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Electroconvulsive therapy response and remission in moderate to severe depressive illness: a decade of national Scottish data
Published online by Cambridge University Press: 18 September 2024
Abstract
Background
Despite strong evidence of efficacy of electroconvulsive therapy (ECT) in the treatment of depression, no sensitive and specific predictors of ECT response have been identified. Previous meta-analyses have suggested some pre-treatment associations with response at a population level.
Aims
Using 10 years (2009–2018) of routinely collected Scottish data of people with moderate to severe depression (n = 2074) receiving ECT we tested two hypotheses: (a) that there were significant group-level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (b) that it was possible to develop a method for predicting illness remission for individual patients using machine learning.
Method
Data were analysed on a group level using descriptive statistics and association analyses as well as using individual patient prediction with machine learning methodologies, including cross-validation.
Results
ECT is highly effective for moderate to severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, necessity for urgent intervention, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for necessity for urgent intervention and, in addition, history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.
Conclusions
Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could inform shared decision-making, prevent the unnecessary use of ECT when it is unlikely to be beneficial and ensure prompt use of ECT when it is likely to be effective.
- Type
- Original Article
- Information
- Copyright
- Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Royal College of Psychiatrists
References
Liu, Q, He, H, Yang, J, Feng, X, Zhao, F, Lyu, J. Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study. J Psychiatr Res 2020; 126: 134–40.CrossRefGoogle ScholarPubMed
Rush, AJ, Kilner, J, Fava, M, Wisniewski, SR, Warden, D, Nierenberg, AA, et al. Clinically relevant findings from STAR*D. Psychiatr Ann 2008; 38: 188–93.CrossRefGoogle Scholar
Pagnin, D, de Queiroz, V, Pini, S, Cassano, GB. Efficacy of ECT in depression: a meta-analytic review. J ECT 2004; 20: 13–20.CrossRefGoogle ScholarPubMed
Group UER. Efficacy and safety of electroconvulsive therapy in depressive disorders: a systematic review and meta-analysis. Lancet 2003; 361: 799–808.CrossRefGoogle Scholar
Rosenquist, PB, Brenes, GB, Arnold, EM, Kimball, J, McCall, WV. Health-related quality of life and the practice of electroconvulsive therapy. J ECT 2006; 22: 18–24.CrossRefGoogle ScholarPubMed
Buley, N, Copland, E, Hodge, S, Chaplin, R. A further decrease in the rates of administration of electroconvulsive therapy in England. J ECT 2017; 33: 198–202.CrossRefGoogle ScholarPubMed
Fergusson, GM, Cullen, LA, Freeman, CP, Hendry, JD. Electroconvulsive therapy in Scottish clinical practice: a national audit of demographics, standards, and outcome. J ECT 2004; 20: 166–73.CrossRefGoogle Scholar
Kellner, CH, Popeo, DM, Pasculli, RM, Briggs, MC, Gamss, S. Appropriateness for electroconvulsive therapy (ECT) can be assessed on a three-item scale. Med Hypotheses 2012; 79: 204–6.CrossRefGoogle ScholarPubMed
van Diermen, L, van den Ameele, S, Kamperman, AM, Sabbe, BCG, Vermeulen, T, Schrijvers, D, et al. Prediction of electroconvulsive therapy response and remission in major depression: meta-analysis. Br J Psychiatry 2018; 212: 71–80.CrossRefGoogle ScholarPubMed
Scottish ECT Accreditation Network. Scottish ECT Accreditation Network (SEAN): 2021 (Reporting on 2020 Data). Public Health Scotland, 2021 (https://www.publichealthscotland.scot/media/10034/sean_2021_management_information_report.pdf).Google Scholar
World Health Organization. Diagnostic and Management Guidelines for Mental Disorders in Primary Care: ICD-10 Chapter V, Primary Care Version. Hogrefe & Huber Publishers, 1996.Google Scholar
Montgomery, SA, Asberg, M. A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134: 382–9.CrossRefGoogle ScholarPubMed
Muller, MJ, Szegedi, A, Wetzel, H, Benkert, O. Moderate and severe depression. Gradations for the Montgomery-Asberg Depression Rating Scale. J Affect Disord 2000; 60(2): 137–40.Google ScholarPubMed
Poldrack, RA, Huckins, G, Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 2020; 77: 534–40.CrossRefGoogle ScholarPubMed
Steele, JD, Paulus, MP. Pragmatic neuroscience for clinical psychiatry. Br J Psychiatry 2019; 215: 404–8.CrossRefGoogle ScholarPubMed
Varga, TV, Niss, K, Estampador, AC, Collin, CB, Moseley, PL. Association is not prediction: a landscape of confused reporting in diabetes – a systematic review. Diabetes Res Clin Pract 2020; 170: 108497.CrossRefGoogle Scholar
Koutsouleris, N, Vetter, C, Wiegand, A. Neurominer [computer software]. Section for Neurodiagnostic Applications, Ludwig-Maximillian-University of Munich, 2022 (http://www.proniapredictors.eu/neurominer/index.html).Google Scholar
Paykel, ES, Ramana, R, Cooper, Z, Hayhurst, H, Kerr, J, Barocka, A. Residual symptoms after partial remission: an important outcome in depression. Psychol Med 1995; 25(6): 1171–80.CrossRefGoogle ScholarPubMed
Wu, O. Rethinking class imbalance in machine learning. arXiv [cs.LG] [Preprint] 2023. Available from: https://arxiv.org/abs/2305.03900.Google Scholar
Waite, S, Tor, PC, Mohan, T, Davidson, D, Hussain, S, Dong, V, et al. The utility of the Sydney Melancholia Prototype Index (SMPI) for predicting response to electroconvulsive therapy in depression: a CARE network study. J Psychiatr Res 2022; 155: 180–5.CrossRefGoogle ScholarPubMed
Blanken, M, Oudega, ML, Hoogendoorn, AW, Sonnenberg, CS, Rhebergen, D, Klumpers, UMH, et al. Sex-specifics of ECT outcome. J Affect Disord 2023; 326: 243–8.CrossRefGoogle ScholarPubMed
Haq, AU, Sitzmann, AF, Goldman, ML, Maixner, DF, Mickey, BJ. Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. J Clin Psychiatry 2015; 76: 1374–84.CrossRefGoogle ScholarPubMed
Nygren, A, Reutfors, J, Brandt, L, Boden, R, Nordenskjold, A, Tiger, M. Response to electroconvulsive therapy in treatment-resistant depression: nationwide observational follow-up study. BJPsych Open 2023; 9(2): e35.CrossRefGoogle ScholarPubMed
Heijnen, WT, Birkenhager, TK, Wierdsma, AI, van den Broek, WW. Antidepressant pharmacotherapy failure and response to subsequent electroconvulsive therapy: a meta-analysis. J Clin Psychopharmacol 2010; 30: 616–9.CrossRefGoogle ScholarPubMed
Clements, CC, Karlsson, R, Lu, Y, Jureus, A, Ruck, C, Andersson, E, et al. Genome-wide association study of patients with a severe major depressive episode treated with electroconvulsive therapy. Mol Psychiatry 2021; 26: 2429–39.CrossRefGoogle ScholarPubMed
Foo, JC, Streit, F, Frank, J, Witt, SH, Treutlein, J, Major Depressive Disorder Working Group of the Psychiatric Genomics C, et al. Evidence for increased genetic risk load for major depression in patients assigned to electroconvulsive therapy. Am J Med Genet B Neuropsychiatr Genet 2019; 180: 35–45.CrossRefGoogle ScholarPubMed
Yang, WC, Lin, CH, Chen, CC. Risk factors of relapse after successful electroconvulsive therapy for Taiwanese patients with major depression. J ECT 2020; 36: 106–10.CrossRefGoogle ScholarPubMed
Lambrichts, S, Vansteelandt, K, Crauwels, B, Obbels, J, Pilato, E, Denduyver, J, et al. Relapse after abrupt discontinuation of maintenance electroconvulsive therapy during the COVID-19 pandemic. Acta Psychiatr Scand 2021; 144: 230–7.CrossRefGoogle ScholarPubMed
Royal College of Psychiatrists. Electroconvulsive Therapy Information Resource. RCPsych, 2022 (https://www.rcpsych.ac.uk/mental-health/treatments-and-wellbeing/ect).Google Scholar
Husain, MM, Rush, AJ, Fink, M, Knapp, R, Petrides, G, Rummans, T, et al. Speed of response and remission in major depressive disorder with acute electroconvulsive therapy (ECT): a Consortium for Research in ECT (CORE) report. J Clin Psychiatry 2004; 65: 485–91.CrossRefGoogle Scholar
Ronnqvist, I, Nilsson, FK, Nordenskjold, A. Electroconvulsive therapy and the risk of suicide in hospitalized patients with major depressive disorder. JAMA Netw Open 2021; 4(7): e2116589.CrossRefGoogle ScholarPubMed
Kaster, TS, Blumberger, DM, Gomes, T, Sutradhar, R, Wijeysundera, DN, Vigod, SN. Risk of suicide death following electroconvulsive therapy treatment for depression: a propensity score-weighted, retrospective cohort study in Canada. Lancet Psychiatry 2022; 9: 435–46.CrossRefGoogle ScholarPubMed
Brodaty, H, Berle, D, Hickie, I, Mason, C. Perceptions of outcome from electroconvulsive therapy by depressed patients and psychiatrists. Aust N Z J Psychiatry 2003; 37: 196–9.CrossRefGoogle ScholarPubMed
Iltis, AS, Fortier, R, Ontjes, N, McCall, WV. Ethics considerations in laws restricting incapacitated patients’ access to ECT. J Am Acad Psychiatry Law 2023; 51: 47–55.Google ScholarPubMed
Salagre, E, Rohde, C, Ishtiak-Ahmed, K, Gasse, C, Ostergaard, SD. Survival rate following involuntary electroconvulsive therapy: a population-based study. J ECT 2021; 37: 94–9.CrossRefGoogle ScholarPubMed
Lin, CH, Chen, MC, Yang, WC, Lane, HY. Early improvement predicts outcome of major depressive patients treated with electroconvulsive therapy. Eur Neuropsychopharmacol 2016; 26: 225–33.CrossRefGoogle ScholarPubMed
Lin, HS, Lin, CH. Early improvement in HAMD-17 and HAMD-6 scores predicts ultimate response and remission for depressed patients treated with fluoxetine or ECT. J Affect Disord 2019; 245: 91–7.CrossRefGoogle ScholarPubMed
Martinez-Amoros, E, Goldberg, X, Galvez, V, de Arriba-Arnau, A, Soria, V, Menchon, JM, et al. Early improvement as a predictor of final remission in major depressive disorder: new insights in electroconvulsive therapy. J Affect Disord 2018; 235: 169–75.CrossRefGoogle ScholarPubMed
Zheng, W, He, M, Gu, LM, Lao, GH, Wang, DF, Mai, JX, et al. Early improvement as a predictor of final remission in patients with treatment-resistant depression receiving electroconvulsive therapy with ketofol anesthesia. J Affect Disord 2022; 310: 223–7.CrossRefGoogle ScholarPubMed
Tsuchiyama, K, Nagayama, H, Yamada, K, Isogawa, K, Katsuragi, S, Kiyota, A. Predicting efficacy of electroconvulsive therapy in major depressive disorder. Psychiatry Clin Neurosci 2005; 59: 546–50.CrossRefGoogle ScholarPubMed
Birkenhager, TK, Roos, J, Kamperman, AM. Improvement after two sessions of electroconvulsive therapy predicts final remission in in-patients with major depression. Acta Psychiatr Scand 2019; 140: 189–95.CrossRefGoogle ScholarPubMed
Maoz, H, Nitzan, U, Goldwyn, Y, Krieger, I, Bloch, Y. When can we predict the outcome of an electroconvulsive therapy course in adolescents? A retrospective study. J ECT 2018; 34: 104–7.CrossRefGoogle ScholarPubMed
Chen, CC, Lin, CH, Yang, WC, Chen, MC. Clinical factors related to acute electroconvulsive therapy outcome for patients with major depressive disorder. Int Clin Psychopharmacol 2017; 32: 127–34.CrossRefGoogle ScholarPubMed
de Vreede, IM, Burger, H, van Vliet, IM. Prediction of response to ECT with routinely collected data in major depression. J Affect Disord 2005; 86: 323–7.CrossRefGoogle ScholarPubMed
van Diermen, L, Hebbrecht, K, Schrijvers, D, Sabbe, BCG, Fransen, E, Birkenhager, TK. The Maudsley Staging Method as predictor of electroconvulsive therapy effectiveness in depression. Acta Psychiatr Scand 2018; 138: 605–14.CrossRefGoogle ScholarPubMed
Jiang, R, Abbott, CC, Jiang, T, Du, Y, Espinoza, R, Narr, KL, et al. SMRI biomarkers predict electroconvulsive treatment outcomes: accuracy with independent data sets. Neuropsychopharmacology 2018; 43: 1078–87.CrossRefGoogle ScholarPubMed
Leaver, AM, Wade, B, Vasavada, M, Hellemann, G, Joshi, SH, Espinoza, R, et al. Fronto-temporal connectivity predicts ECT outcome in major depression. Front Psychiatry 2018; 9: 92.CrossRefGoogle ScholarPubMed
Mulders, PCR, Llera, A, Beckmann, CF, Vandenbulcke, M, Stek, M, Sienaert, P, et al. Structural changes induced by electroconvulsive therapy are associated with clinical outcome. Brain Stimul 2020; 13: 696–704.CrossRefGoogle ScholarPubMed
Redlich, R, Opel, N, Grotegerd, D, Dohm, K, Zaremba, D, Burger, C, et al. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 2016; 73: 557–64.CrossRefGoogle ScholarPubMed
Takamiya, A, Liang, KC, Nishikata, S, Tarumi, R, Sawada, K, Kurokawa, S, et al. Predicting individual remission after electroconvulsive therapy based on structural magnetic resonance imaging: a machine learning approach. J ECT 2020; 36: 205–10.CrossRefGoogle ScholarPubMed
van Waarde, JA, Scholte, HS, van Oudheusden, LJ, Verwey, B, Denys, D, van Wingen, GA. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 2015; 20: 609–14.CrossRefGoogle ScholarPubMed
Wade, BSC, Hellemann, G, Espinoza, RT, Woods, RP, Joshi, SH, Redlich, R, et al. Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy. Hum Brain Mapp 2021; 42: 5322–33.CrossRefGoogle ScholarPubMed
Wang, J, Wei, Q, Yuan, X, Jiang, X, Xu, J, Zhou, X, et al. Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder. J Affect Disord 2018; 225: 658–64.CrossRefGoogle ScholarPubMed
Bruin, WB, Oltedal, L, Bartsch, H, Abbott, C, Argyelan, M, Barbour, T, et al. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis. Psychol Med 2024: 54: 495–506.CrossRefGoogle ScholarPubMed
Nakajima, K, Takamiya, A, Uchida, T, Kudo, S, Nishida, H, Minami, F, et al. Individual prediction of remission based on clinical features following electroconvulsive therapy: a machine learning approach. J Clin Psychiatry 2022; 83(5): 21m14293.CrossRefGoogle ScholarPubMed
Semkovska, M, Landau, S, Dunne, R, Kolshus, E, Kavanagh, A, Jelovac, A, et al. Bitemporal versus high-dose unilateral twice-weekly electroconvulsive therapy for depression (EFFECT-Dep): a pragmatic, randomized, non-inferiority trial. Am J Psychiatry 2016; 173: 408–17.CrossRefGoogle ScholarPubMed
Lihua, P, Su, M, Ke, W, Ziemann-Gimmel, P. Different regimens of intravenous sedatives or hypnotics for electroconvulsive therapy (ECT) in adult patients with depression. Cochrane Database Syst Rev 2014; 2014(4): CD009763.Google ScholarPubMed
Galvez, V, McGuirk, L, Loo, CK. The use of ketamine in ECT anaesthesia: a systematic review and critical commentary on efficacy, cognitive, safety and seizure outcomes. World J Biol Psychiatry 2017; 18: 424–44.CrossRefGoogle ScholarPubMed
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