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Vinkers CH, Kupka RW, Penninx BW, Ruhé HG, van Gaalen JM, van Haaren PCF, Schellekens AFA, Jauhar S, Ramos-Quiroga JA, Vieta E, Tiihonen J, Veldman SE, Veling W, Vis R, de Wit LE, Luykx JJ. Discontinuation of psychotropic medication: a synthesis of evidence across medication classes. Mol Psychiatry 2024; 29:2575-2586. [PMID: 38503923 DOI: 10.1038/s41380-024-02445-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/22/2023] [Accepted: 01/22/2024] [Indexed: 03/21/2024]
Abstract
Pharmacotherapy is an effective treatment modality across psychiatric disorders. Nevertheless, many patients discontinue their medication at some point. Evidence-based guidance for patients, clinicians, and policymakers on rational discontinuation strategies is vital to enable the best, personalized treatment for any given patient. Nonetheless, there is a scarcity of guidelines on discontinuation strategies. In this perspective, we therefore summarize and critically appraise the evidence on discontinuation of six major psychotropic medication classes: antidepressants, antipsychotics, benzodiazepines, mood stabilizers, opioids, and stimulants. For each medication class, a wide range of topics pertaining to each of the following questions are discussed: (1) Who can discontinue (e.g., what are risk factors for relapse?); (2) When to discontinue (e.g., after 1 year or several years of antidepressant use?); and (3) How to discontinue (e.g., what's the efficacy of dose reduction compared to full cessation and interventions to mitigate relapse risk?). We thus highlight how comparing the evidence across medication classes can identify knowledge gaps, which may pave the way for more integrated research on discontinuation.
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Affiliation(s)
- Christiaan H Vinkers
- Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University Medical Center location Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands.
- Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands.
- GGZ inGeest Mental Health Care, Amsterdam, The Netherlands.
| | - Ralph W Kupka
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Brenda W Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Henricus G Ruhé
- Department of Psychiatry, Radboudumc, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jakob M van Gaalen
- GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paul C F van Haaren
- Department of Psychiatry, Radboudumc, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Arnt F A Schellekens
- Department of Psychiatry, Radboudumc, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Nijmegen Institute for Scientist Practitioners in Addiction (NISPA), Nijmegen, The Netherlands
| | - Sameer Jauhar
- Centre for Affective Disorders, Psychological Medicine, IoPPN, King's College, London, UK
| | - Josep A Ramos-Quiroga
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Group of Psychiatry, Mental Health and Addictions, Vall d'Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Catalonia, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Jari Tiihonen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Neuroscience, Karolinska Institutet, 11364, Stockholm, Sweden
- Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Stijn E Veldman
- Department of Psychiatry, Radboudumc, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Nijmegen Institute for Scientist Practitioners in Addiction (NISPA), Nijmegen, The Netherlands
- Novadic-Kentron Addiction Care, Vught, The Netherlands
| | - Wim Veling
- Department of Psychiatry, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Roeland Vis
- Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein/Utrecht, The Netherlands
| | - Laura E de Wit
- Department of Psychiatry, St. Antonius Hospital, Nieuwegein/Utrecht, The Netherlands
| | - Jurjen J Luykx
- GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
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Cheng Z, Johar A, Lagergren J, Schandl A, Lagergren P. Disease-specific health-related quality of life trajectories up to 15 years after curative treatment for esophageal cancer-a prospective cohort study. Cancer Med 2024; 13:e7466. [PMID: 38963063 PMCID: PMC11222968 DOI: 10.1002/cam4.7466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 06/10/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND The presence of distinct long-term disease-specific HRQL trajectories after curative treatment for esophageal cancer and factors associated with such trajectories are unclear. MATERIALS AND METHODS This population-based and longitudinal cohort study included 425 esophageal cancer patients who underwent curative treatment, including esophagectomy, in Sweden in 2001-2005 and were followed up until 2020, that is, 15-year follow-up. The outcomes were 10 disease-specific HRQL symptoms, measured by the well-validated EORTC QLQ-OES18 questionnaire at 6 months (n = 402 patients), and 3 (n = 178), 5 (n = 141), 10 (n = 92), and 15 years (n = 52) after treatment. HRQL symptoms were examined for distinct trajectories by growth mixture models. Weighted logistic regression models provided odds ratios (OR) with 95% confidence intervals (95% CI) for nine factors in relation to HRQL trajectories: age, sex, education, proxy baseline HRQL, comorbidity, tumor histology, chemo(radio)therapy, pathological tumor stage, and postoperative complications. RESULTS Distinct HRQL trajectories were identified for each of the 10 disease-specific symptoms. HRQL trajectories with more symptoms tended to persist or alleviate over time, while trajectories with fewer symptoms were more stable. Eating difficulty had three trajectories: persistently less, persistently moderate, and persistently more symptoms. The OR of having a persistently more eating difficulty trajectory was decreased for adenocarcinoma histology (OR = 0.44, 95% CI 0.21-0.95), and increased for pathological tumor stage III-IV (OR = 2.19, 95% CI 0.99-4.82) and 30-day postoperative complications (OR = 2.54, 95% CI 1.26-5.12). CONCLUSION Distinct trajectories with long-term persistent or deteriorating disease-specific HRQL symptoms were identified after esophageal cancer treatment. Tumor histology, tumor stage, and postoperative complications may facilitate detection of high-risk patients for unwanted trajectories.
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Affiliation(s)
- Zhao Cheng
- Surgical Care Science, Department of Molecular Medicine and SurgeryKarolinska Institutet, Karolinska University HospitalStockholmSweden
| | - Asif Johar
- Surgical Care Science, Department of Molecular Medicine and SurgeryKarolinska Institutet, Karolinska University HospitalStockholmSweden
| | - Jesper Lagergren
- Upper Gastrointestinal Surgery, Department of Molecular medicine and SurgeryKarolinska Institutet, Karolinska University HospitalStockholmSweden
- School of Cancer and Pharmaceutical SciencesKing's College LondonUK
| | - Anna Schandl
- Surgical Care Science, Department of Molecular Medicine and SurgeryKarolinska Institutet, Karolinska University HospitalStockholmSweden
| | - Pernilla Lagergren
- Surgical Care Science, Department of Molecular Medicine and SurgeryKarolinska Institutet, Karolinska University HospitalStockholmSweden
- Department of Surgery and CancerImperial College LondonLondonUK
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Banerjee S, Wu Y, Bingham KS, Marino P, Meyers BS, Mulsant BH, Neufeld NH, Oliver LD, Power JD, Rothschild AJ, Sirey JA, Voineskos AN, Whyte EM, Alexopoulos GS, Flint AJ. Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning. Psychol Med 2024; 54:1142-1151. [PMID: 37818656 DOI: 10.1017/s0033291723002945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
BACKGROUND Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. METHOD One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. RESULTS Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. CONCLUSIONS Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
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Affiliation(s)
- Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, USA
| | - Yiyuan Wu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, USA
| | - Kathleen S Bingham
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Benoit H Mulsant
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Nicholas H Neufeld
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | | | | | - Anthony J Rothschild
- University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA
| | - Jo Anne Sirey
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Aristotle N Voineskos
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Alastair J Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
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Cheng Z, Johar A, Lagergren J, Schandl A, Lagergren P. Health-related quality of life trajectories up to 15 years after curative treatment for esophageal cancer: a prospective cohort study. Int J Surg 2024; 110:1537-1545. [PMID: 38116704 PMCID: PMC10942160 DOI: 10.1097/js9.0000000000001026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND The differentiation of specific, long-term health-related quality of life (HRQL) trajectories among esophageal cancer survivors remains unclear. The authors aimed to identify potentially distinctly different HRQL-trajectories and uncover the underlying factors of such trajectories in patients having undergone surgery (esophagectomy) for esophageal cancer. MATERIALS AND METHODS This nationwide, prospective, and longitudinal cohort study included 420 patients who underwent curative treatment for esophageal cancer, including esophageal cancer surgery, in Sweden from 2001to 2005. The main outcome was HRQL summary score trajectories, measured by the well-validated EORTC QLQ-C30 questionnaire at 6 months, 3, 5, 10, and 15 years after esophagectomy, and analyzed using growth mixture models. Potentially underlying factors for these trajectories (age, sex, education, proxy baseline HRQL, comorbidity, tumor histology, chemo(radio)therapy, pathological tumor stage, and postoperative complications) were analyzed using weighted logistic regression providing odds ratios (OR) with 95% CI. RESULTS Four distinct HRQL summary score trajectories were identified: Persistently good, improving, deteriorating, and persistently poor. The odds of belonging to a persistently poor trajectory were decreased by longer education (>12 years versus <9 years: OR 0.18, 95% CI: 0.05-0.66) and adenocarcinoma histology (adenocarcinoma versus squamous cell carcinoma: OR 0.37, 95% CI: 0.16-0.85), and increased by more advanced pathological tumor stage (III-IV versus 0-I: OR 2.82, 95% CI: 1.08-7.41) and postoperative complications (OR 2.94, 95% CI: 1.36-6.36). CONCLUSION Distinct trajectories with persistently poor or deteriorating HRQL were identified after curative treatment for esophageal cancer. Education, tumor histology, pathological tumor stage, and postoperative complications might influence HRQL trajectories. The results may contribute to a more tailored follow-up with timely and targeted interventions. Future research remains to confirm these findings.
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Affiliation(s)
- Zhao Cheng
- Surgical Care Science, Department of Molecular medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Asif Johar
- Surgical Care Science, Department of Molecular medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Lagergren
- Upper Gastrointestinal Surgery, Department of Molecular medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- School of Cancer and Pharmaceutical Sciences, King’s College London, United Kingdom
| | - Anna Schandl
- Surgical Care Science, Department of Molecular medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pernilla Lagergren
- Surgical Care Science, Department of Molecular medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Department of Surgery and Cancer, Imperial College London, United Kingdom
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Cheng P, Wang L, Zhao G, Li W. Dynamic risk factors of psychiatric readmission for major depressive disorder: A longitudinal study on patients treated with mono-antidepressant. Psychiatry Res 2024; 333:115750. [PMID: 38277810 DOI: 10.1016/j.psychres.2024.115750] [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: 11/15/2023] [Revised: 01/19/2024] [Accepted: 01/21/2024] [Indexed: 01/28/2024]
Abstract
In this comprehensive study, we sought to unravel the risk factors for recurrence in Major Depressive Disorder (MDD), uniquely focusing on patients undergoing mono-antidepressant treatment. By considering psychiatric readmission as a direct indicator of MDD recurrence, we meticulously analyzed the records of 1,456 inpatients from a Chinese mental health center from 2012 to 2020. Our follow-up periods, spanning 90, 180, and 365 days post-discharge, allowed for a nuanced understanding of the recurrence dynamics. We identified four critical risk factors: thyroid function (FT3 and TSH), high-density lipoprotein (HDL) levels, and region of residence. Notably, the study revealed an increasing risk of readmission associated with decreased FT3 and HDL over time, while elevated TSH and residing in another province 's impact diminished. The antidepressant type did not significantly alter readmission risks, providing a unique perspective on MDD management. This research contributes to the field by offering a deeper understanding of how demographic and biochemical factors influence the likelihood of MDD recurrence, guiding more effective treatment approaches.
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Affiliation(s)
- Peng Cheng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Lirong Wang
- The Xiangya Hospital of Central South University, Changsha, Hunan 410008, China
| | - Guangju Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Weihui Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
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Cheng Z, Johar A, Nilsson M, Schandl A, Lagergren P. Cancer-related fatigue trajectories up to 5 years after curative treatment for oesophageal cancer. Br J Cancer 2024; 130:628-637. [PMID: 38135716 PMCID: PMC10876982 DOI: 10.1038/s41416-023-02551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Whether cancer-related fatigue develops differently after curative-intended oesophageal cancer treatment and the related modifiable factors are unclear. METHODS This population-based and longitudinal cohort included 409 oesophageal cancer patients who underwent curative oesophagectomy in 2013-2020 in Sweden. The main outcome was cancer-related fatigue trajectories with measurements at 1, 1.5, 2, 2.5, 3, 4 and 5 years postoperatively by validated EORTC QLQ-FA12 questionnaire, and analysed using growth mixture models. Weighted logistic regressions provided odds ratios (OR) with 95% confidence intervals (95% CI) for underlying sociodemographic, clinical, and patient-reported outcome factors in relation to the identified trajectories. RESULTS Two distinct overall cancer-related fatigue trajectories were identified: low level of persistent fatigue and high level of increasing fatigue, with 64% and 36% of patients, respectively. The odds of having high level of fatigue trajectory were increased by Charlson comorbidity index (≥ 2 versus 0: OR = 2.52, 95% CI 1.07-5.94), pathological tumour Stage (III-IV versus 0-I: OR = 2.52, 95% CI 1.33-4.77), anxiety (OR = 7.58, 95% CI 2.20-26.17), depression (OR = 15.90, 95% CI 4.44-56.93) and pain (continuous score: OR = 1.02, 95% CI 1.01-1.04). CONCLUSIONS Long-term trajectories with high level of increasing cancer-related fatigue and the associated modifiable factors were identified after oesophageal cancer treatment. The results may facilitate early identification and targeted intervention for such high-risk patients.
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Affiliation(s)
- Zhao Cheng
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Asif Johar
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Magnus Nilsson
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Upper Abdominal Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Schandl
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pernilla Lagergren
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
- Department of Surgery and Cancer, Imperial College London, London, UK.
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Krystal JH, Kavalali ET, Monteggia LM. Ketamine and rapid antidepressant action: new treatments and novel synaptic signaling mechanisms. Neuropsychopharmacology 2024; 49:41-50. [PMID: 37488280 PMCID: PMC10700627 DOI: 10.1038/s41386-023-01629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/29/2023] [Accepted: 06/04/2023] [Indexed: 07/26/2023]
Abstract
Ketamine is an open channel blocker of ionotropic glutamatergic N-Methyl-D-Aspartate (NMDA) receptors. The discovery of its rapid antidepressant effects in patients with depression and treatment-resistant depression fostered novel effective treatments for mood disorders. This discovery not only provided new insight into the neurobiology of mood disorders but also uncovered fundamental synaptic plasticity mechanisms that underlie its treatment. In this review, we discuss key clinical aspects of ketamine's effect as a rapidly acting antidepressant, synaptic and circuit mechanisms underlying its action, as well as how these novel perspectives in clinical practice and synapse biology form a road map for future studies aimed at more effective treatments for neuropsychiatric disorders.
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Affiliation(s)
- John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Ege T Kavalali
- Department of Pharmacology and the Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Lisa M Monteggia
- Department of Pharmacology and the Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.
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Krystal JH, Kaye AP, Jefferson S, Girgenti MJ, Wilkinson ST, Sanacora G, Esterlis I. Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments. Proc Natl Acad Sci U S A 2023; 120:e2305772120. [PMID: 38011560 DOI: 10.1073/pnas.2305772120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023] Open
Abstract
Ketamine has emerged as a transformative and mechanistically novel pharmacotherapy for depression. Its rapid onset of action, efficacy for treatment-resistant symptoms, and protection against relapse distinguish it from prior antidepressants. Its discovery emerged from a reconceptualization of the neurobiology of depression and, in turn, insights from the elaboration of its mechanisms of action inform studies of the pathophysiology of depression and related disorders. It has been 25 y since we first presented our ketamine findings in depression. Thus, it is timely for this review to consider what we have learned from studies of ketamine and to suggest future directions for the optimization of rapid-acting antidepressant treatment.
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Affiliation(s)
- John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Alfred P Kaye
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Sarah Jefferson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Matthew J Girgenti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Samuel T Wilkinson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
| | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
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Alcantarilla L, López-Castro M, Betriu M, Torres A, Garcia C, Solé E, Gelabert E, Roca-Lecumberri A. Risk factors for relapse or recurrence in women with bipolar disorder and recurrent major depressive disorder in the perinatal period: a systematic review. Arch Womens Ment Health 2023; 26:737-754. [PMID: 37718376 DOI: 10.1007/s00737-023-01370-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
It is well known that the perinatal period supposes a considerable risk of relapse for women with bipolar disorder (BD) and recurrent major depressive disorder (rMDD), with the consequences that this entails. Therefore, the authors sought to provide a critical appraisal of the evidence related to specific risk factors for this population with the aim of improving the prevention of relapses during pregnancy and postpartum. The authors conducted a systematic review assessing 18 original studies that provided data on risk factors for relapse or recurrence of BD and/or rMDD in the perinatal period (pregnancy and postpartum). Recurrences of BD and rMDD are more frequent in the postpartum period than in pregnancy, with the first 4-6 weeks postpartum being especially complicated. In addition, women with BD type I are at higher risk than those with BD type II and rMDD, and the most frequent presentation of perinatal episodes of both disorders is a major depressive episode. Other risk factors consistently repeated were early age of onset of illnesses, severity criteria, primiparity, abrupt discontinuation of treatment, and personal or family history of perinatal affective episodes. This review shows that there are common and different risk factors according to the type of disorder and to perinatal timing (pregnancy or postpartum) that should be known for an adequate prevention of relapses.
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Affiliation(s)
- Laura Alcantarilla
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
- Psychiatry Service, Hospital de Sagunto, Valencia, Spain
| | - María López-Castro
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
- Psychiatry Service, Sant Pau's Biomedical Research Institute (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Maria Betriu
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Anna Torres
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Cristina Garcia
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Eva Solé
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Estel Gelabert
- Department of Clinical Psychology and Health, Autonomous University of Barcelona, Barcelona, Spain
| | - Alba Roca-Lecumberri
- Perinatal Mental Health Unit CLINIC_BCN, Hospital Clínic de Barcelona, Barcelona, Spain.
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Saulnier KG, Panaite V, Ganoczy D, Kim HM, Zivin K, Hofer T, Piette JD, Pfeiffer PN. Depression symptom outcomes and re-engagement among VA patients who discontinue care while symptomatic. Gen Hosp Psychiatry 2023; 85:87-94. [PMID: 37862961 DOI: 10.1016/j.genhosppsych.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/12/2023] [Accepted: 10/12/2023] [Indexed: 10/22/2023]
Abstract
OBJECTIVE Evaluate outcomes of Veterans who discontinued treatment with at least moderate ongoing depressive symptoms. METHOD Veterans with elevated depression symptoms from 29 Department of Veterans Affairs facilities completed baseline surveys and follow-up assessments for one year. Analyses examined rates and predictors of treatment discontinuation, treatment re-engagement, and subsequent symptoms among patients who remained out of care. RESULTS A total of 242 (17.8%; n = 1359) participants discontinued treatment while symptomatic, with Black participants, participants with less severe depression, and participants receiving only psychotherapy (versus combined psychotherapy and antidepressant medications) discontinuing at higher rates. Among all participants who discontinued treatment (n = 445), 45.8% re-engaged within the following six months with participants receiving combined treatment re-engaging at higher rates. Of participants who discontinued while symptomatic within the first 6 months of the study and did not return to care (n = 112), 68.8% remained symptomatic at 12 months. Lower baseline treatment expectancy and greater depression symptom severity were associated with remaining symptomatic while untreated. CONCLUSIONS Black race, lower symptom severity, and treatment modality may help identify patients at higher risk for discontinuing care while symptomatic, whereas patients with lower treatment expectations may be at greater risk for remaining out of care despite continuing symptoms.
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Affiliation(s)
- K G Saulnier
- VA Serious Mental Illness Treatment Resource and Evaluation Center, Ann Arbor, MI, USA; VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; University of Michigan Medical School, Ann Arbor, MI, USA.
| | - V Panaite
- James A. Haley Veterans' Hospital, Tampa, FL, USA
| | - D Ganoczy
- VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - H M Kim
- VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; University of Michigan Consulting for Statistics, Computing, and Analytics Research, Ann Arbor, MI, USA
| | - K Zivin
- University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - T Hofer
- University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - J D Piette
- University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - P N Pfeiffer
- VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
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11
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Koshikawa Y, Onohara A, Wakeno M, Takekita Y, Kinoshita T, Kato M. Characteristics of persistent depression in the long-term: Randomized controlled trial and two-year observational study. Heliyon 2023; 9:e20917. [PMID: 37886758 PMCID: PMC10597827 DOI: 10.1016/j.heliyon.2023.e20917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 10/07/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
Major depressive disorder is a chronic condition that can recur and relapse. It would be clinically useful to know the patient background to predict the chronicity of depressive symptoms, and the change in diagnosis of bipolar disorder. This study included 197 patients enrolled in a six-week randomized controlled trial with a two-year follow-up. We conducted multiple logistic regression analyses to identify the clinical and sociodemographic characteristics associated with persistent depressive disorder (PDD), relapse, and changes in bipolar disorder diagnosis. The significantly correlated factors were residual symptoms, including insight, work and activity, and general somatic symptoms at week six. We could not identify any factors that contributed to relapse or change in the diagnosis of bipolar disorder. We found that the specific residual symptoms of acute treatment affected long-term treatment outcomes for depression. Attention should be paid to the residual symptoms of depression in the early stages of treatment, and measures should be considered to improve them. There are several limitations to this study, including the fact that PDD may exist among patients who discontinued treatment, treatment was not standardized during the study period, and adherence was confirmed verbally.
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Affiliation(s)
- Yosuke Koshikawa
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Ai Onohara
- Social Welfare Corporation Uminoko Gakuen Ikejimaryo, Osaka, Japan
| | - Masataka Wakeno
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | | | | | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
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12
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Solmi M, Cortese S, Vita G, De Prisco M, Radua J, Dragioti E, Köhler-Forsberg O, Madsen NM, Rohde C, Eudave L, Aymerich C, Pedruzo B, Rodriguez V, Rosson S, Sabé M, Hojlund M, Catalan A, de Luca B, Fornaro M, Ostuzzi G, Barbui C, Salazar-de-Pablo G, Fusar-Poli P, Correll CU. An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders. Mol Psychiatry 2023; 28:3671-3687. [PMID: 37957292 PMCID: PMC10730397 DOI: 10.1038/s41380-023-02298-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/21/2023] [Accepted: 10/06/2023] [Indexed: 11/15/2023]
Abstract
We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an umbrella review (protocol link ), searching MEDLINE/Embase (19/07/2022), including systematic reviews of studies reporting on MCPs of response, remission, recovery, or relapse, in DSM/ICD-defined mental disorders. From published predictors, we filtered MCPs, validating MCP criteria. AMSTAR2/PROBAST measured quality/risk of bias of systematic reviews/individual studies. We included 117 systematic reviews, 403 studies, 299,888 individuals with mental disorders, testing 796 prediction models. Only 4.3%/1.2% of the systematic reviews/individual studies were at low risk of bias. The most frequently targeted outcome was remission (36.9%), the least frequent was recovery (2.5%). Studies mainly focused on depressive (39.4%), substance-use (17.9%), and schizophrenia-spectrum (11.9%) disorders. We identified numerous MCPs within disorders for response, remission and relapse, but none for recovery. Transdiagnostic MCPs of remission included lower disease-specific symptoms (disorders = 5), female sex/higher education (disorders = 3), and quality of life/functioning (disorders = 2). Transdiagnostic MCPs of relapse included higher disease-specific symptoms (disorders = 5), higher depressive symptoms (disorders = 3), and younger age/higher anxiety symptoms/global illness severity/ number of previous episodes/negative life events (disorders = 2). Finally, positive trans-outcome MCPs for depression included less negative life events/depressive symptoms (response, remission, less relapse), female sex (response, remission) and better functioning (response, less relapse); for schizophrenia, less positive symptoms/higher depressive symptoms (remission, less relapse); for substance use disorder, marital status/higher education (remission, less relapse). Male sex, younger age, more clinical symptoms and comorbid mental/physical symptoms/disorders were poor prognostic factors, while positive factors included social contacts and employment, absent negative life events, higher education, early access/intervention, lower disease-specific and comorbid mental and physical symptoms/conditions, across mental disorders. Current data limitations include high risk of bias of studies and extraction of single predictors from multivariable models. Identified MCPs can inform future development, validation or refinement of prediction models of key outcomes in mental disorders.
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Affiliation(s)
- Marco Solmi
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
- DiMePRe-J-Department of Precision and Regenerative Medicine-Jonic Area, University of Bari "Aldo Moro", Bari, Italy
| | - Giovanni Vita
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Michele De Prisco
- Bipolar and Depressive Disorders Unit, Hospìtal Clinic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Imaging of Mood- and Anxiety-Related Disorders (IMARD), CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Elena Dragioti
- University of Ioannina, Research Laboratory Psychology of Patients, Families & Health Professionals, Department of Nursing, School of Health Sciences, Ioannina, Greece
- Linköping University, Pain and Rehabilitation Centre and Department of Health, Medicine and Caring Sciences, Linköping, Sweden
| | - Ole Köhler-Forsberg
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Nanna M Madsen
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Christopher Rohde
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Luis Eudave
- Faculty of Education and Psychology, University of Navarra, Pamplona, Spain
| | - Claudia Aymerich
- Biobizkaia Health Research Institute, Basurto University Hospital, OSI Bilbao-Basurto. University of the Basque Country UPV/EHU. Centro de Investigación en Red de Salud Mental. (CIBERSAM), Instituto de Salud Carlos III. Plaza de Cruces 12, 48903, Barakaldo, Bizkaia, Spain
| | - Borja Pedruzo
- Psychiatry Department, Basurto University Hospital, Bilbao, Spain
| | | | - Stella Rosson
- Mental Health Department, Local Health Unit ULSS3 Serenissima, Venice, Italy
| | - Michel Sabé
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, 2, Chemin du Petit-Bel-Air, CH-1226, Thonex, Switzerland
| | - Mikkel Hojlund
- Department of Psychiatry Aabenraa, Mental Health Services Region of Southern Denmark, Aabenraa, Denmark
- Clinical Pharmacology, Pharmacy, and Environmental Medicine, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Child and Adolescent Mental Health Centre, Mental Health Services Capital Region of Denmark, Copenhagen, Denmark
| | - Ana Catalan
- Biobizkaia Health Research Institute, Basurto University Hospital, OSI Bilbao-Basurto. University of the Basque Country UPV/EHU. Centro de Investigación en Red de Salud Mental. (CIBERSAM), Instituto de Salud Carlos III. Plaza de Cruces 12, 48903, Barakaldo, Bizkaia, Spain
| | - Beatrice de Luca
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Michele Fornaro
- Department of Psychiatry, Federico II of Naples, Naples, Italy
| | - Giovanni Ostuzzi
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Corrado Barbui
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Gonzalo Salazar-de-Pablo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Psychiatry and Mental Health. Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Madrid, Spain
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Outreach and Support in South London (OASIS) service, NHS South London and Maudsley Foundation Trust, London, UK
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany.
- The Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA.
- The Feinstein Institute for Medical Research, Center for Psychiatric Neuroscience, Manhasset, NY, USA.
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13
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Yamazaki R, Matsuda Y, Oba M, Oi H, Kito S. Maintenance repetitive transcranial magnetic stimulation (rTMS) therapy for treatment-resistant depression: a study protocol of a multisite, prospective, non-randomized longitudinal study. BMC Psychiatry 2023; 23:437. [PMID: 37322460 PMCID: PMC10273734 DOI: 10.1186/s12888-023-04944-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is a widely used treatment for major depressive disorder (MDD), and its effectiveness in preventing relapse/recurrence of MDD has been explored. Although few small sample controlled studies exist, the protocols of maintenance rTMS therapy were heterogeneous and evidence of its effectiveness is not sufficient. Thus, this study aims to evaluate whether maintenance rTMS is effective in maintaining the treatment response in patients with MDD with a large sample size and feasible study design. METHODS In this multicenter open-labelled parallel-group trial we plan to recruit 300 patients with MDD who have responded or remitted to acute rTMS therapy. Participants would be classified into two groups according to their preference; the maintenance rTMS and pharmacotherapy group, and the pharmacotherapy only group. The protocol of maintenance rTMS therapy is once a week for the first six months and once biweekly for the second six months. The primary outcome is the relapse/recurrence rates during 12 months following enrollment. Other measures of depressive symptoms and recurrence/relapse rates at different time points are the secondary outcomes. The primary analysis is the between-group comparison adjusted for background factors using a logistic regression model. We will perform the group comparison with inverse probability of treatment weighting as the sensitivity analysis to ensure the comparability of the two groups. DISCUSSION We hypothesize that maintenance rTMS therapy could be an effective and safe treatment for preventing depressive relapse/recurrence. Considering the limitation of potential bias owing to the study design, we plan to use statistical approaches and external data to avoid overestimation of the efficacy. TRIAL REGISTRATION Japan Registry of Clinical Trials, ID: jRCT1032220048 . Registered 1 May 2022.
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Affiliation(s)
- Ryuichi Yamazaki
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Yuki Matsuda
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Mari Oba
- Department of Clinical Data Science, Clinical Research and Education Premotion Division, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hideki Oi
- Department of Clinical Data Science, Clinical Research and Education Premotion Division, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Shinsuke Kito
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi-Machi, Kodaira-Shi, Tokyo, 1878551, Japan.
- Neuromodulation Therapy and Research Center, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan.
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14
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Curtiss JE, Mischoulon D, Fisher LB, Cusin C, Fedor S, Picard RW, Pedrelli P. Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach. Psychol Med 2023; 53:3124-3132. [PMID: 34937601 PMCID: PMC10606954 DOI: 10.1017/s0033291721005183] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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 Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain 'early warning signals' (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). METHODS Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. RESULTS Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = -0.23, p = 0.23) nor in network connectivity (r = -0.12, p = 0.59) were associated with changes in depression symptoms. CONCLUSIONS This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.
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Affiliation(s)
- Joshua E. Curtiss
- Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - David Mischoulon
- Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Lauren B. Fisher
- Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Cristina Cusin
- Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Szymon Fedor
- The Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rosalind W. Picard
- The Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Paola Pedrelli
- Depression Clinical and Research Program at Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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15
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Belge JB, Sabbe ACF, Sabbe BGCC. An update on pharmacotherapy for recurrent depression in 2022. Expert Opin Pharmacother 2023; 24:1387-1394. [PMID: 37300545 DOI: 10.1080/14656566.2023.2223962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Major depressive disorder remains a major challenge due to its biopsychosocial burden with increased morbidity and mortality. Despite successful treatment options for the acute episode, recurrence rates are high, on average four times in a life span. AREAS COVERED Both pharmacological as non-pharmacological evidence-based therapeutic options to prevent and treat recurrent depression are discussed. EXPERT OPINION Although some risk factors for recurrence are well known, better evidence is needed. Antidepressant medication should be continued after acute treatment at its full therapeutic dose for longer periods, at least 1 year. There are no clear differences between classes of antidepressant medication when treatment is focused on preventing relapse. Bupropion is the only antidepressant with a proven efficacy to prevent recurrence in seasonal affective disorder. Recent findings conclude maintenance subanesthetic ketamine and esketamine treatment can be effective in sustaining antidepressant effect following remission. Furthermore, the pharmacological approach must be integrated with lifestyle interventions, especially aerobic exercise. Finally, combining pharma- and psychotherapy seems to improve outcome. Network and complexity sciences will help to decrease the high recurrence rates of MDD by developing more integrative and personalized approaches.
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Affiliation(s)
- Jean-Baptiste Belge
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Amber C F Sabbe
- Department of Internal Medicine, University Hospital of Antwerp, Edegem, Belgium
- Campus Drie Eiken, Universiteitsplein 1, University of Antwerp, Wilrijk, Belgium
| | - Bernard G C C Sabbe
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
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16
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Gao M, Tu H, Liu P, Zhang Y, Zhang R, Jing L, Zhang K. Association analysis of gut microbiota and efficacy of SSRIs antidepressants in patients with major depressive disorder. J Affect Disord 2023; 330:40-47. [PMID: 36871910 DOI: 10.1016/j.jad.2023.02.143] [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: 10/24/2022] [Revised: 02/13/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND Relevant studies have shown that gut microbiome plays an important role in the occurrence, development and treatment of major depressive disorder (MDD). Many studies have also shown that, selective serotonin reuptake inhibitors (SSRIs) antidepressants can improve the symptoms of depression by changing the distribution of gut microbiome, Here we investigated whether a distinct gut microbiome was associated with Major depressive disorder (MDD), and how it was modulated by SSRIs antidepressants. METHOD In this study, we analyzed the gut microbiome composition of 62 patients with first-episode MDD and 41 matched healthy controls, before SSRIs antidepressants treatment, using 16S rRNA gene sequencing. MDD patients characterized as treatment-resistant (TR) or responders (R) to antidepressants by score reduction rate were ≥50 % after SSRIs antidepressants treatment for eight weeks. RESULTS LDA effect size (LEfSe) analysis found that there were 50 different bacterial groups among the three groups, of which 19 genera were mainly at the genus level. The relative abundance of 12 genera increased in the HCs group, 5 genera in the R group increased in relative abundance, and 2 genera in the TR group increased in relative abundance. The correlation analysis of 19 bacterial genera and the score reduction rate showed that Blautia, Bifidobacterium and Coprococcus with higher relative abundance in the treatment effective group were related to the efficacy of SSRIs antidepressants. CONCLUSIONS Patients with MDD have a distinct gut microbiome that changes after SSRIs antidepressants treatment. Dysbiosis could be a new therapeutic target and prognostic tool for the treatment of patients with MDD.
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Affiliation(s)
- Mingxue Gao
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
| | - Hongwei Tu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
| | - Yanyan Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Ruiyu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
| | - Lin Jing
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China.
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17
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Kubo K, Sakurai H, Tani H, Watanabe K, Mimura M, Uchida H. Predicting relapse from the time to remission during the acute treatment of depression: A re-analysis of the STAR*D data. J Affect Disord 2023; 320:710-715. [PMID: 36208688 DOI: 10.1016/j.jad.2022.09.162] [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: 04/30/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Predicting relapse during maintenance treatment for depression is challenging. The objective of this analysis was to investigate the association between the time taken to achieve remission in the acute phase, and the subsequent relapse rate or time to relapse using the Sequenced Treatment Alternatives to Relieve Depression dataset. METHOD Data of 1296 outpatients with nonpsychotic depression who entered a 12-month naturalistic follow-up period after achieving remission with citalopram for up to 14 weeks were analyzed. One-way analysis of variance and the Jonckheere-Terpstra trend test were performed to compare the relapse rates and days to relapse during the follow-up period among those who achieved remission at weeks 2, 4, 6, 9, 12, and 14. Remission and relapse were defined as scores of ≤5 and ≥11, respectively, on the 16-Item Quick Inventory of Depressive Symptomatology and Self-Report. RESULTS The relapse rates were significantly different among those who achieved remission each week (F(5, 1087) = 4.995, p < 0.001). The lowest and highest relapse rates were observed in those who achieved remission at weeks 4 (25.7 %) and 12 (42.4 %), respectively, with a significant difference (p = 0.006). There was also a significant negative trend between the weeks taken to achieve remission and the days to relapse (z = -6.13, p < 0.001). CONCLUSIONS Patients with depression who show a faster response to antidepressant treatment are more likely to maintain remission in the long term. This finding suggests that, to prevent relapse, close attention should be paid to patients who require a relatively long time to achieve remission.
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Affiliation(s)
- Kaoruhiko Kubo
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Hitoshi Sakurai
- Department of Neuropsychiatry, Kyorin University Faculty of Medicine, Tokyo, Japan.
| | - Hideaki Tani
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Koichiro Watanabe
- Department of Neuropsychiatry, Kyorin University Faculty of Medicine, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
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18
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Jia H, Yiyun C, Zhiguo W, Yousong S, Min Z, Yifan S, Na Z, Feng J, Yiru F, Daihui P. Associations between gastrointestinal symptoms, medication use, and spontaneous drug discontinuation in patients with major depressive disorder in China. J Affect Disord 2022; 319:462-468. [PMID: 36055529 DOI: 10.1016/j.jad.2022.08.116] [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: 02/07/2022] [Revised: 03/31/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND The study was designed to investigate the associations between gastrointestinal (GI) symptoms, medication use, and spontaneous drug discontinuation (SDD) in patients with major depressive disorder (MDD). METHODS This cross-sectional study included 3256 MDD patients from the National Survey on Symptomatology of Depression (NSSD). Differences in the sociodemographic factors, clinical characteristics, medication use, and self-reported reasons for SDD were compared in patients with different frequencies of GI symptoms. A multiple logistic regression analysis was employed to assess the contribution of GI symptoms to the risk of spontaneous drug discontinuation. RESULTS MDD patients with a higher frequency of GI symptoms were prone to have higher proportions of mood stabilizer and benzodiazepine uses (ps for trend < 0.001) but a lower proportion of SNRI use (pfor trend < 0.001). With the increase in GI symptoms, patients were prone to report worries about long-term side effects (pfor trend < 0.001), with the patients stating ineffective treatments (pfor trend = 0.002) and intolerance of adverse drug reactions (pfor trend = 0.022) as the reasons for SDD. Compared with those patients without GI symptoms, all of the MDD patients with GI symptom frequencies of several days (OR = 1.317; 95 % CI: 1.045-1.660), more than half of all days (OR = 1.305; 95 % CI: 1.005-1.695), and nearly every day (OR = 1.820; 95 %: 1.309-2.531) had an increased risk of SDD. CONCLUSION GI symptoms are highly associated with drug discontinuation in MDD patients. These findings may have important implications for clinical treatment options, as well as for drug adherence management, in MDD patients.
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Affiliation(s)
- Huang Jia
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Cai Yiyun
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, PR China
| | - Wu Zhiguo
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Su Yousong
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Zhang Min
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Shi Yifan
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Zhu Na
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; Shanghai Pudong New Area Mental Health Center, Shanghai 200122, PR China
| | - Jin Feng
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Fang Yiru
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Peng Daihui
- Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
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19
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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20
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Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting. Sci Rep 2022; 12:11171. [PMID: 35778458 PMCID: PMC9249776 DOI: 10.1038/s41598-022-13893-9] [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: 03/07/2022] [Accepted: 05/30/2022] [Indexed: 12/04/2022] Open
Abstract
The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.
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21
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Cheng Z, Anandavadivelan P, Nilsson M, Johar A, Lagergren P. Body Mass Index-Adjusted Weight Loss Grading System and Cancer-Related Fatigue in Survivors 1 Year After Esophageal Cancer Surgery. Ann Surg Oncol 2022; 29:10.1245/s10434-022-11633-x. [PMID: 35364767 PMCID: PMC9174120 DOI: 10.1245/s10434-022-11633-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/01/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND The association between pre- and postoperative weight loss and cancer-related fatigue after esophageal cancer surgery is unclear. This nationwide, prospective, longitudinal cohort study aimed to assess the influence of weight loss on cancer-related fatigue among esophageal cancer survivors. METHODS Patients who underwent esophagectomy for cancer between 2013 and 2019 in Sweden were enrolled in this study. Exposure was measured by the body mass index-adjusted weight loss grading system (WLGS). Cancer-related fatigue was assessed using the fatigue scale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the EORTC QLQ-Fatigue 12 (QLQ-FA12) questionnaire measuring overall fatigue and physical, emotional, and cognitive fatigue. Growth mixture models were used to identify unobserved trajectories of cancer-related fatigue. Multivariable linear and logistic regression models were fitted to assess the associations between WLGS and cancer-related fatigue, adjusting for potential confounders. RESULTS Three trajectories were identified-low, moderate, and severe persistent fatigue. Cancer-related fatigue remained stable in each trajectory between 1 and 3 years after esophagectomy. Among the 356 enrolled patients, 4.5-22.6% were categorized into the severe persistent fatigue trajectory in terms of QLQ-C30 (19.9%), FA12 overall (10.5%), physical (22.6%), emotional (15.9%), and cognitive fatigue (4.5%). No association between pre- or postoperative WLGS and cancer-related fatigue was found between 1 and 3 years after esophageal cancer surgery. CONCLUSIONS Weight loss did not seem to influence cancer-related fatigue after esophageal cancer surgery.
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Affiliation(s)
- Zhao Cheng
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Poorna Anandavadivelan
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Magnus Nilsson
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Upper Abdominal Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Asif Johar
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pernilla Lagergren
- Surgical Care Science, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
- Department of Surgery and Cancer, Imperial College London, London, UK.
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22
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Approach to Evaluating and Managing Adult Attention-Deficit/Hyperactivity Disorder in Primary Care. Harv Rev Psychiatry 2021; 28:100-106. [PMID: 32134834 DOI: 10.1097/hrp.0000000000000248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Requests for the evaluation of potential adult attention-deficit/hyperactivity disorder (ADHD) is on the rise across primary care clinics. Many health care providers, however, may feel ill equipped to diagnose and manage adults presenting with inattention and impulsivity. The diagnosis of ADHD is often complicated by medical and psychiatric conditions that can contribute to inattention symptoms. In this article, the authors provide a pragmatic clinical approach for evaluating and managing adult ADHD in the primary care setting.
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 169] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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24
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Bolinski F, Etzelmüller A, De Witte NAJ, van Beurden C, Debard G, Bonroy B, Cuijpers P, Riper H, Kleiboer A. Physiological and self-reported arousal in virtual reality versus face-to-face emotional activation and cognitive restructuring in university students: A crossover experimental study using wearable monitoring. Behav Res Ther 2021; 142:103877. [PMID: 34029860 DOI: 10.1016/j.brat.2021.103877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 04/16/2021] [Accepted: 04/26/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Arousal may be important for learning to restructure ones' negative cognitions, a core technique in depression treatment. In virtual reality (VR), situations may be experienced more vividly than, e.g., in an imaginative approach, potentially aiding the emotional activation of negative cognitions. However, it is unclear whether such activation and subsequent cognitive restructuring in VR elicits more physiological, e.g. changes in skin conductance (SC), heart rate (HR), and self-reported arousal. METHOD In a cross-over experiment, 41 healthy students experienced two sets, one in VR, one face-to-face (F2F), of three situations aimed at activating negative cognitions. Order of the sets and mode of delivery were randomised. A wristband wearable monitored SC and HR; self-reported arousal was registered verbally. RESULTS Repeated measures analyses of variance revealed significantly more SC peaks per minute, F (1, 40) = 13.89, p = .001, higher mean SC, F (1,40) = 7.47, p = .001, and higher mean HR, F (1, 40) = 75.84, p < .001 in VR compared to F2F. No differences emerged on the paired-samples t-test for self-reported arousal, t (40) = -1.35, p = .18. DISCUSSION To the best of our knowledge, this is the first study indicating that emotional activation and subsequent cognitive restructuring in VR can lead to significantly more physiological arousal compared to an imaginative approach. These findings need to be replicated before they can be extended to patient populations.
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Affiliation(s)
- Felix Bolinski
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands.
| | - Anne Etzelmüller
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands; GET.ON Institute/HelloBetter, Hamburg, Germany
| | - Nele A J De Witte
- Expertise Unit Psychology, Technology & Society, Thomas More University of Applied Sciences, Antwerp, Belgium
| | - Cecile van Beurden
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands
| | - Glen Debard
- Mobilab & Care, Thomas More University of Applied Sciences, Geel, Belgium
| | - Bert Bonroy
- Mobilab & Care, Thomas More University of Applied Sciences, Geel, Belgium
| | - Pim Cuijpers
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
| | - Heleen Riper
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
| | - Annet Kleiboer
- Vrije Universiteit Amsterdam, Department of Clinical, Neuro- & Developmental Psychology, Section of Clinical Psychology, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
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25
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Immanuel SA, Schrader G, Bidargaddi N. Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis. Front Psychiatry 2021; 12:558056. [PMID: 33692704 PMCID: PMC7938319 DOI: 10.3389/fpsyt.2021.558056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse. Materials and Methods: Anonymized records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analyzed. To characterize the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders. Results: Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001]. Discussion: The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random. Conclusion: This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.
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Affiliation(s)
- Sarah A. Immanuel
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Digital Health Research Centre, Flinders University, Adelaide, SA, Australia
| | - Geoff Schrader
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Barossa Gawler Adelaide Hills Fleurieu Local Health Network, Adelaide, SA, Australia
| | - Niranjan Bidargaddi
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Digital Health Research Centre, Flinders University, Adelaide, SA, Australia
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26
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Lenze EJ, Nicol GE, Barbour DL, Kannampallil T, Wong AWK, Piccirillo J, Drysdale AT, Sylvester CM, Haddad R, Miller JP, Low CA, Lenze SN, Freedland KE, Rodebaugh TL. Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders. J Psychiatry Neurosci 2021; 46:E97-E110. [PMID: 33206039 PMCID: PMC7955843 DOI: 10.1503/jpn.200042] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.
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Affiliation(s)
- Eric J Lenze
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Ginger E Nicol
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Dennis L Barbour
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Thomas Kannampallil
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Alex W K Wong
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Jay Piccirillo
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Andrew T Drysdale
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Chad M Sylvester
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Rita Haddad
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - J Philip Miller
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Carissa A Low
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Shannon N Lenze
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Kenneth E Freedland
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
| | - Thomas L Rodebaugh
- From the Washington University School of Medicine, St. Louis, Missouri (Lenze, Nicol, Kannampallil Wong, Piccirillo, Drysdale, Sylvester, Haddad, Miller, Lenze, Freedland); the Washington University McKelvey School of Engineering, St. Louis, MO (Barbour); the University of Pittsburgh, Pittsburgh, PA (Low); and the Washington University School of Arts & Sciences, St. Louis, MO (Rodebaugh)
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27
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Huijbers MJ, Wentink C, Simons E, Spijker J, Speckens A. Discontinuing antidepressant medication after mindfulness-based cognitive therapy: a mixed-methods study exploring predictors and outcomes of different discontinuation trajectories, and its facilitators and barriers. BMJ Open 2020; 10:e039053. [PMID: 33177138 PMCID: PMC7661362 DOI: 10.1136/bmjopen-2020-039053] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 09/15/2020] [Accepted: 09/30/2020] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES This study aimed to explore predictors and outcomes associated with different trajectories of discontinuing antidepressant medication (ADM), in recurrently depressed individuals after participation in mindfulness-based cognitive therapy (MBCT). Facilitators and barriers of discontinuation were explored qualitatively. DESIGN Mixed-methods study combining quantitative and qualitative data, drawn from a randomised controlled trial. SETTING Twelve secondary and tertiary psychiatric outpatient clinics in the Netherlands. PARTICIPANTS Recurrently depressed individuals (N=226) who had been using ADM for at least 6 months and in partial or full remission. Regardless of trial condition, we made post-hoc classifications of patients' actual discontinuation trajectories: full discontinuation (n=82), partial discontinuation (n=34) and no discontinuation (n=110) of ADM within 6 months after baseline. A subset of patients (n=15) and physicians (n=7) were interviewed to examine facilitators and barriers of discontinuation. INTERVENTIONS All participants were offered MBCT, which consisted of eight weekly sessions in a group. PRIMARY AND SECONDARY OUTCOME MEASURES Demographic and clinical predictors of successful discontinuation within 6 months, relapse risk within 15 months associated with different discontinuation trajectories, and barriers and facilitators of discontinuation. RESULTS Of the 128 patients assigned to MBCT with discontinuation, only 68 (53%) fully discontinued ADM within 6 months, and 17 (13%) discontinued partially. Predictors of full discontinuation were female sex, being employed and lower levels of depression. Relapse risk was lower after no discontinuation (45%) or partial discontinuation (38%), compared with full discontinuation (66%) (p=0.02). Facilitators and barriers of discontinuation were clustered within five themes: (1) pre-existing beliefs about depression, medication and tapering; (2) current experience with ADM; (3) life circumstances; (4) clinical support and (5) mindfulness. CONCLUSIONS Discontinuing antidepressants appears to be difficult, stressing the need to support patients and physicians in this process. MBCT may offer one of these forms of support. TRIAL REGISTRATION NUMBER ClinicalTrials.gov Registry (NCT00928980); post-results.
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Affiliation(s)
| | | | - Esther Simons
- Primary and Community Care, Radboudumc, Nijmegen, The Netherlands
| | - Jan Spijker
- Expertise Centre for Depression, Pro Persona Locatie Tarweweg, Nijmegen, Gelderland, The Netherlands
| | - Anne Speckens
- Psychiatry, Radboudumc, Nijmegen, Gelderland, The Netherlands
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Larsen KG, Kennedy SH, Reines EH, Thase ME. Patient Response Trajectories in Major Depressive Disorder. PSYCHOPHARMACOLOGY BULLETIN 2020; 50:8-28. [PMID: 33012870 PMCID: PMC7511149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To investigate whether the efficacy of antidepressants can be understood in terms of patient response-trajectory classes. EXPERIMENTAL DESIGN Patient-level data were analysed from 1357 adults with MDD randomised to either escitalopram 20 mg/day (n = 676) or placebo (n = 681) in five 8-week randomised placebo-controlled trials. Growth mixture models (GMMs) were used to identify the response trajectories; longitudinal latent class analysis (LLCA) was used to corroborate the findings. PRINCIPAL OBSERVATIONS Three classes of response were identified for escitalopram and placebo based on the trajectory of the patients' Montgomery-Åsberg Depression Rating Scale (MADRS) total scores during treatment. All three classes had similar mean baseline MADRS scores, but the change from baseline after 8 weeks differed: -4.2 MADRS points for non-responders, -18.4 MADRS points for slow responders, and -26.7 points for fast responders. The proportions of non-responders, slow responders and fast responders were 53%, 38% and 9%, respectively, with placebo and 27%, 58% and 14%, respectively, with escitalopram. Receiver operating curve analysis showed that a cut-off of ≥43% improvement from baseline to week 2 predicted fast responders, and a cut-off of ≥28% improvement from baseline to week 4 predicted responders (fast or slow). There were no clinically useful differences at baseline that predicted the trajectory class to which a patient would belong. CONCLUSIONS The presence of fast-, slow- and non-responder classes has a clear clinical relevance for guiding treatment decisions; individual patients can be classified by the change in their MADRS score from baseline at 2 or 4 weeks.
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Affiliation(s)
- Klaus G Larsen
- Larsen, Heldbo Reines, H. Lundbeck A/S, Copenhagen Valby, Denmark. Kennedy, Centre for Depression and Suicide Studies, St. Michael's Hospital and University of Toronto, Toronto, Ontario, Canada. Thase, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sidney H Kennedy
- Larsen, Heldbo Reines, H. Lundbeck A/S, Copenhagen Valby, Denmark. Kennedy, Centre for Depression and Suicide Studies, St. Michael's Hospital and University of Toronto, Toronto, Ontario, Canada. Thase, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Elin Heldbo Reines
- Larsen, Heldbo Reines, H. Lundbeck A/S, Copenhagen Valby, Denmark. Kennedy, Centre for Depression and Suicide Studies, St. Michael's Hospital and University of Toronto, Toronto, Ontario, Canada. Thase, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Michael E Thase
- Larsen, Heldbo Reines, H. Lundbeck A/S, Copenhagen Valby, Denmark. Kennedy, Centre for Depression and Suicide Studies, St. Michael's Hospital and University of Toronto, Toronto, Ontario, Canada. Thase, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
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Rush AJ, Thase ME. Improving Depression Outcome by Patient-Centered Medical Management. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2020; 18:244-254. [PMID: 33343242 DOI: 10.1176/appi.focus.18207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 05/30/2018] [Accepted: 06/11/2018] [Indexed: 12/01/2022]
Abstract
(Reprinted with permission from The American Journal of Psychiatry 2018; 175:1187-1198).
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Affiliation(s)
- A John Rush
- Duke-National University of Singapore Graduate Medical School, Singapore; the Department of Psychiatry, Duke University Medical School, Durham, N.C.; the Department of Psychiatry, Texas Tech Health Sciences Center-Permian Basin, Midland-Odessa; the Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia; and the Corporal Michael J. Crescenz VA Medical Center, Philadelphia
| | - Michael E Thase
- Duke-National University of Singapore Graduate Medical School, Singapore; the Department of Psychiatry, Duke University Medical School, Durham, N.C.; the Department of Psychiatry, Texas Tech Health Sciences Center-Permian Basin, Midland-Odessa; the Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia; and the Corporal Michael J. Crescenz VA Medical Center, Philadelphia
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Abstract
A total of 201 patients with major depressive disorder from four hospitals in Malaysia were followed up for 5 years to determine the prognostic factors of recurrent major depressive disorder that could potentially contribute to improving the management of MDD patients. For each individual patient, at the time of recruitment as part of a case-control study, information was collected on recent threatening life events, personality and social and occupational functioning, while blood samples were collected to genotype single nucleotide polymorphisms of vitamin D receptor (VDR), zinc transporter-3 (ZnT3), dopamine transporter-1 (DAT1), brain-derived neurotropic factor (BDNF), serotonin receptor 1A (HT1A) and 2A (HT2A) genes. Kaplan-Meier and Cox-regression were used to estimate hazard functions for recurrence of major depressive disorder. Individuals with severe MDD in previous major depressive episodes had five and a half times higher hazard of developing recurrence compared to mild and moderate MDD (HR = 5.565, 95% CI = 1.631–18.994, p = 0.006). Individuals who scored higher on social avoidance had three and a half times higher hazard of recurrence of MDD (HR = 3.525, 95% CI = 1.349–9.209; p = 0.010). There was significant interaction between ApaI +64978C>A single nucleotide polymorphism and severity. The hazard ratio increased by 6.4 times from mild and moderate to severe MDD for A/A genotype while that for C/A genotype increased by 11.3 times. Social avoidance and severity of depression at first episode were prognostic of recurrence. Screening for personality factors at first encounter with MDD patients needs to be considered as part of the clinical practice. For those at risk of recurrence in relation to social avoidance, the psychological intervention prescribed should be customized to focus on this modifiable factor. Prompt and appropriate management of severe MDD is recommended to reduce risk of recurrence.
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Frässle S, Marquand AF, Schmaal L, Dinga R, Veltman DJ, van der Wee NJA, van Tol MJ, Schöbi D, Penninx BWJH, Stephan KE. Predicting individual clinical trajectories of depression with generative embedding. NEUROIMAGE-CLINICAL 2020; 26:102213. [PMID: 32197140 PMCID: PMC7082217 DOI: 10.1016/j.nicl.2020.102213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/27/2020] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Patients with major depressive disorder (MDD) show variable clinical trajectories. Generative embedding (GE) is used to predict clinical trajectories in MDD patients. GE classifies patients with chronic depression vs. fast remission with 79% accuracy. GE provides mechanistic interpretability and outperforms conventional measures. Proof-of-concept that illustrates the potential of GE for clinical prediction.
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Richard Dinga
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Dick J Veltman
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden University, Leiden, The Netherlands
| | - Marie-José van Tol
- Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland
| | - Brenda W J H Penninx
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC, VU University, and Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Max Planck Institute for Metabolism Research, Cologne, Germany
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Kumagai N, Tajika A, Hasegawa A, Kawanishi N, Horikoshi M, Shimodera S, Kurata K, Chino B, Furukawa TA. Predicting recurrence of depression using lifelog data: an explanatory feasibility study with a panel VAR approach. BMC Psychiatry 2019; 19:391. [PMID: 31829206 PMCID: PMC6907185 DOI: 10.1186/s12888-019-2382-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 11/29/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Although depression has a high rate of recurrence, no prior studies have established a method that could identify the warning signs of its recurrence. METHODS We collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. We assessed depression and its recurrence using both the Kessler Psychological Distress Scale (K6) and the Patient Health Questionnaire-9. RESULTS A panel vector autoregressive analysis indicated that long sleep time was a important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks. CONCLUSIONS The panel vector autoregressive approach can identify the warning signs of depression recurrence; however, the convenient sampling of the present cohort may limit the scope towards drawing a generalised conclusion.
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Affiliation(s)
- Narimasa Kumagai
- grid.443473.3Department of Economics, Seinan Gakuin University, 6-2-92, Nishijin, Sawara-ku, Fukuoka, 814-8511 Japan
| | - Aran Tajika
- Department of Psychiatry, Kyoto University Hospital, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Akio Hasegawa
- 0000 0001 2291 1583grid.418163.9Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288 Japan
| | - Nao Kawanishi
- Sonas Inc., 6F, Grace Imas Building, 5-24-2, Hongo, Bunkyo-ku, Tokyo, 113-0033 Japan
| | - Masaru Horikoshi
- 0000 0004 1763 8916grid.419280.6National Center for Cognitive Behavior Therapy and Research, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi-cho, Kodaira, Tokyo, 187-8553 Japan
| | - Shinji Shimodera
- Ginza Shimodera Clinic, 8B-6-9-6 Ginza Chuo Ward, Tokyo, 104-0061 Japan ,0000 0004 0372 2033grid.258799.8Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine / School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Ken’ichi Kurata
- Kabe Mental Health Clinic, 4-6-2 Kabe, Asakita-ku, Hiroshima, 731-0221 Japan
| | - Bun Chino
- Ginza Taimei Clinic, 5-1-15 Ginza, Chuou-ku, Tokyo, 104-0061 Japan
| | - Toshi A. Furukawa
- 0000 0004 0372 2033grid.258799.8Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine / School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501 Japan
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Ross EL, Vijan S, Miller EM, Valenstein M, Zivin K. The Cost-Effectiveness of Cognitive Behavioral Therapy Versus Second-Generation Antidepressants for Initial Treatment of Major Depressive Disorder in the United States: A Decision Analytic Model. Ann Intern Med 2019; 171:785-795. [PMID: 31658472 PMCID: PMC7188559 DOI: 10.7326/m18-1480] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background Most guidelines for major depressive disorder recommend initial treatment with either a second-generation antidepressant (SGA) or cognitive behavioral therapy (CBT). Although most trials suggest that these treatments have similar efficacy, their health economic implications are uncertain. Objective To quantify the cost-effectiveness of CBT versus SGA for initial treatment of depression. Design Decision analytic model. Data Sources Relative effectiveness data from a meta-analysis of randomized controlled trials; additional clinical and economic data from other publications. Target Population Adults with newly diagnosed major depressive disorder in the United States. Time Horizon 1 to 5 years. Perspectives Health care sector and societal. Intervention Initial treatment with either an SGA or group and individual CBT. Outcome Measures Costs in 2014 U.S. dollars, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Results of Base-Case Analysis In model projections, CBT produced higher QALYs (3 days more at 1 year and 20 days more at 5 years) with higher costs at 1 year (health care sector, $900; societal, $1500) but lower costs at 5 years (health care sector, -$1800; societal, -$2500). Results of Sensitivity Analysis In probabilistic sensitivity analyses, SGA had a 64% to 77% likelihood of having an incremental cost-effectiveness ratio of $100 000 or less per QALY at 1 year; CBT had a 73% to 77% likelihood at 5 years. Uncertainty in the relative risk for relapse of depression contributed the most to overall uncertainty in the optimal treatment. Limitation Long-term trials comparing CBT and SGA are lacking. Conclusion Neither SGAs nor CBT provides consistently superior cost-effectiveness relative to the other. Given many patients' preference for psychotherapy over pharmacotherapy, increasing patient access to CBT may be warranted. Primary Funding Source Department of Veterans Affairs, National Institute of Mental Health.
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Affiliation(s)
- Eric L Ross
- Harvard Medical School and Massachusetts General Hospital, Boston, and McLean Hospital, Belmont, Massachusetts (E.L.R.)
| | - Sandeep Vijan
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, and University of Michigan Medical School, Ann Arbor, Michigan (S.V.)
| | - Erin M Miller
- University of Michigan Medical School, Ann Arbor, Michigan (E.M.M.)
| | - Marcia Valenstein
- University of Michigan Medical School and the Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan (M.V.)
| | - Kara Zivin
- University of Michigan Medical School, Center for Clinical Management Research, VA Ann Arbor Healthcare System, University of Michigan School of Public Health, and the Institute for Social Research, University of Michigan, Ann Arbor, Michigan (K.Z.)
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McPherson S, Hengartner MP. Long-term outcomes of trials in the National Institute for Health and Care Excellence depression guideline. BJPsych Open 2019; 5:e81. [PMID: 31685073 PMCID: PMC6737515 DOI: 10.1192/bjo.2019.65] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The forthcoming National Institute for Health and Care Excellence depression guideline reviews short-term outcomes for long-term depression. We present effect sizes for long-term outcomes in trials that report these data. Psychological therapies become more effective, whereas antidepressants become less effective over the long term. We review other forms of longitudinal research that support these findings.
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Affiliation(s)
- Susan McPherson
- Researcher, School of Health and Social Care, University of Essex, UK
| | - Michael P Hengartner
- Senior Lecturer and Researcher, School of Applied Psychology, Zurich University of Applied Sciences, Switzerland
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Serafini G, Santi F, Gonda X, Aguglia A, Fiorillo A, Pompili M, Carvalho AF, Amore M. Predictors of recurrence in a sample of 508 outpatients with major depressive disorder. J Psychiatr Res 2019; 114:80-87. [PMID: 31051436 DOI: 10.1016/j.jpsychires.2019.04.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 04/15/2019] [Accepted: 04/18/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Specific predictors of relapse/recurrence in major depressive disorder (MDD) have been identified but evidence across studies are inconsistent. This study aimed to identify the most relevant socio-demographic/clinical predictors of MDD recurrence in a sample of 508 outpatients. METHODS This naturalistic cohort study included 508 currently euthymic MDD patients (mean age = 54.1 ± 16.2) of which 53.9% had a single and 46.1% recurrent depressive episodes. A detailed data collection was performed and illness histories were retraced through clinical files and lifetime computerized medical records. RESULTS Compared to patients with single episode, MDD patients with recurrent episodes significantly differ regarding current age, gender, working status, positive history of psychiatric disorders in family, first-lifetime illness episode characteristics, first-episode and current psychotic symptoms, current melancholic features and seasonality, age at first treatment, duration of untreated illness, and comorbid cardiovascular/endocrinological conditions. However, after multivariate analyses controlling for current age, gender, educational level, working status differences, psychiatric conditions in family, and age of illness episode, recurrence was associated with older age (p ≤ .001), younger age at first treatment (p ≤ .005), being treated with previous psychoactive treatments (p .001), and longer duration of untreated illness (p .001). CONCLUSIONS The variables associated with MDD recurrence identified in the current study may aid in the stratification of patients who could benefit from more intensive maintenance treatments for MDD. However, clinicians should rapidly identify cases that are not likely to recur in order to avoid unnecessary treatments which are commonly considered as the standard of care.
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Affiliation(s)
- Gianluca Serafini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
| | - Francesca Santi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Xenia Gonda
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Semmelweis University, Budapest, Hungary
| | - Andrea Aguglia
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Andrea Fiorillo
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Suicide Prevention Center, Sant'Andrea Hospital, University of Rome, Rome, Italy
| | - André F Carvalho
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Mario Amore
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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Prediction of prolonged treatment course for depressive and anxiety disorders in an outpatient setting: The Leiden routine outcome monitoring study. J Affect Disord 2019; 247:81-87. [PMID: 30658244 DOI: 10.1016/j.jad.2018.12.035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/04/2018] [Accepted: 12/16/2018] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The aim of this study was to improve clinical identification of patients with a prolonged treatment course for depressive and anxiety disorders early in treatment. METHOD We conducted a cohort study in 1.225 adult patients with a depressive or anxiety disorders in psychiatric specialty care setting between 2007 and 2011, with at least two Brief Symptom Inventory (BSI) assessments within 6 months. With logistic regression, we modelled baseline age, gender, ethnicity, education, marital status, housing situation, employment status, psychiatric comorbidity and both baseline and 1st follow-up BSI scores to predict prolonged treatment course (>2 years). Based on the regression coefficients, we present an easy to use risk prediction score. RESULTS BSI at 1st follow-up proved to be a strong predictor for both depressive and anxiety disorders (OR = 2.17 (CI95% 1.73-2.74); OR = 2.52 (CI95% 1.86-3.23)). The final risk prediction score included BSI 1st follow-up and comorbid axis II disorder for depressive disorder, for anxiety disorders BSI 1st follow-up and age were included. For depressive disorders, for 28% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was60% (sensitivity 0.38, specificity 0.81). For anxiety disorders, for 35% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was 52% (sensitivity 0.55, specificity 0.75). CONCLUSIONS A high level of symptoms at 2-6 months of follow-up is a strong predictor for prolonged treatment course. This facilitates early identification of patients at risk of a prolonged course of treatment; in a relatively easy way by a self-assessed symptom severity.
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Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression. NEUROIMAGE-CLINICAL 2018; 20:1001-1009. [PMID: 30321791 PMCID: PMC6197328 DOI: 10.1016/j.nicl.2018.10.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 10/01/2018] [Accepted: 10/03/2018] [Indexed: 12/18/2022]
Abstract
Background Major Depressive Disorder (MDD) is a prevalent, disruptive illness. A majority of those with MDD are at high risk for recurrence and increased risk for morbidity and mortality. This study examined whether multimodal baseline (and retest) Cognitive Control performance and neuroimaging markers (task activation and neural connectivity between key brain nodes) could differentiate between those with and without future recurrence of a major depressive (MD) episode within one year. We hypothesized that performance and neuroimaging measures of Cognitive Control would identify markers that differ between these two groups. Methods A prospective cohort study of young adults (ages 18–23) with history (h) of early-onset MDD (N = 60), now remitted, and healthy young adults (N = 49). Baseline Cognitive Control measures of performance, task fMRI and resting state connectivity (and reliability retest 4–12 weeks later) were used to compare those with future recurrence of MDD (N = 21) relative to those without future recurrence of MDD (N = 34 with resilience). The measures tested were (1) Parametric Go/No-Go (PGNG) performance, and task activation for (2) PGNG Correct Rejections, (3) PGNG Commission errors, and (4 & 5), resting state connectivity analyses of Cognitive Control Network to and from subgenual anterior cingulate. Results Relative to other groups at baseline, the group with MDD Recurrence had less bilateral middle frontal gyrus activation during commission errors. MDD Recurrence exhibited greater connectivity of right middle frontal gyrus to subgenual anterior cingulate (SGAC). SGAC connectivity was also elevated in this group to numerous regions in the Cognitive Control Network. Moderate to strong ICCs were present from test to retest, and highest for rs-fMRI markers. There were modest, significant correlations between task, connectivity and behavioral markers that distinguished between groups. Conclusion Markers of Cognitive Control function could identify those with early course MD who are at risk for depression recurrence. Those at high risk for recurrence would benefit from maintenance or preventative treatments. Future studies could test and validate these markers as potential predictors, accounting for sample selection and bias in feature detection. Tools are needed to increase identification of MDD recurrence Cognitive control behavior and depression symptoms have been predictive of recurrence in prior studies, but with low accuracy In remitted Major Depressive Disorder, those who will go on to have future depressive episodes differed in cognitive control activation and connectivity Symptoms, performance, task activation, and seed-based connectivity can contribute to identification of risk for recurrence
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Buckman JEJ, Underwood A, Clarke K, Saunders R, Hollon SD, Fearon P, Pilling S. Risk factors for relapse and recurrence of depression in adults and how they operate: A four-phase systematic review and meta-synthesis. Clin Psychol Rev 2018; 64:13-38. [PMID: 30075313 PMCID: PMC6237833 DOI: 10.1016/j.cpr.2018.07.005] [Citation(s) in RCA: 195] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 02/16/2018] [Accepted: 07/21/2018] [Indexed: 12/23/2022]
Abstract
PURPOSE To review and synthesise prognostic indices that predict subsequent risk, prescriptive indices that moderate treatment response, and mechanisms that underlie each with respect to relapse and recurrence of depression in adults. RESULTS AND CONCLUSIONS Childhood maltreatment, post-treatment residual symptoms, and a history of recurrence emerged as strong prognostic indicators of risk and each could be used prescriptively to indicate who benefits most from continued or prophylactic treatment. Targeting prognostic indices or their "down-stream" consequences will be particularly beneficial because each is either a cause or a consequence of the causal mechanisms underlying risk of recurrence. The cognitive and neural mechanisms that underlie the prognostic indices are likely addressed by the effects of treatments that are moderated by the prescriptive factors. For example, psychosocial interventions that target the consequences of childhood maltreatment, extending pharmacotherapy or adapting psychological therapies to deal with residual symptoms, or using cognitive or mindfulness-based therapies for those with prior histories of recurrence. Future research that focuses on understanding causal pathways that link childhood maltreatment, or cognitive diatheses, to dysfunction in the neocortical and limbic pathways that process affective information and facilitate cognitive control, might result in more enduring effects of treatments for depression.
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Affiliation(s)
- J E J Buckman
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK.
| | - A Underwood
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - K Clarke
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - R Saunders
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - S D Hollon
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - P Fearon
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - S Pilling
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
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Deng Y, McQuoid DR, Potter GG, Steffens DC, Albert K, Riddle M, Beyer JL, Taylor WD. Predictors of recurrence in remitted late-life depression. Depress Anxiety 2018; 35:658-667. [PMID: 29749006 PMCID: PMC6035781 DOI: 10.1002/da.22772] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 03/22/2018] [Accepted: 04/23/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Late-life depression (LLD) is associated with a fragile antidepressant response and high recurrence risk. This study examined what measures predict recurrence in remitted LLD. METHODS Individuals of age 60 years or older with a Diagnostic and Statistical Manual - IV (DSM-IV) diagnosis of major depressive disorder were enrolled in the neurocognitive outcomes of depression in the elderly study. Participants received manualized antidepressant treatment and were followed longitudinally for an average of 5 years. Study analyses included participants who remitted. Measures included demographic and clinical measures, medical comorbidity, disability, life stress, social support, and neuropsychological testing. A subset underwent structural magnetic resonance imaging (MRI). RESULTS Of 241 remitted elders, approximately over 4 years, 137 (56.8%) experienced recurrence and 104 (43.2%) maintained remission. In the final model, greater recurrence risk was associated with female sex (hazard ratio [HR] = 1.536; confidence interval [CI] = 1.027-2.297), younger age of onset (HR = 0.990; CI = 0.981-0.999), higher perceived stress (HR = 1.121; CI = 1.022-1.229), disability (HR = 1.060; CI = 1.005-1.119), and less support with activities (HR = 0.885; CI = 0.812-0.963). Recurrence risk was also associated with higher Montgomery-Asberg Depression Rating Scale (MADRS) scores prior to censoring (HR = 1.081; CI = 1.033-1.131) and baseline symptoms of suicidal thoughts by MADRS (HR = 1.175; CI = 1.002-1.377) and sadness by Center for Epidemiologic Studies-Depression (HR = 1.302; CI, 1.080-1.569). Sex, age of onset, and suicidal thoughts were no longer associated with recurrence in a model incorporating report of multiple prior episodes (HR = 2.107; CI = 1.252-3.548). Neither neuropsychological test performance nor MRI measures of aging pathology were associated with recurrence. CONCLUSIONS Over half of the depressed elders who remitted experienced recurrence, mostly within 2 years. Multiple clinical and environmental measures predict recurrence risk. Work is needed to develop instruments that stratify risk.
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Affiliation(s)
- Yi Deng
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Douglas R. McQuoid
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA
| | - Guy G. Potter
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA
| | - David C. Steffens
- Department of Psychiatry, University of Connecticut Health Center, Farmington, CT, 06030, USA
| | - Kimberly Albert
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Meghan Riddle
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - John L. Beyer
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA
| | - Warren D. Taylor
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212, USA,Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, 37212, USA
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Gerhard DM, Duman RS. Rapid-Acting Antidepressants: Mechanistic Insights and Future Directions. Curr Behav Neurosci Rep 2018; 5:36-47. [PMID: 30034992 PMCID: PMC6051539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
PURPOSE OF REVIEW Ketamine produces rapid (within hours) antidepressant actions, even in patients considered treatment resistant, and even shows promise for suicidal ideation. Here, we review current research on the molecular and cellular mechanisms of ketamine and other novel rapid-acting antidepressants, and briefly explore gender differences in the pathophysiology and treatment of MDD. RECENT FINDINGS Ketamine, an NMDA receptor antagonist, increases BDNF release and synaptic connectivity, opposing the deficits caused by chronic stress and depression. Efforts are focused on the development of novel rapid agents that produce similar synaptic and rapid antidepressant actions, but without the side effects of ketamine. The impact of gender on the response to ketamine and other rapid-acting antidepressants is in early stages of investigation. SUMMARY The discovery that ketamine produces rapid therapeutic actions for depression and suicidal ideation represents a major breakthrough and much needed alternative to currently available medications. However, novel fast acting agents with fewer side effects are needed, as well as elucidation of the efficacy of these rapid-acting antidepressants for depression in women.
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Affiliation(s)
- Danielle M Gerhard
- Department of Psychiatry, Laboratory of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06508, USA
| | - Ronald S Duman
- Department of Psychiatry, Laboratory of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06508, USA
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Carhart-Harris RL, Bolstridge M, Day CMJ, Rucker J, Watts R, Erritzoe DE, Kaelen M, Giribaldi B, Bloomfield M, Pilling S, Rickard JA, Forbes B, Feilding A, Taylor D, Curran HV, Nutt DJ. Psilocybin with psychological support for treatment-resistant depression: six-month follow-up. Psychopharmacology (Berl) 2018; 235:399-408. [PMID: 29119217 PMCID: PMC5813086 DOI: 10.1007/s00213-017-4771-x] [Citation(s) in RCA: 458] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 10/19/2017] [Indexed: 01/30/2023]
Abstract
RATIONALE Recent clinical trials are reporting marked improvements in mental health outcomes with psychedelic drug-assisted psychotherapy. OBJECTIVES Here, we report on safety and efficacy outcomes for up to 6 months in an open-label trial of psilocybin for treatment-resistant depression. METHODS Twenty patients (six females) with (mostly) severe, unipolar, treatment-resistant major depression received two oral doses of psilocybin (10 and 25 mg, 7 days apart) in a supportive setting. Depressive symptoms were assessed from 1 week to 6 months post-treatment, with the self-rated QIDS-SR16 as the primary outcome measure. RESULTS Treatment was generally well tolerated. Relative to baseline, marked reductions in depressive symptoms were observed for the first 5 weeks post-treatment (Cohen's d = 2.2 at week 1 and 2.3 at week 5, both p < 0.001); nine and four patients met the criteria for response and remission at week 5. Results remained positive at 3 and 6 months (Cohen's d = 1.5 and 1.4, respectively, both p < 0.001). No patients sought conventional antidepressant treatment within 5 weeks of psilocybin. Reductions in depressive symptoms at 5 weeks were predicted by the quality of the acute psychedelic experience. CONCLUSIONS Although limited conclusions can be drawn about treatment efficacy from open-label trials, tolerability was good, effect sizes large and symptom improvements appeared rapidly after just two psilocybin treatment sessions and remained significant 6 months post-treatment in a treatment-resistant cohort. Psilocybin represents a promising paradigm for unresponsive depression that warrants further research in double-blind randomised control trials.
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Affiliation(s)
- R L Carhart-Harris
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
| | - M Bolstridge
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - C M J Day
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - J Rucker
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
- The Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South West London and St George's Mental Health NHS Trust, London, UK
| | - R Watts
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - D E Erritzoe
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - M Kaelen
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - B Giribaldi
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - M Bloomfield
- Division of Psychiatry, University College London and Clinical Psychopharmacology Unit, University College London, London, UK
| | - S Pilling
- Clinical Psychology and Clinical Effectiveness, University College London, London, UK
| | - J A Rickard
- Barts Health Pharmaceuticals, Barts Health NHS Trust, the Royal London Hospital, London, UK
| | - B Forbes
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - A Feilding
- The Beckley Foundation, Beckley Park, Oxford, UK
| | - D Taylor
- Pharmacy and Pathology, South London and Maudsley NHS Foundation Trust, London, UK
| | - H V Curran
- Clinical Psychology and Clinical Effectiveness, University College London, London, UK
- Clinical Psychopharmacology Unit, University College London, London, UK
| | - D J Nutt
- Psychedelic Research Group, Centre for Neuropsychopharmacology, Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
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Browning M. Symptom trajectories in discontinuation trials. Lancet Psychiatry 2017; 4:176-178. [PMID: 28189574 DOI: 10.1016/s2215-0366(17)30054-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 01/30/2017] [Indexed: 11/25/2022]
Affiliation(s)
- Michael Browning
- Department of Psychiatry, University of Oxford and Oxford Health NHS Trust, Oxford OX3 7JX, UK.
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