Algorithms of diagnosis and therapy of a primary depressive episode

Authors

DOI:

https://doi.org/10.14739/2310-1210.2023.4.262510

Keywords:

depression, mental disorders, psychiatry, psychotherapy, diagnosis, therapy

Abstract

Depressive disorders are among the most widespread forms of mental pathology with significant medical and social consequences and require adequate and timely medical assistance. It is known that algorithms are effective methods of optimizing the provision of psychiatric care in order to prevent the development of therapy-resistant forms of mental pathology and reduce their treatment costs, which is particularly relevant in the case of a primary depressive episode (PDE).

The aim of the work is to develop algorithms for rapid diagnosis and therapy of the PDE.

Materials and methods. During 2018–2021, 131 patients (49 men and 82 women) with the PDE who sought outpatient psychiatric help were clinically examined. All subjects were tested to determine the level of depressive disorder, according to the unified clinical protocol of highly specialized medical care, according to the Hamilton Rating Scale for Depression, Hamilton Anxiety Rating Scale, and the Clinical Global Impressions Scale. The clinical-ethological description consisted of three stages: at the first stage, at a patients’ first visit, a general description within the framework of a clinical and psychopathological examinations; at the second stage, the clinical-ethological characteristics of non-verbal behavior were studied; at the last, third stage, non-verbal behavior was recoded into ethological elements according to A. A. Korobov’s glossary (1991). Statistical analysis was performed using the Statistica 10 license package of application programs.

Results. An algorithm for the PDE diagnosis has been developed based upon four stages: the first one – the symptomatic state diagnosis; the second – clinical-ethological analysis of depressive phenomenon signs and a neurophysiological electroencephalographic (EEG) examination; the third – psycho-experimental examinations using appropriate tools; the fourth – structuring of the obtained diagnostic data, diagnosis verification and development of therapeutic intervention tactics. A therapeutic algorithm for the PDE treatment was also developed and proposed: at the first stage – use of existing antidepressants with proven clinical effectiveness; at the second – switching from a drug in case of its ineffectiveness within 3–4 weeks to another drug with a different mechanism of action; at the third – in case of the previous stage ineffectiveness, using a combination antidepressant therapy (combining drugs of different groups); at the fourth – application of therapeutic schemes using diuretics, pathogenetic psychotherapy and electroconvulsive therapy.

Conclusions. The proposed diagnostic and treatment algorithms are reliable enough to detect and treat such a category as the primary depressive episode. The effectiveness of personalized comprehensive diagnostic and treatment methods for these conditions should be based on the principles of phasing, comprehensiveness, using integrated approaches, combining therapies aimed at developing an adequate attitude to a disease state, mitigating the intensity of negative emotions, restoring internal and external resources of patients.

Author Biographies

V. L. Pidlubnyi, Zaporizhzhia State Medical and Pharmaceutical University, Ukraine

MD, PhD, DSc, Professor of the Department of Psychiatry, Psychotherapy, General and Medical Psychology, Narcology and Sexology

V. S. Makoid, Zaporizhzhia State Medical and Pharmaceutical University, Ukraine

MD, PhD student of the Department of Psychiatry, Psychotherapy, General and Medical Psychology, Narcology and Sexology

References

Adli, M., Rush, A. J., Möller, H. J., & Bauer, M. (2003). Algorithms for optimizing the treatment of depression: making the right decision at the right time. Pharmacopsychiatry, 36(Suppl 3), S222-S229. https://doi.org/10.1055/s-2003-45134

Mann, J. J. (2005). The medical management of depression. The New England journal of medicine, 353(17), 1819-1834. https://doi.org/10.1056/NEJMra050730

Bauer, M., Pfennig, A., Linden, M., Smolka, M. N., Neu, P., & Adli, M. (2009). Efficacy of an algorithm-guided treatment compared with treatment as usual: a randomized, controlled study of inpatients with depression. Journal of clinical psychopharmacology, 29(4), 327-333. https://doi.org/10.1097/JCP.0b013e3181ac4839

Ricken, R., Wiethoff, K., Reinhold, T., Schietsch, K., Stamm, T., Kiermeir, J., Neu, P., Heinz, A., Bauer, M., & Adli, M. (2011). Algorithm-guided treatment of depression reduces treatment costs--results from the randomized controlled German Algorithm Project (GAPII). Journal of affective disorders, 134(1-3), 249-256. https://doi.org/10.1016/j.jad.2011.05.053

Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., McGrath, P. J., Rosenbaum, J. F., Sackeim, H. A., Kupfer, D. J., Luther, J., & Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. The American journal of psychiatry, 163(11), 1905-1917. https://doi.org/10.1176/ajp.2006.163.11.1905

Richardson, K., & Barkham, M. (2020). Recovery from depression: a systematic review of perceptions and associated factors. Journal of mental health, 29(1), 103-115. https://doi.org/10.1080/09638237.2017.1370629

Maruta, N. O., & Zhupanova, D. O. (2016). Kliniko-psykholohichni osoblyvosti khvorykh na depresiiu z riznym rivnem medykamentoznoho komplaiensu (diahnostyka i korektsiia) [Clinical-psychological peculiarities of patients with depression and different levels of medication compliance (diagnosis and correction)]. Ukrainskyi visnyk psykhonevrolohii, 24(1), 5-11. [in Ukrainian].

Bennabi, D., Aouizerate, B., El-Hage, W., Doumy, O., Moliere, F., Courtet, P., Nieto, I., Bellivier, F., Bubrovsky, M., Vaiva, G., Holztmann, J., Bougerol, T., Richieri, R., Lancon, C., Camus, V., Saba, G., Haesbaert, F., d'Amato, T., Charpeaud, T., Llorca, P. M., … Haffen, E. (2015). Risk factors for treatment resistance in unipolar depression: a systematic review. Journal of affective disorders, 171, 137-141. https://doi.org/10.1016/j.jad.2014.09.020

Lamers, F., de Jonge, P., Nolen, W. A., Smit, J. H., Zitman, F. G., Beekman, A. T., & Penninx, B. W. (2010). Identifying depressive subtypes in a large cohort study: results from the Netherlands Study of Depression and Anxiety (NESDA). The Journal of clinical psychiatry, 71(12), 1582-1589. https://doi.org/10.4088/JCP.09m05398blu

Troyan, O. S. (2021). AGEADJUSTED NORMATIVE DATA AND DISCRIMINATIVE VALIDITY OF COGNITIVE TESTS IN THE UKRAINIAN ADULT PATIENTS WITH MAJOR DEPRESSIVE DISORDER. Modern Medical Technology, (2), 4-14. https://doi.org/10.34287/MMT.2(49).2021.1

Maruta, N. A., Yavdak, Y. A., & Cheredniakova, E. S. (2017). Otsenka neirotroficheskoi terapii depressii [Evaluation the neurotrophic therapy of depression]. Mizhnarodnyi nevrolohichnyi zhurnal, (3), 29-38. [in Russsian]. https://doi.org/10.22141/2224-0713.3.89.2017.104240

Papakostas, G. I., Fava, M., & Thase, M. E. (2008). Treatment of SSRI-Resistant Depression: A Meta-Analysis Comparing Within- Versus Across-Class Switches. Biological Psychiatry, 63(7), 699-704. https://doi.org/10.1016/j.biopsych.2007.08.010

Ricken, R., Wiethoff, K., Reinhold, T., Schietsch, K., Stamm, T., Kiermeir, J., Neu, P., Heinz, A., Bauer, M., & Adli, M. (2011). Algorithm-guided treatment of depression reduces treatment costs--results from the randomized controlled German Algorithm Project (GAPII). Journal of affective disorders, 134(1-3), 249-256. https://doi.org/10.1016/j.jad.2011.05.053

Trivedi, M. H., Fava, M., Wisniewski, S. R., Thase, M. E., Quitkin, F., Warden, D., Ritz, L., Nierenberg, A. A., Lebowitz, B. D., Biggs, M. M., Luther, J. F., Shores-Wilson, K., Rush, A. J., & STAR*D Study Team (2006). Medication augmentation after the failure of SSRIs for depression. The New England journal of medicine, 354(12), 1243-1252. https://doi.org/10.1056/NEJMoa052964

Davies, P., Ijaz, S., Williams, C. J., Kessler, D., Lewis, G., & Wiles, N. (2019). Pharmacological interventions for treatment-resistant depression in adults. The Cochrane database of systematic reviews, 12(12), CD010557. https://doi.org/10.1002/14651858.CD010557.pub2

Kupfer, D. J. (1993). Management of recurrent depression. The Journal of Clinical Psychiatry, 54(2, Suppl), 29-33.

Rush, A. J., & Kupfer, D. J. (2001). Strategies and tactics in the treatment of depression. In G. O. Gabbard (Ed.), Treatment of Psychiatric Disorders (p. 1417-1439). American Psychiatric Publishing, Inc.

Downloads

Published

2023-07-20

How to Cite

1.
Pidlubnyi VL, Makoid VS. Algorithms of diagnosis and therapy of a primary depressive episode. Zaporozhye Medical Journal [Internet]. 2023Jul.20 [cited 2024May20];25(4):333-8. Available from: http://zmj.zsmu.edu.ua/article/view/262510