Literature DB >> 29986372

Algorithms For Treatment of Major Depressive Disorder: Efficacy and Cost-Effectiveness.

Michael Bauer1, A John Rush2,3,4, Roland Ricken5, Maximilian Pilhatsch1, Mazda Adli5,6.   

Abstract

In spite of multiple new treatment options, chronic and treatment refractory courses still are a major challenge in the treatment of depression. Providing algorithm-guided antidepressant treatments is considered an important strategy to optimize treatment delivery and avoid or overcome treatment-resistant courses of major depressive disorder (MDD). The clinical benefits of algorithms in the treatment of inpatients with MDD have been investigated in large-scale, randomized controlled trials. Results showed that a stepwise treatment regimen (algorithm) with critical decision points at the end of each treatment step based on standardized and systematic measurements of response and an algorithm-guided decision-making process increases the chances of achieving remission and optimizes prescription behaviors for antidepressants. In conclusion, research in MDD revealed that systematic and structured treatment procedures, the diligent assessment of response at critical decision points, and timely dose and treatment type adjustments make the substantial difference in treatment outcomes between algorithm-guided treatment and treatment as usual. © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2018        PMID: 29986372     DOI: 10.1055/a-0643-4830

Source DB:  PubMed          Journal:  Pharmacopsychiatry        ISSN: 0176-3679            Impact factor:   5.788


  6 in total

1.  Prescribing for moderate or severe unipolar depression in patients under the long-term care of UK adult mental health services.

Authors:  Carol Paton; Ian M Anderson; Philip J Cowen; Oriana Delgado; Thomas R E Barnes
Journal:  Ther Adv Psychopharmacol       Date:  2020-06-15

2.  An episode level evaluation of the treatment journey of patients with major depressive disorder and treatment-resistant depression.

Authors:  Bingcao Wu; Qian Cai; John J Sheehan; Carmela Benson; Nancy Connolly; Larry Alphs
Journal:  PLoS One       Date:  2019-08-08       Impact factor: 3.240

3.  A surrogate measure for patient reported symptom remission in administrative data.

Authors:  Farrokh Alemi; Mai Aljuaid; Naren Durbha; Melanie Yousefi; Hua Min; Louisa G Sylvia; Andrew A Nierenberg
Journal:  BMC Psychiatry       Date:  2021-03-04       Impact factor: 3.630

4.  Association between Estimated Cardiorespiratory Fitness and Depression among Middle-income Country Adults: Evidence from National Health Survey.

Authors:  Eduardo Lattari; Andreza Jesus Costa Pascouto; Bruno Ribeiro Ramalho Oliveira; Livia Soares Silva; Aldair José Oliveira; Sérgio Machado; Geraldo Albuquerque Maranhao Neto
Journal:  Clin Pract Epidemiol Ment Health       Date:  2021-12-22

5.  Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China.

Authors:  Lu Yang; Yousong Su; Sijia Dong; Tao Wu; Yongjing Zhang; Hong Qiu; Wenjie Gu; Hong Qiu; Yifeng Xu; JianLi Wang; Jun Chen; Yiru Fang
Journal:  Front Pharmacol       Date:  2022-08-31       Impact factor: 5.988

6.  Clinically Significant Changes in the 17- and 6-Item Hamilton Rating Scales for Depression: A STAR*D Report.

Authors:  Augustus John Rush; Charles South; Shailesh Jain; Raafae Agha; Mingxu Zhang; Shristi Shrestha; Zershana Khan; Mudasar Hassan; Madhukar H Trivedi
Journal:  Neuropsychiatr Dis Treat       Date:  2021-07-14       Impact factor: 2.570

  6 in total

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