect persisted with longer follow-up periods, although these results were less robust. Also, psychological interventions combined with M-ADM significantly reduced relapse during a 24-month period (RR 0.76, 95% CI: 0.62–0.94, p = 0.010), but this effect was not significant for longer follow-up periods. No meta-analysis could be performed on relapse prevention in anxiety disorders, as only two studies focused on relapse prevention in anxiety disorders.
Conclusions
In patients with remitted MDD, psychological relapse prevention interventions substantially reduce risk of relapse/recurrence. It is recommended to offer these interventions to remitted MDD patients. Studies on anxiety disorders are needed.
Systematic review registration number
PROSPERO 2018: CRD42018103142.
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Vander Zwalmen Y, Hoorelbeke K, Liebaert E, Nève de Mévergnies C, Koster EHW. Cognitive remediation for depression vulnerability: Current challenges and new directions. Front Psychol 2022; 13:903446. [PMID: 35936259 PMCID: PMC9352853 DOI: 10.3389/fpsyg.2022.903446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
It is increasingly acknowledged that cognitive impairment can play an important role in depression vulnerability. Therefore, cognitive remediation strategies, and cognitive control training (CCT) procedures have gained attention in recent years as possible interventions for depression. Recent studies suggest a small to medium effect on indicators of depression vulnerability. Despite initial evidence for the efficacy and effectiveness of CCT, several central questions remain. In this paper we consider the key challenges for the clinical implementation of CCT, including exploration of (1) potential working mechanisms and related to this, moderators of training effects, (2) necessary conditions under which CCT could be optimally administered, such as dose requirements and training schedules, and (3) how CCT could interact with or augment existing treatments of depression. Revisiting the CCT literature, we also reflect upon the possibilities to evolve toward a stratified medicine approach, in which individual differences could be taken into account and used to optimize prevention of depression.
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Affiliation(s)
- Yannick Vander Zwalmen
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
- *Correspondence: Yannick Vander Zwalmen,
| | - Kristof Hoorelbeke
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Eveline Liebaert
- Department of Head and Skin, Ghent University Hospital, Ghent, Belgium
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Buckman JEJ, Saunders R, Stott J, Cohen ZD, Arundell LL, Eley TC, Hollon SD, Kendrick T, Ambler G, Watkins E, Gilbody S, Kessler D, Wiles N, Richards D, Brabyn S, Littlewood E, DeRubeis RJ, Lewis G, Pilling S. Socioeconomic Indicators of Treatment Prognosis for Adults With Depression: A Systematic Review and Individual Patient Data Meta-analysis. JAMA Psychiatry 2022; 79:406-416. [PMID: 35262620 PMCID: PMC8908224 DOI: 10.1001/jamapsychiatry.2022.0100] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Importance Socioeconomic factors are associated with the prevalence of depression, but their associations with prognosis are unknown. Understanding this association would aid in the clinical management of depression. Objective To determine whether employment status, financial strain, housing status, and educational attainment inform prognosis for adults treated for depression in primary care, independent of treatment and after accounting for clinical prognostic factors. Data Sources The Embase, International Pharmaceutical Abstracts, MEDLINE, PsycINFO, and Cochrane (CENTRAL) databases were searched from database inception to October 8, 2021. Study Selection Inclusion criteria were as follows: randomized clinical trials that used the Revised Clinical Interview Schedule (CIS-R; the most common comprehensive screening and diagnostic measure of depressive and anxiety symptoms in primary care randomized clinical trials), measured socioeconomic factors at baseline, and sampled patients with unipolar depression who sought treatment for depression from general physicians/practitioners or who scored 12 or more points on the CIS-R. Exclusion criteria included patients with depression secondary to a personality or psychotic disorder or neurologic condition, studies of bipolar or psychotic depression, studies that included children or adolescents, and feasibility studies. Studies were independently assessed against inclusion and exclusion criteria by 2 reviewers. Data Extraction and Synthesis Data were extracted and cleaned by data managers for each included study, further cleaned by multiple reviewers, and cross-checked by study chief investigators. Risk of bias and quality were assessed using the Quality in Prognosis Studies (QUIPS) and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) tools, respectively. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses-Individual Participant Data (PRISMA-IPD) reporting guidelines. Main Outcomes and Measures Depressive symptoms at 3 to 4 months after baseline. Results This systematic review and individual patient data meta-analysis identified 9 eligible studies that provided individual patient data for 4864 patients (mean [SD] age, 42.5 (14.0) years; 3279 women [67.4%]). The 2-stage random-effects meta-analysis end point depressive symptom scale scores were 28% (95% CI, 20%-36%) higher for unemployed patients than for employed patients and 18% (95% CI, 6%-30%) lower for patients who were homeowners than for patients living with family or friends, in hostels, or homeless, which were equivalent to 4.2 points (95% CI, 3.6-6.2 points) and 2.9 points (95% CI, 1.1-4.9 points) on the Beck Depression Inventory II, respectively. Financial strain and educational attainment were associated with prognosis independent of treatment, but unlike employment and housing status, there was little evidence of associations after adjusting for clinical prognostic factors. Conclusions and Relevance Results of this systematic review and meta-analysis revealed that unemployment was associated with a poor prognosis whereas home ownership was associated with improved prognosis. These differences were clinically important and independent of the type of treatment received. Interventions that address employment or housing difficulties could improve outcomes for patients with depression.
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Affiliation(s)
- Joshua E. J. Buckman
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
- iCope Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, United Kingdom
| | - Rob Saunders
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | - Joshua Stott
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | | | - Laura-Louise Arundell
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | - Thalia C. Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Steven D. Hollon
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Tony Kendrick
- Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Gareth Ambler
- Statistical Science, University College London, London, United Kingdom
| | - Edward Watkins
- Department of Psychology, University of Exeter, Exeter, United Kingdom
| | - Simon Gilbody
- Department of Health Sciences, University of York, York, United Kingdom
| | - David Kessler
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicola Wiles
- Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - David Richards
- Institute of Health Research, University of Exeter College of Medicine and Health, Exeter, United Kingdom
- Department of Health and Caring Sciences, Western Norway University of Applied Sciences, Bergen, Norway
| | - Sally Brabyn
- Department of Health Sciences, University of York, York, United Kingdom
| | | | - Robert J. DeRubeis
- University of Pennsylvania College of Arts and Sciences, Department of Psychology, Philadelphia
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, United Kingdom
| | - Stephen Pilling
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
- Camden & Islington NHS Foundation Trust, London, United Kingdom
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