Int J Neuropsychopharmacol 2017 09;20(9):721-730
Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany; Universität Regensburg, Department of Psychiatry and Psychotherapy, Regensburg, Germany; East London NHS Foundation Trust, City and Hackney Centre for Mental Health, Donald Winnicott Centre, London, United Kingdom; kbo-Lech-Mangfall-Klinik Garmisch-Partenkirchen, Department of Psychiatry and Psychotherapy, Garmisch-Partenkirchen, Germany; Ludwig-Maximilians-Universität, Department of Psychiatry and Psychotherapy, München, Germany; kbo-Isar-Amper-Klinikum, Department of Psychiatry and Psychotherapy, München, Germany; Heinrich-Heine-Universität Düsseldorf, Department of Psychiatry and Psychotherapy, Düsseldorf, Germany; Institut für Psychologische Medizin, Haag, Germany; St. Joseph-Krankenhaus, Department of Psychiatry and Psychotherapy, Berlin, Germany; LWL-Klinikum Gütersloh, Department of Psychiatry and Psychotherapy, Gütersloh, Germany; Universitätsklinikum Carl Gustav Carus, Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany; Jena University Hospital, Department of Medical Statistics, Informatics and Documentation, Friedrich-Schiller-Universität Jena, Jena, Germany; Fliedner Klinik Berlin, Center for Psychiatry, Psychotherapy and Psychosomatic Medicine.
Background: Treatment algorithms are considered as key to improve outcomes by enhancing the quality of care. This is the first randomized controlled study to evaluate the clinical effect of algorithm-guided treatment in inpatients with major depressive disorder.
Methods: Inpatients, aged 18 to 70 years with major depressive disorder from 10 German psychiatric departments were randomized to 5 different treatment arms (from 2000 to 2005), 3 of which were standardized stepwise drug treatment algorithms (ALGO). The fourth arm proposed medications and provided less specific recommendations based on a computerized documentation and expert system (CDES), the fifth arm received treatment as usual (TAU). ALGO included 3 different second-step strategies: lithium augmentation (ALGO LA), antidepressant dose-escalation (ALGO DE), and switch to a different antidepressant (ALGO SW). Time to remission (21-item Hamilton Depression Rating Scale ≤9) was the primary outcome.
Results: Time to remission was significantly shorter for ALGO DE (n=91) compared with both TAU (n=84) (HR=1.67; P=.014) and CDES (n=79) (HR=1.59; P=.031) and ALGO SW (n=89) compared with both TAU (HR=1.64; P=.018) and CDES (HR=1.56; P=.038). For both ALGO LA (n=86) and ALGO DE, fewer antidepressant medications were needed to achieve remission than for CDES or TAU (P<.001). Remission rates at discharge differed across groups; ALGO DE had the highest (89.2%) and TAU the lowest rates (66.2%).
Conclusions: A highly structured algorithm-guided treatment is associated with shorter times and fewer medication changes to achieve remission with depressed inpatients than treatment as usual or computerized medication choice guidance.