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Sackeim HA, Aaronson ST, Bunker MT, Conway CR, George MS, McAlister-Williams RH, Prudic J, Thase ME, Young AH, Rush AJ. Update on the assessment of resistance to antidepressant treatment: Rationale for the Antidepressant Treatment History Form: Short Form-2 (ATHF-SF2). J Psychiatr Res 2024; 176:325-337. [PMID: 38917723 DOI: 10.1016/j.jpsychires.2024.05.046] [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: 01/28/2024] [Revised: 04/09/2024] [Accepted: 05/29/2024] [Indexed: 06/27/2024]
Abstract
All definitions of treatment-resistant depression (TRD) require that patients have experienced insufficient benefit from one or more adequate antidepressant trials. Thus, identifying "failed, adequate trials" is key to the assessment of TRD. The Antidepressant Treatment History Form (ATHF) was one of the first and most widely used instruments that provided objective criteria in making these assessments. The original ATHF was updated in 2018 to the ATHF-SF, changing to a checklist format for scoring, and including specific pharmacotherapy, brain stimulation, and psychotherapy interventions as potentially adequate antidepressant treatments. The ATHF-SF2, presented here, is based on the consensus of the ATHF workgroup about the novel interventions introduced since the last revision and which should/should not be considered effective treatments for major depressive episodes. This document describes the rationale for these choices and, for each intervention, the minimal criteria for determining the adequacy of treatment administration. The Supplementary Material that accompanies this article provide the Scoring Checklist, Data Collection Forms (current episode and composite of previous episodes), and Instruction Manual for the ATHF-SF2.
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Affiliation(s)
- Harold A Sackeim
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, USA.
| | - Scott T Aaronson
- Sheppard Pratt Health System and Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | | | - Charles R Conway
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Mark S George
- Departments of Psychiatry,Neurology,and Neuroscience, Medical University of South Carolina and Ralph H. Johnson VA Medical Center, Charleston, SC, USA
| | - R Hamish McAlister-Williams
- Northern Centre for Mood Disorders, Translational and Clinical Research Institute, Newcastle University, UK; Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Joan Prudic
- New York State Psychiatric Institute and Department of Psychiatry, Columbia University, New York, NY, USA
| | - Michael E Thase
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, and South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, UK
| | - A John Rush
- Duke-NUS Medical School, Singapore; Duke University, Durham, NC, USA; Texas Tech University, Permian Basin, TX, USA
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Arnold PIM, Janzing JGE, Hommersom A. Machine learning for antidepressant treatment selection in depression. Drug Discov Today 2024; 29:104068. [PMID: 38925472 DOI: 10.1016/j.drudis.2024.104068] [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: 09/16/2023] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.
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Affiliation(s)
- Prehm I M Arnold
- Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands.
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Wu YK, Su YA, Zhu LL, Yan C, Li JT, Lin JY, Chen J, Chen L, Li K, Stein DJ, Si TM. A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder. Transl Psychiatry 2024; 14:136. [PMID: 38443354 PMCID: PMC10915152 DOI: 10.1038/s41398-024-02838-7] [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: 03/11/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.
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Affiliation(s)
- Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Lin-Lin Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing-Yu Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - JingXu Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Lin Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, 100101, China
| | - Dan J Stein
- Neuroscience Institute, Department of Psychiatry and Mental Health, South African Medical Research Council (SAMRC), Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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Sackeim HA, Aaronson ST, Carpenter LL, Hutton TM, Pages K, Lucas L, Chen B. When to hold and when to fold: Early prediction of nonresponse to transcranial magnetic stimulation in major depressive disorder. Brain Stimul 2024; 17:272-282. [PMID: 38458381 DOI: 10.1016/j.brs.2024.02.019] [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: 01/13/2024] [Revised: 02/21/2024] [Accepted: 02/28/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Determining when to recommend a change in treatment regimen due to insufficient improvement is a common challenge in therapeutics. METHODS In a sample of 7215 patients with major depressive disorder treated with transcranial magnetic stimulation (TMS) and with PHQ-9 scores before, during and after the course, 3 groups were identified based on number of acute course sessions: exactly 36 sessions (N = 3591), more than 36 sessions (N = 975), and less than 36 sessions (N = 2649). Two techniques were used to determine thresholds for percentage change in PHQ-9 scores at assessments after 10, 20, and 30 sessions that optimized prediction of endpoint response status: the Youden index and fixing the false positive rate at 10%. Positive and negative predictive values were calculated to assess the accuracy of identifying final nonresponders and responders, respectively. RESULTS There was greater accuracy in predicting final response than nonresponse, especially in the groups that had at least 36 sessions. Substantial proportions of patients with low levels of early improvement were classified as responders at the end of treatment. LIMITATIONS The findings should be validated with clinician ratings using a more comprehensive depression severity scale. CONCLUSIONS Manifesting clinical improvement early in the TMS course is strongly predictive of final status as a responder, while lack of early improvement is a relatively poor indicator of final nonresponse status. The predictive value of lack of early symptomatic improvement is too low to make reliable recommendations regarding changes in treatment regimen.
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Affiliation(s)
- Harold A Sackeim
- Department of Psychiatry, Columbia University, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA.
| | - Scott T Aaronson
- Sheppard Pratt Health System, Baltimore, MD, USA; Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Linda L Carpenter
- Butler Hospital, Providence, RI, USA; Brown University Department of Psychiatry and Human Behavior, Providence, RI, USA
| | | | | | | | - Bing Chen
- NAMSA, St. Louis Park, Minneapolis, MN, USA
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Vos CF, Birkenhäger TK, Nolen WA, van den Broek WW, ter Hark SE, Schellekens AF, Verkes RJ, Janzing JG. The Relationship of Early Sleep Improvement With Response to Pharmacotherapy in Unipolar Psychotic Depression. J Clin Psychopharmacol 2023; 43:486-492. [PMID: 37930199 PMCID: PMC10662627 DOI: 10.1097/jcp.0000000000001756] [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: 03/24/2023] [Accepted: 07/08/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND Since insomnia and depression are interrelated, improved sleep early in antidepressant pharmacotherapy may predict a positive treatment outcome. We investigated whether early insomnia improvement (EII) predicted treatment outcome in psychotic depression (PD) and examined if there was an interaction effect between EII and treatment type to assess if findings were treatment-specific. METHODS This study is a secondary analysis of a randomized trial comparing 7 weeks treatment with the antidepressants venlafaxine, imipramine and venlafaxine plus the antipsychotic quetiapine in PD ( n = 114). Early insomnia improvement, defined as ≥20% reduced insomnia after 2 weeks, was assessed by the Hamilton Rating Scale for Depression (HAM-D-17). Associations between EII and treatment outcome were examined using logistic regressions. Subsequently, we added interaction terms between EII and treatment type to assess interaction effects. The predictive value of EII was compared with early response on overall depression (≥20% reduced HAM-D-17 score after 2 weeks). RESULTS EII was associated with response (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.7-23.4; P = <0.001), remission of depression (OR, 6.1; 95% CI, 1.6-22.3; P = 0.009), and remission of psychosis (OR, 4.1; 95% CI, 1.6-10.9; P = 0.004). We found no interaction effects between EII and treatment type on depression outcome. Early insomnia improvement and early response on overall depression had a comparable predictive ability for treatment outcome. CONCLUSIONS Early insomnia improvement was associated with a positive outcome in pharmacotherapy of PD, regardless of the medication type. Future studies are needed to confirm our findings and to examine the generalizability of EII as predictor in treatment of depression.
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Affiliation(s)
- Cornelis F. Vos
- From the Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Tom K. Birkenhäger
- Department of Psychiatry, Erasmus University Medical Centre, Rotterdam, the Netherlands
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Willem A. Nolen
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Sophie E. ter Hark
- From the Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Arnt F.A. Schellekens
- From the Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Robbert-Jan Verkes
- From the Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Joost G.E. Janzing
- From the Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Tian S, Wang Q, Zhang S, Chen Z, Dai Z, Zhang W, Yao Z, Lu Q. Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder. J Affect Disord 2023; 340:751-757. [PMID: 37597781 DOI: 10.1016/j.jad.2023.08.096] [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: 04/21/2023] [Revised: 08/05/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response. METHODS Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions. RESULTS After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969). LIMITATIONS Relatively small sample size and not a double-blind design. CONCLUSIONS The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.
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Affiliation(s)
- Shui Tian
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Department of Medical Psychology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Siqi Zhang
- Insitut des Sciences Cognitives, Marc Jeannerod, CNRS, France
| | - Zhilu Chen
- Department of Psychiatry, the Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Wei Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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Cai X, Wu M, Zhang Z, Liu H, Huang S, Song J, Ren S, Huang Y. Electroacupuncture alleviated depression‐like behaviors in ventromedial prefrontal cortex of chronic unpredictable mild stress‐induced rats: Increasing synaptic transmission and phosphorylating dopamine transporter. CNS Neurosci Ther 2023. [PMID: 37002793 PMCID: PMC10401110 DOI: 10.1111/cns.14200] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023] Open
Abstract
AIMS Electroacupuncture (EA) shows advantages in both clinical practice and depression animal models. Dopaminergic-related dysfunction in the prefrontal cortex (PFC) may be a hidden antidepressant mechanism of EA, where dopamine transporter (DAT) plays an essential role. This study aimed to investigate the synaptic transmission and DAT-related changes of EA in depression. METHODS Male Sprague-Dawley rats were subjected to 3-week chronic unpredictable mild stress (CUMS). The successfully modeled rats were then randomly and equally assigned to CUMS, selective serotonin reuptake inhibitor (SSRI), and EA or SSRI + EA groups, followed by a 2-week treatment respectively. After monitoring body weight and behavioral tests of all rats, the ventromedial PFC (vmPFC) tissue was collected for electrophysiology and the expression detection of DAT, phosphorylated DAT (p-DAT), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), and trace amine-associated receptor 1 (TAAR1). RESULTS Depressive-like behaviors induced by CUMS were alleviated by EA, SSRI, and SSRI + EA treatments through behavioral tests. Compared with CUMS group, EA improved synaptic transmission in vmPFC by upregulating spontaneous excitatory postsynaptic currents amplitude. Molecularly, EA reversed the increased total DAT and p-DAT expression as well as the decreased ratio of p-DAT/total DAT along with the activation of TAAR1, cAMP, and PKA in vmPFC. CONCLUSION We speculated that the antidepressant effect of EA was associated with enhanced synaptic transmission in vmPFC, and the upregulated phosphorylation of DAT relevant to TAAR1, cAMP, and PKA may be the potential mechanism.
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Affiliation(s)
- Xiaowen Cai
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
| | - Mei Wu
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
| | - Zhinan Zhang
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
| | - Huacong Liu
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
| | - Shengtao Huang
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
| | - Jia Song
- Guangdong‐Hong Kong‐Macao Greater Bay Area Center for Brain Science and Brain‐Inspired Intelligence, Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders Southern Medical University Guangzhou 510515 Guangdong China
| | - Siqiang Ren
- Guangdong‐Hong Kong‐Macao Greater Bay Area Center for Brain Science and Brain‐Inspired Intelligence, Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders Southern Medical University Guangzhou 510515 Guangdong China
| | - Yong Huang
- School of Traditional Chinese Medicine Southern Medical University Guangzhou 510515 Guangdong China
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Kim HK, Blumberger DM, Karp JF, Lenze E, Reynolds CF, Mulsant BH. Venlafaxine XR treatment for older patients with major depressive disorder: decision trees for when to change treatment. EVIDENCE-BASED MENTAL HEALTH 2022; 25:156-162. [PMID: 36100357 PMCID: PMC10134194 DOI: 10.1136/ebmental-2022-300479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/31/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Predictors of antidepressant response in older patients with major depressive disorder (MDD) need to be confirmed before they can guide treatment. OBJECTIVE To create decision trees for early identification of older patients with MDD who are unlikely to respond to 12 weeks of antidepressant treatment, we analysed data from 454 older participants treated with venlafaxine XR (150-300 mg/day) for up to 12 weeks in the Incomplete Response in Late-Life Depression: Getting to Remission study. METHODS We selected the earliest decision point when we could detect participants who had not yet responded (defined as >50% symptom improvement) but would do so after 12 weeks of treatment. Using receiver operating characteristic models, we created two decision trees to minimise either false identification of future responders (false positives) or false identification of future non-responders (false negatives). These decision trees integrated baseline characteristics and treatment response at the early decision point as predictors. FINDING We selected week 4 as the optimal early decision point. Both decision trees shared minimal symptom reduction at week 4, longer episode duration and not having responded to an antidepressant previously as predictors of non-response. Test negative predictive values of the leftmost terminal node of the two trees were 77.4% and 76.6%, respectively. CONCLUSION Our decision trees have the potential to guide treatment in older patients with MDD but they require to be validated in other larger samples. CLINICAL IMPLICATIONS Once confirmed, our findings may be used to guide changes in antidepressant treatment in older patients with poor early response.
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Affiliation(s)
| | - Daniel M Blumberger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jordan F Karp
- Department of Psychiatry, University of Arizona, Tucson, Arizona, USA
| | - Eric Lenze
- Department of Psychiatry, University of Washington, St. Louis, Missouri, USA
| | - Charles F Reynolds
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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de Vries YA, Harris MG, Vigo D, Chiu WT, Sampson NA, Al-Hamzawi A, Alonso J, Andrade LH, Benjet C, Bruffaerts R, Bunting B, de Almeida JMC, de Girolamo G, Florescu S, Gureje O, Haro JM, Hu C, Karam EG, Kawakami N, Kovess-Masfety V, Lee S, Moskalewicz J, Navarro-Mateu F, Ojagbemi A, Posada-Villa J, Scott K, Torres Y, Zarkov Z, Nierenberg A, Kessler RC, de Jonge P. Perceived helpfulness of treatment for specific phobia: Findings from the World Mental Health Surveys. J Affect Disord 2021; 288:199-209. [PMID: 33940429 PMCID: PMC8154701 DOI: 10.1016/j.jad.2021.04.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/05/2021] [Accepted: 04/07/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Although randomized trials show that specific phobia treatments can be effective, it is unclear whether patients experience treatment as helpful in clinical practice. We investigated this issue by assessing perceived treatment helpfulness for specific phobia in a cross-national epidemiological survey. METHODS Cross-sectional population-based WHO World Mental Health (WMH) surveys in 24 countries (n=112,507) assessed lifetime specific phobia. Respondents who met lifetime criteria were asked whether they ever received treatment they considered helpful and the number of professionals seen up to the time of receiving helpful treatment. Discrete-event survival analysis was used to calculate conditional-cumulative probabilities of obtaining helpful treatment across number of professionals seen and of persisting in help-seeking after prior unhelpful treatment. RESULTS 23.0% of respondents reported receiving helpful treatment from the first professional seen, whereas cumulative probability of receiving helpful treatment was 85.7% after seeing up to 9 professionals. However, only 14.7% of patients persisted in seeing up to 9 professionals, resulting in the proportion of patients ever receiving helpful treatment (47.5%) being much lower than it could have been with persistence in help-seeking. Few predictors were found either of perceived helpfulness or of persistence in help-seeking after earlier unhelpful treatments. LIMITATIONS Retrospective recall and lack of information about either types of treatments received or objective symptomatic improvements limit results. CONCLUSIONS Despite these limitations, results suggest that helpfulness of specific phobia treatment could be increased, perhaps substantially, by increasing patient persistence in help-seeking after earlier unhelpful treatments. Improved understanding is needed of barriers to help-seeking persistence.
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Affiliation(s)
- Ymkje Anna de Vries
- Department of Developmental Psychology, University of Groningen, Groningen, NL; Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, Groningen, NL
| | - Meredith G. Harris
- School of Public Health, The University of Queensland, Herston, QLD 4006, Australia; Queensland Centre for Mental Health Research, The Park Centre for Mental Health, QLD 4072, Australia
| | - Daniel Vigo
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Wai Tat Chiu
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
| | - Nancy A. Sampson
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Al-Hamzawi
- College of Medicine, Al-Qadisiya University, Diwaniya governorate, Iraq
| | - Jordi Alonso
- Health Services Research Unit, IMIM-Hospital del Mar Medical Research Institute, Barcelona, Spain; CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Pompeu Fabra University (UPF), Barcelona, Spain
| | - Laura H. Andrade
- Núcleo de Epidemiologia Psiquiatrica - LIM 23, Instituto de Psiquiatria Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, Brazil Section of Psychiatric Epidemiology - LIM 23, Institute of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil
| | - Corina Benjet
- Department of Epidemiologic and Psychosocial Research, National Institute of Psychiatry Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Ronny Bruffaerts
- Universitair Psychiatrisch Centrum - Katholieke Universiteit Leuven (UPC-KUL), Campus Gasthuisberg, Leuven, Belgium
| | - Brendan Bunting
- School of Psychology, Ulster University, Londonderry, United Kingdom
| | - José Miguel Caldas de Almeida
- Lisbon Institute of Global Mental Health and Chronic Diseases Research Center (CEDOC), NOVA Medical School
- Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
| | | | - Silvia Florescu
- National School of Public Health, Management and Development, Bucharest, Romania
| | - Oye Gureje
- Department of Psychiatry, University College Hospital, Ibadan, Nigeria
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Sant Boi de Llobregat, Barcelona, Spain; Department of Psychology, College of Education, King Saud University, Riyadh, Saudi Arabia
| | - Chiyi Hu
- Shenzhen Institute of Mental Health & Shenzhen Kangning Hospital, Shenzhen, China
| | - Elie G. Karam
- Department of Psychiatry and Clinical Psychology, St George Hospital University Medical Center, Balamand University, Faculty of Medicine, Beirut, Lebanon; Institute for Development, Research, Advocacy and Applied Care (IDRAAC), Beirut, Lebanon
| | - Norito Kawakami
- Department of Mental Health, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Viviane Kovess-Masfety
- Ecole des Hautes Etudes en Santé Publique (EHESP), EA 4057, Paris Descartes University, Paris, France
| | - Sing Lee
- Department of Psychiatry, Chinese University of Hong Kong, Tai Po, Hong Kong
| | | | - Fernando Navarro-Mateu
- UDIF-SM, Servicio Murciano de Salud. IMIB-Arrixaca. CIBERESP-Murcia, Región de Murcia, Spain
| | - Akin Ojagbemi
- Department of Psychiatry, University of Ibadan, Nigeria
| | - José Posada-Villa
- Colegio Mayor de Cundinamarca University, Faculty of Social Sciences, Bogota, Colombia (Cundinamarca University, calle 28 # 5B 02, Bogotá, 11001000 (zip), Colombia)
| | - Kate Scott
- Department of Psychological Medicine, University of Otago, Dunedin, Otago, New Zealand
| | - Yolanda Torres
- Center for Excellence on Research in Mental Health, CES University, Medellin, Colombia
| | - Zahari Zarkov
- Department of Mental Health, National Center of Public Health and Analyses, Sofia, Bulgaria
| | - Andrew Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
| | - Peter de Jonge
- Department of Developmental Psychology, University of Groningen, Groningen, NL; Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, Groningen, NL
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The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial. Neuropsychopharmacology 2021; 46:1307-1314. [PMID: 33637837 PMCID: PMC8134561 DOI: 10.1038/s41386-021-00981-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 11/25/2022]
Abstract
Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.
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11
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Xiao L, Zhu X, Gillespie A, Feng Y, Zhou J, Chen X, Gao Y, Wang X, Ma X, Gao C, Xie Y, Pan X, Bai Y, Xu X, Wang G, Chen R. Effectiveness of mirtazapine as add-on to paroxetine v. paroxetine or mirtazapine monotherapy in patients with major depressive disorder with early non-response to paroxetine: a two-phase, multicentre, randomized, double-blind clinical trial. Psychol Med 2021; 51:1166-1174. [PMID: 31931894 DOI: 10.1017/s0033291719004069] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND This study aimed to examine the efficacy of combining paroxetine and mirtazapine v. switching to mirtazapine, for patients with major depressive disorder (MDD) who have had an insufficient response to SSRI monotherapy (paroxetine) after the first 2 weeks of treatment. METHODS This double-blind, randomized, placebo-controlled, three-arm study recruited participants from five hospitals in China. Eligible participants were aged 18-60 years with MDD of at least moderate severity. Participants received paroxetine during a 2-week open-label phase and patients who had not achieved early improvement were randomized to paroxetine, mirtazapine or paroxetine combined with mirtazapine for 6 weeks. The primary outcome was improvement on the Hamilton Rating Scale for Depression 17-item (HAMD-17) scores 6 weeks after randomization. RESULTS A total of 204 patients who showed early non-response to paroxetine monotherapy were randomly assigned to receive either mirtazapine and placebo (n = 68), paroxetine and placebo (n = 68) or mirtazapine and paroxetine (n = 68), with 164 patients completing the outcome assessment. At week 8, the least squares (LS) mean change of HAMD-17 scores did not significantly differ among the three groups, (12.98 points) in the mirtazapine group, (12.50 points) in the paroxetine group and (13.27 points) in the mirtazapine plus paroxetine combination group. Participants in the paroxetine monotherapy group were least likely to experience adverse effects. CONCLUSIONS After 8 weeks follow-up, paroxetine monotherapy, mirtazapine monotherapy and paroxetine/mirtazapine combination therapy were equally effective in non-improvers at 2 weeks. The results of this trial do not support a recommendation to routinely offer additional treatment or a switch in treatment strategies for MDD patients who do not show early improvement after 2 weeks of antidepressant treatment.
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Affiliation(s)
- Le Xiao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xuequan Zhu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Amy Gillespie
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuanyuan Gao
- Department of Psychiatry, The First Hospital of Hebei Medical University, Hebei, China
| | - Xueyi Wang
- Department of Psychiatry, The First Hospital of Hebei Medical University, Hebei, China
| | - Xiancang Ma
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an, China
| | - Chengge Gao
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an, China
| | - Yunshi Xie
- Department of Neurology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Xiaoping Pan
- Department of Neurology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yan Bai
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Runsen Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Department of Psychiatry, University of Oxford, Oxford, UK
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12
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Jones BDM, Husain MI, Mulsant BH. The use of sequential pharmacotherapy for the treatment of acute major depression: a scoping review. Expert Opin Pharmacother 2021; 22:1005-1014. [PMID: 33612048 DOI: 10.1080/14656566.2021.1878144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Major Depressive Disorder (MDD) is a chronic, relapsing, and remitting disorder affecting over 250 million persons each year worldwide. More than 50% of the patients do not respond to their initial antidepressant treatment and may benefit from sequential pharmacotherapy for the acute treatment of their MDD. Although guidelines outline options for next-step treatments, there is a paucity of evidence to select specific second- or third-step treatments. AREAS COVERED This scoping review synthesizes and discusses available evidence for sequential pharmacotherapy for MDD. MEDLINE was searched from inception to 7 July 2020; 4490 studies were identified. We selected meta-analyses and reports on clinical trials that were judged to inform the sequential selection of pharmacotherapy for MDD. EXPERT OPINION Most relevant published trials are focused on, and support, the use of augmentation pharmacotherapy. There is also some support for other strategies such as combining or switching antidepressants. In the future, more studies need to directly compare these sequential options. To provide more personalized treatment within the framework of precision psychiatry, these studies should include an assessment of moderators and mediators ('mechanism') of antidepressant response.
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Affiliation(s)
- Brett D M Jones
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - M Ishrat Husain
- Department of Psychiatry, University of Toronto, Toronto, Canada.,General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Toronto, Canada
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, Canada.,Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Canada
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13
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Malhi GS, Bell E, Bassett D, Boyce P, Bryant R, Hazell P, Hopwood M, Lyndon B, Mulder R, Porter R, Singh AB, Murray G. The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders. Aust N Z J Psychiatry 2021; 55:7-117. [PMID: 33353391 DOI: 10.1177/0004867420979353] [Citation(s) in RCA: 246] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVES To provide advice and guidance regarding the management of mood disorders, derived from scientific evidence and supplemented by expert clinical consensus to formulate s that maximise clinical utility. METHODS Articles and information sourced from search engines including PubMed, EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (e.g. books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Relevant information was appraised and discussed in detail by members of the mood disorders committee, with a view to formulating and developing consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous consultation and external review involving: expert and clinical advisors, key stakeholders, professional bodies and specialist groups with interest in mood disorders. RESULTS The Royal Australian and New Zealand College of Psychiatrists mood disorders clinical practice guidelines 2020 (MDcpg2020) provide up-to-date guidance regarding the management of mood disorders that is informed by evidence and clinical experience. The guideline is intended for clinical use by psychiatrists, psychologists, primary care physicians and others with an interest in mental health care. CONCLUSION The MDcpg2020 builds on the previous 2015 guidelines and maintains its joint focus on both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus. MOOD DISORDERS COMMITTEE Gin S Malhi (Chair), Erica Bell, Darryl Bassett, Philip Boyce, Richard Bryant, Philip Hazell, Malcolm Hopwood, Bill Lyndon, Roger Mulder, Richard Porter, Ajeet B Singh and Greg Murray.
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Affiliation(s)
- Gin S Malhi
- The University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, NSW, Australia.,Academic Department of Psychiatry, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - Erica Bell
- The University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, NSW, Australia.,Academic Department of Psychiatry, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | | | - Philip Boyce
- Department of Psychiatry, Westmead Hospital and the Westmead Clinical School, Wentworthville, NSW, Australia.,Discipline of Psychiatry, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Richard Bryant
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Philip Hazell
- Discipline of Psychiatry, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Malcolm Hopwood
- Department of Psychiatry, University of Melbourne and Professorial Psychiatry Unit, Albert Road Clinic, Melbourne, VIC, Australia
| | - Bill Lyndon
- The University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, NSW, Australia
| | - Roger Mulder
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Richard Porter
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Ajeet B Singh
- The Geelong Clinic Healthscope, IMPACT - Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, VIC, Australia
| | - Greg Murray
- Centre for Mental Health, Swinburne University of Technology, Hawthorn, VIC, Australia
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14
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Younes N, Claude LA, Paoletti X. Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues. Front Psychiatry 2021; 12:644980. [PMID: 34393841 PMCID: PMC8360265 DOI: 10.3389/fpsyt.2021.644980] [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: 12/22/2020] [Accepted: 06/23/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction: Individual participant data meta-analyses (IPD-MAs) include the raw data from relevant randomised clinical trials (RCTs) and involve secondary analyses of the data. Performed since the late 1990s, ~50 such meta-analyses have been carried out in psychiatry, mostly in the field of treatment. IPD-MAs are particularly relevant for three objectives: (1) evaluation of the average effect of an intervention by combining effects from all included trials, (2) evaluation of the heterogeneity of an intervention effect and sub-group analyses to approach personalised psychiatry, (3) mediation analysis or surrogacy evaluation to replace a clinical (final) endpoint for the evaluation of new treatments with intermediate or surrogate endpoints. The objective is to describe the interest and the steps of an IPD-MA method applied to the field of psychiatric therapeutic research. Method: The method is described in three steps. First, the identification of the relevant trials with an explicit description of the inclusion/exclusion criteria for the RCT to be incorporated in the IPD-MA and a definition of the intervention, the population, the context and the relevant points (outcomes or moderators). Second, the data management with the standardisation of collected variables and the evaluation and the assessment of the risk-of-bias for each included trial and of the global risk. Third, the statistical analyses and their interpretations, depending on the objective of the meta-analysis. All steps are illustrated with examples in psychiatry for treatment issues, excluding study protocols. Conclusion: The meta-analysis of individual patient data is challenging. Only strong collaborations between all stakeholders can make such a process efficient. An "ecosystem" that includes all stakeholders (questions of interest prioritised by the community, funders, trialists, journal editors, institutions, …) is required. International medical societies can play a central role in favouring the emergence of such communities.
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Affiliation(s)
- Nadia Younes
- Université Versailles Saint Quentin, Université Paris Saclay, CESP, Team DevPsy, Villejuif, France.,Centre Hospitalier Versailles, Service Hospitalo-Universitaire de Psychiatrie de l'Adulte et d'Addictologie, Le Chesnay, France.,UFR Sciences de la Santé S Veil, Université Versailles Saint Quentin, Paris Saclay, Gif-sur-Yvette, France
| | - Laurie-Anne Claude
- Université Versailles Saint Quentin, Université Paris Saclay, CESP, Team DevPsy, Villejuif, France.,Centre Hospitalier Versailles, Service Hospitalo-Universitaire de Psychiatrie de l'Adulte et d'Addictologie, Le Chesnay, France
| | - Xavier Paoletti
- UFR Sciences de la Santé S Veil, Université Versailles Saint Quentin, Paris Saclay, Gif-sur-Yvette, France.,Institut Curie, Biostatistics, Team Statistical Methods for Precision Medicine, St Cloud, France.,INSERM U900, Statistical Methods for Personalised Medicine Team (STAMPM), St Cloud, France
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15
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Kang HJ, Kim KT, Yoo KH, Park Y, Kim JW, Kim SW, Shin IS, Kim JH, Kim JM. Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants. Int J Mol Sci 2020; 21:ijms21144884. [PMID: 32664413 PMCID: PMC7402334 DOI: 10.3390/ijms21144884] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting.
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Affiliation(s)
- Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Ki-Tae Kim
- Department of Laboratory Medicine, Korea University Anam Hospital, Seoul 02841, Korea;
| | - Kyung-Hun Yoo
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
| | - Yoomi Park
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Ju Han Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
- Correspondence: (J.H.K.); (J.-M.K.)
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
- Correspondence: (J.H.K.); (J.-M.K.)
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16
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Fantasia HC. Esketamine Nasal Spray for Treatment-Resistant Depression. Nurs Womens Health 2020; 24:228-232. [PMID: 32387141 DOI: 10.1016/j.nwh.2020.03.004] [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: 12/18/2019] [Revised: 02/12/2020] [Accepted: 03/01/2020] [Indexed: 06/11/2023]
Abstract
Major depression affects millions of adults in the United States, and women are affected at twice the rate of men. Approximately 30% of individuals may continue to experience depression symptoms despite treatment with oral antidepressants. In March 2019, the U.S. Food and Drug Administration approved esketamine (Spravato), which is specifically indicated for treatment-resistant depression. Available as a nasal spray in a health care office or facility, esketamine has the potential to fill an unmet need for individuals who continue to experience depression with conventional treatment. Symptom improvement may be seen as rapidly as 1 week after treatment initiation. This article provides an overview of esketamine, including administration, adverse effects, and practice implications for women's health nurses.
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17
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Kraus C, Kadriu B, Lanzenberger R, Zarate Jr. CA, Kasper S. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry 2019; 9:127. [PMID: 30944309 PMCID: PMC6447556 DOI: 10.1038/s41398-019-0460-3] [Citation(s) in RCA: 227] [Impact Index Per Article: 45.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/10/2019] [Accepted: 02/11/2019] [Indexed: 02/07/2023] Open
Abstract
Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.
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Affiliation(s)
- Christoph Kraus
- 0000 0000 9259 8492grid.22937.3dDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria ,0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Bashkim Kadriu
- 0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Rupert Lanzenberger
- 0000 0000 9259 8492grid.22937.3dDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Carlos A. Zarate Jr.
- 0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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18
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Curkovic M, Kosec A, Savic A. Re-evaluation of Significance and the Implications of Placebo Effect in Antidepressant Therapy. Front Psychiatry 2019; 10:143. [PMID: 30941064 PMCID: PMC6433820 DOI: 10.3389/fpsyt.2019.00143] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 02/26/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
- Marko Curkovic
- Department for Diagnostics and Intensive Care, University Psychiatric Hospital Vrapce, Zagreb, Croatia
| | - Andro Kosec
- Department of Otorhinolaryngology and Head and Neck Surgery, University Hospital Center Sestre Milosrdnice, Zagreb, Croatia
| | - Aleksandar Savic
- Department for Diagnostics and Intensive Care, University Psychiatric Hospital Vrapce, Zagreb, Croatia.,Department of Psychiatry, University of Zagreb School of Medicine, Zagreb, Croatia
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19
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Jaworska N, de la Salle S, Ibrahim MH, Blier P, Knott V. Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data. Front Psychiatry 2018; 9:768. [PMID: 30692945 PMCID: PMC6339954 DOI: 10.3389/fpsyt.2018.00768] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 12/21/2018] [Indexed: 12/28/2022] Open
Abstract
Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.
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Affiliation(s)
- Natalia Jaworska
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Sara de la Salle
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
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