dc.contributor.author
Adli, Mazda
dc.contributor.author
Wiethoff, Katja
dc.contributor.author
Baghai, Thomas C.
dc.contributor.author
Fisher, Robert
dc.contributor.author
Seemüller, Florian
dc.contributor.author
Laakmann, Gregor
dc.contributor.author
Brieger, Peter
dc.contributor.author
Cordes, Joachim
dc.contributor.author
Malevani, Jaroslav
dc.contributor.author
Laux, Gerd
dc.contributor.author
Hauth, Iris
dc.contributor.author
Möller, Hans-Jürgen
dc.contributor.author
Kronmüller, Klaus-Thomas [u.a.]
dc.date.accessioned
2018-06-08T11:08:04Z
dc.date.available
2017-11-02T10:26:57.044Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/21679
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-24967
dc.description.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.
en
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
treatment algorithms
dc.subject
antidepressants
dc.subject
treatment-resistant depression
dc.subject
medical decision making
dc.subject
German Algorithm Project
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
dc.title
How Effective Is Algorithm-Guided Treatment for Depressed Inpatients?
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
International Journal of Neuropsychopharmacology. - 20 (2017), 9, 721–730
dc.title.subtitle
Results from the Randomized Controlled Multicenter German Algorithm Project 3
Trial
dcterms.bibliographicCitation.doi
10.1093/ijnp/pyx043
dcterms.bibliographicCitation.url
http://doi.org/10.1093/ijnp/pyx043
refubium.affiliation
Charité - Universitätsmedizin Berlin
de
refubium.mycore.fudocsId
FUDOCS_document_000000028425
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000009069
dcterms.accessRights.openaire
open access