Adli, Mazda and Wiethoff, Katja and Baghai, Thomas C. and Fisher, Robert and Seemueller, Florian and Laakmann, Gregor and Brieger, Peter and Cordes, Joachim and Malevani, Jaroslav and Laux, Gerd and Hauth, Iris and Moeller, Hans-Juergen and Kronmueller, Klaus-Thomas and Smolka, Michael N. and Schlattmann, Peter and Berger, Maximilian and Ricken, Roland and Stamm, Thomas J. and Heinz, Andreas and Bauer, Michael (2017) How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial. INTERNATIONAL JOURNAL OF NEUROPSYCHOPHARMACOLOGY, 20 (9). pp. 721-730. ISSN 1461-1457, 1469-5111
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | PRIMARY-CARE PATIENTS; PSYCHIATRY WFSBP GUIDELINES; MAJOR DEPRESSION; PHARMACOLOGICAL-TREATMENT; BIOLOGICAL TREATMENT; WORLD FEDERATION; RATING-SCALE; DISORDER; AUGMENTATION; REMISSION; treatment algorithms; antidepressants; treatment-resistant depression; medical decision making; German Algorithm Project |
| Subjects: | 100 Philosophy & psychology > 150 Psychology |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Dec 2018 13:15 |
| Last Modified: | 20 Feb 2019 15:06 |
| URI: | https://pred.uni-regensburg.de/id/eprint/1276 |
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