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Nervenheilkunde 2009; 28(04): 179-185
DOI: 10.1055/s-0038-1628599
Thema zum Schwerpunkt
Schattauer GmbH

Das Berliner Algorithmusprojekt

Vergleich systematischer Therapiealgorithmen mit der Standardbehandlung bei Patienten mit unipolarer DepressionThe German Algorithm ProjectTherapy algorithms in comparison to treatment as usual in patients with unipolar depression
K. Wiethoff
1   Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, Campus Charité Mitte
,
R. Ricken
1   Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, Campus Charité Mitte
,
M. E. Keck
2   Neuroscience Center Universität Zürich, Klinik Schlössli, Privatklinik für Psychiatrie Oetwil
,
T. Baghai
3   Klinik für Psychiatrie und Psychotherapie Ludwig-Maximilians-Universität München
,
M. Bauer
4   Klinik für Psychiatrie und Psychotherapie, Universitätsklinikum Carl Gustav Carus, Technische Universität Dresden
,
H.-J. Möller
3   Klinik für Psychiatrie und Psychotherapie Ludwig-Maximilians-Universität München
,
M. Adli
1   Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, Campus Charité Mitte
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Eingegangen am: 01. Dezember 2008

angenommen am: 09. Februar 2009

Publikationsdatum:
24. Januar 2018 (online)

    Lizenzen und Reprints

Zusammenfassung

Trotz der erheblichen Zunahme an Behandlungsoptionen und neuen, besser verträglichen Substanzen, bleiben therapieresistente depressive Störungen bis heute ein großes Problem. Ein Ansatzpunkt zur Verbesserung der Behandlungsergebnisse liegt in der Optimierung des Therapieprozesses durch die Anwendung von Therapiealgorithmen. Diese umfassen strukturierte und systematische Behandlungsempfehlungen für den Fall, dass eine adäquate antidepressive Monotherapie nicht zu dem zuvor definierten Behandlungserfolg führt. Im Rahmen des mehrphasigen Berliner Algorithmusprojekts wurde die algorithmusgestützte Depressionsbehandlung im Vergleich zur üblichen Therapie nach freier Arztentscheidung einer Evaluation unterzogen. Die Ergebnisse dieser und weiterer Algorithmusstudien, die in dieser Übersicht dargestellt werden, zeigen den Nutzen systematischer Therapiealgorithmen in der Depressionsbehandlung in Bezug auf das Therapieergebnis und die Qualität der Behandlung. Ziel der weiteren Algorithmusforschung liegt in der Identifizierung von geeigneten klinischen oder biologischen Parametern, die eine effektive Individualisierung algorithmusgestützter Therapie ermöglichen.

Summary

Despite the enormous increase in treatment options and the development of new and better tolerated substances, treatment resistance remains a major clinical problem in the therapy of major depression. Treatment algorithms are therefore considered as key instruments in optimizing the treatment of depressive disorders. They offer structured and systematic treatment recommendations for the case of incomplete response to an adequately performed antidepressant monotherapy. The multiphasic German Algorithm Project (“Berliner Algorithmusprojekt”) aimed at comparing algorithm guided treatments with treatment as usual. The results and other algorithm studies, which are reviewed in this article, show the benefit of treatment algorithms for treatment outcome and treatment quality. Valid clinical or pharmacogenetic predictors of response are needed to further tailor specific algorithms to individual patients.

Schlüsselwörter

Depression - Therapiealgorithmen - Therapieresistenz - Berliner Algorithmusprojekt

Keywords

Depression - treatment algorithms - treatment resistance - German Algorithm Project
 

  • Literatur

  • 1 Adli M. et al. Effectiveness and feasibility of a standardized stepwise drug treatment algorithm for inpatients with depressive disorders – results of a twoyear observational study. J Clin Psychiatr 2002; 63: 782-790.
    CrossrefGoogle Scholar
  • 2 Adli M. et al. Algorithms for optimizing the treatment of depression: making the right decision at the right time. Pharmacopsychiatry 2003; 36 (Suppl. 03) 222-229.
    Artikel in Thieme ConnectGoogle Scholar
  • 3 Adli M. et al. Algorithmusgestützte Behandlung stationärer Patienten mit depressiven Erkrankungen: das Berliner Algorithmusprojekt. In: Bauer M, Berghöfer A, Adli M. (Hrsg). Therapieresistente Depressionen. Heidelberg; Springer; 2005
    Google Scholar
  • 4 Adli M, Bauer M, Rush AJ. Algorithms and collaborative-care systems for depression: are they effective and why? A systematic review. Biol Psychiatr 2006; 59: 1029-1038.
    CrossrefGoogle Scholar
  • 5 Adli M. et al. Response to lithium augmentation treatment in depression is associated with the glycogen synthase kinase 3-beta- 50T/C-single nucleotide polymorphism. Biol Psychiatr 2007; 62 (11) 1295-302.
    CrossrefGoogle Scholar
  • 6 Alexopoulos G. et al. Remission in depressed geriatric primary care patients: A report from the PROSPECT study. Am J Psychiatry 2005; 162: 718-724.
    CrossrefGoogle Scholar
  • 7 Amsterdam JD, Hornig-Rohan M. Treatment algorithms in treatment-resistant depression. Psychiatr Clin North Am 1996; 19: 371-386.
    CrossrefGoogle Scholar
  • 8 Badamgarav E. et al. Effectiveness of disease management programs in depression: a systematic review. Am J Psychiatry 2003; 160: 2080-2090.
    CrossrefPubMedGoogle Scholar
  • 9 Basco MR, Rush AJ. Compliance with pharmacotherapy in mood disorders. Psychiatr Ann. 1995 25. 269-270 278-279..
    Google Scholar
  • 10 Bauer M. et al. World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for biological treatment of unipolar depressive disorders, Part 1: acute and continuation treatment of major depressive disorder. World J Biol Psychiatr 2002; 03: 5-43.
    CrossrefGoogle Scholar
  • 11 Bauer M. et al. World Federation of Societies of Biological Psychiatry (WFSBP), Guidelines for Biological Treatment of Unipolar Depressive Disorders, part 2: Maintenance treatment of major depressive disorder and treatment of chronic depressive disorders and subthreshold depressions. World J Biol Psychiatry 2002; 03: 69-86.
    CrossrefGoogle Scholar
  • 12 Bauer M. et al. Effectiveness of an algorithm-guided treatment regimen compared to treatment as usual: a randomized controlled study of inpatients with depression. J Clin Psychopharmacology; eingereicht..
    Google Scholar
  • 13 Bech P, Rafaelsen OJ. The melancholia scale: development, consistency, validity and utility. In: Sartorius N, Ban TA. (eds). Assessment of Depression. Berlin: Springer; 1986
    Google Scholar
  • 14 Birkenhäger TK. et al. Outcome of a 4-step treatment algorithm for depressed inpatients. J Clin Psychiatry 2006; 67: 1266-1271.
    CrossrefGoogle Scholar
  • 15 Bondolfi G. et al. A stepwise drug treatment algorithm to obtain complete remission in depression: a Geneva study. Swiss med Wkly 2006; 136: 78-85.
    Google Scholar
  • 16 Bruce M. et al. Reducing suicidal ideation and depressive symptoms in depressed older primary care patients. JAMA 2004; 291: 1081-1091.
    CrossrefGoogle Scholar
  • 17 Crismon ML. et al. The Texas Medication Algorithm Project: Report of the Texas consensus conference panel on medication treatment of major depressive disorder. J Clin Psychiatr 1999; 60: 142-156.
    CrossrefGoogle Scholar
  • 18 Faltermaier-Temizel M. et al. Prädiktive Faktoren für den Therapieerfolg beim depressiven Syndrom. Nervenarzt 1997; 68: 62-66.
    CrossrefGoogle Scholar
  • 19 Fava M. et al. Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. Psychiatr Clin North Am 2003; 26: 457-494.
    CrossrefGoogle Scholar
  • 20 Flint AJ, Rifat SL. The effect of sequential antidepressant treatment on geriatric depression. J Affect Dis 1996; 36: 95-105.
    CrossrefGoogle Scholar
  • 21 Friedman RA, Koesis JH. Pharmacotherapy for chronic depression. Psychiatr Clin North Am 1996; 19: 121-132.
    CrossrefGoogle Scholar
  • 22 Gilbert DA. et al. Texas Medication Algorithm Project: definitions, rationale, and methods to develop medication algorithms. J Clin Psychiatry 1998; 59: 345-351.
    CrossrefGoogle Scholar
  • 23 Greden JF. The burden of disease for treatment-resistant depression. J Clin Psychiatry 2001; 62 (Suppl. 16) 26-31.
    Google Scholar
  • 24 Hamilton M. The Hamilton Rating Scale for Depression. In: Sartorius N, Ban TA. (eds). Assessment of Depression. Heidelberg, Berlin, New York: Springer; 1986
    Google Scholar
  • 25 Hawley CJ. et al. A protocol for the pharmacologic treatment of major depression. A field test of a potential prototype. J Affect Disord 1998; 47: 87-96.
    CrossrefGoogle Scholar
  • 26 Hirschfeld RM. et al. The National Depressive and Manic-Depressive Association consensus statement on the undertreatment of depression. JAMA 2002; 277: 333-340.
    Google Scholar
  • 27 Judd LL. et al. A prospective 12-year study of subsyndromal and syndromal depressive symptoms in unipolar major depressive disorders. Arch Gen Psychiatry 1998; 55: 694-700.
    CrossrefGoogle Scholar
  • 28 Katon W. et al. Collaborative management to achieve treatment guidelines. Impact on depression in primary care. JAMA 1995; 273: 1026-1031.
    CrossrefGoogle Scholar
  • 29 Katon W. et al. Stepped collaborative care for primary care patients with persistent symptoms of depression. Arch Gen Psychiatr 1999; 56: 1109-1115.
    CrossrefGoogle Scholar
  • 30 Katon W. et al. Long-term effects of a collaborative care intervention in persistently depressed primay care patients. J Gen Intern Med 2002; 17: 741-748.
    CrossrefGoogle Scholar
  • 31 Linden M. et al. Structure and feasibility of a standardized stepwise drug treatment regimen (SSTR) for depressed inpatients. Pharmacopsychiatr 1994; 27 (suppl): 51-53.
    Google Scholar
  • 32 Linden M. Der Einfluss von Leitlinien, Standards und ökonomischen Vorgaben auf medizinische Entscheidungsprozesse. ZäFQ 2004; 98: 200-205.
    Google Scholar
  • 33 Miller IW J. et al. The treatment of chronic depression. Part 3: Psychosocial functioning before and after treatment with sertraline or imipramine. J Clin Psychiatry 1998; 59: 608-619.
    CrossrefGoogle Scholar
  • 34 Mulsant BH. et al. Pharmacological treatment of depression in older primary care patients: the PROSPECT alorithm. Int J of Geriatric Psychiatry 2001; 16: 585-592.
    Google Scholar
  • 35 Paykel ES. et al. Residual symptoms after partial remission: an important outcome in depression. Psychol Med 1995; 25: 1171-1180.
    CrossrefGoogle Scholar
  • 36 Paykel ES. et al. Prevention of relapse in residual depression by cognitive therapy. A controlled trial. Arch Gen Psychiat 1999; 56: 829-835.
    CrossrefGoogle Scholar
  • 37 Riso LP. et al. A prospective test of criteria for response, remission, relapse, recovery, and recurrence in patients treated with cognitive behavioural therapy. J Affect Disord 1997; 43: 131-142.
    CrossrefGoogle Scholar
  • 38 Rush AJ. et al. Medication treatment for the severely and persistently ill: the Texas medication algorithm project. J Clin Psychiatry 1999; 60: 284-291.
    CrossrefGoogle Scholar
  • 39 Rush AJ, Ryan ND. Current and emerging therapeutics for depression. in: Charney DS, Coyle JT, Davis KL, Nemeroff CB. (eds.). Neuropsychopharmacology. The Fifth Generation of Progress. Baltimore: Lippincott Williams & Wilkins; 2002
    Google Scholar
  • 40 Rush AJ. et al. Texas Medication Algorithm Project, Phase 3 (TMAP-3): Rationale and Study Design. J Clin Psychiatry 2003; 64: 357-369.
    CrossrefGoogle Scholar
  • 41 Rush AJ. et al. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials 2004; 25: 119-142.
    CrossrefGoogle Scholar
  • 42 Rush AJ. Algorithmusgestützte Behandlung der Depression. In: Bauer M, Berghöfer A, Adli M. (Hrsg). Therapieresistente Depressionen. Heidelberg, Berlin, New York: Springer; 2005
    Google Scholar
  • 43 Rush AJ. et al. Bupropion-SR, sertraline, or venlafaxin-XR after failure of SSRIs for depression. N Engl J Med 2006; 354: 1231-1242.
    CrossrefGoogle Scholar
  • 44 Stamm T. et al. Serotonin transporter gene and response to lithium augmentation in depression. Psychiatr Genet 2008; 18 (02) 92-97.
    CrossrefGoogle Scholar
  • 45 Thase ME, Rush AJ. Treatment-resistant depression. In: Bloom FE, Kupfer DJ. (eds). Psychopharmacology: The Fourth Generation of Progress. New York: Raven Press; 1995
    Google Scholar
  • 46 Toprac MG. et al. The Texas Medication Algorithm Project. Patient and family education program; a consumer guided initiative. J of Clin Psychiatry 2000; 61: 477-486.
    Google Scholar
  • 47 Trivedi MH. et al. Medication Augmentation after the failure of SSRIs for depression. N Engl J Med 2006; 354: 1243-1252.
    CrossrefGoogle Scholar
  • 48 Trivedi MH. et al. Texas Medication Algorithm Project (TMAP): Clinical Results for Patients with Major Depressive Disorder. Arch Gen Psychiat 2004; 61: 669-680.
    CrossrefGoogle Scholar
  • 49 Trivedi MH. et al. Assessing physicians’ use of treatment algorithms: Project IMPACTS study design and rationale. Contemporary Clinical Trials 2007; 28: 192-212.
    CrossrefGoogle Scholar
  • 50 Unützer J. et al. Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA 2002; 288: 2836-2845.
    CrossrefGoogle Scholar