Treatment Algorithm for Depression

Introduction

The guidelines below are intended to provide a series of treatment steps for depression to clinicians. They are framed within recognisable tiers of service (primary care, secondary care, specialist/ tertiary care) and all treatments are evidence-based. Although each tier is included, it is expected that the suggested steps might be most useful for psychiatrists in secondary care/ CMHTs.

Disclaimer

All prescribers need to be responsible for their own prescribing and there will always be factors that mean a specific treatment is not suitable. Many patients may have already had trials of particular drugs, and so strict adherence to an algorithm is rarely sensible.

General principles

These treatment guidelines are informed by several principles:

  1. The guidelines need to be simple, so all the underlying evidence is not included. Neither is lots of discussion about switchovers and side-effects. We expect clinicians to be able to inform patients appropriately and prescribe safely.
  2. Where there is uncertainty about which treatment is preferred, the patient should make this choice.
  3. Each step has a number of choices, most of which have similar risk/ benefit ratios.
  4. Sequenced and stepped-care for depression is supported by good quality evidence. Some references are below.

Clinicians are, of course, not obliged to follow these before making a referral to the Advanced Interventions Service but the principles of how many treatments are likely to have been delivered within each tier of service delivery are often a factor when considering referrals and making treatment recommendations. For example, someone who has only had two antidepressant treatment trials and one augmentation trial are unlikely to be suitable for referral since there are many other evidence-based treatments that should be provided first.

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Download these guidelines as a PDF (138Kb) | Last updated: 6 January 2022

References

Adli, M., Rush, A. J., Moller, H. J., et al (2003) Algorithms for Optimizing the Treatment of Depression: Making the Right Decision at the Right Time. Pharmacopsychiatry, 36 (Suppl 3), 222-229. http://dx.doi.org/10.1055/s-2003-45134
Amsterdam, J. D. & Hornig-Rohan, M. (1996) Treatment Algorithms in Treatment-Resistant Depression. Psychiatric Clinics of North America, 19, 371-386. https://doi.org/10.1016/s0193-953x(05)70293-8
Bauer, M., Pfennig, A., Linden, M., et al (2009) Efficacy of an Algorithm-Guided Treatment Compared With Treatment as Usual: A Randomized, Controlled Study of Inpatients With Depression. Journal of Clinical Psychopharmacology, 29, 327-333. http://dx.doi.org/10.1097/JCP.0b013e3181ac4839
Bauer, M., Rush, A. J., Ricken, R., et al (2019) Algorithms For Treatment of Major Depressive Disorder: Efficacy and Cost-Effectiveness. Pharmacopsychiatry, 52, 117-125. http://doi.org/10.1055/a-0643-4830
Browning, M., Bilderbeck, A. C., Dias, R., et al (2021) The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial. Neuropsychopharmacology, 46, 1307-1314. https://doi.org/10.1038/s41386-021-00981-z
Gilbert, D. A., Altshuler, K. Z., Rago, W. V., et al (1998) Texas Medication Algorithm Project: definitions, rationale, and methods to develop medication algorithms. Journal of Clinical Psychiatry, 59, 345-351. http://www.ncbi.nlm.nih.gov/pubmed/9714262
Hall-Flavin, D. K., Winner, J. G., Allen, J. D., et al (2012) Using a pharmacogenomic algorithm to guide the treatment of depression. Translational Psychiatry, 2, e172. http://dx.doi.org/10.1038/tp.2012.99
Ricken, R., Wiethoff, K., Reinhold, T., et al (2011) Algorithm-guided treatment of depression reduces treatment costs - Results from the randomized controlled German Algorithm Project (GAPII). Journal of Affective Disorders, 134, 249-256. http://dx.doi.org/10.1016/j.jad.2011.05.053
Rollman, B. L., Hanusa, B. H., Lowe, H. J., et al (2002) A Randomized Trial Using Computerized Decision Support to Improve Treatment of Major Depression in Primary Care. Journal of General Internal Medicine, 17, 493-503. https://doi.org/10.1046/j.1525-1497.2002.10421.x
Trivedi, M. H., Kern, J. K., Grannemann, B. D., et al (2004) A Computerized Clinical Decision Support System as a Means of Implementing Depression Guidelines. Psychiatric Services, 55, 879-885. http://dx.doi.org/10.1176/appi.ps.55.8.879
Trivedi, M. H. & Kleiber, B. A. (2001) Algorithm for the Treatment of Chronic Depression. Journal of Clinical Psychiatry, 62 (Suppl 6), 22-29. https://www.ncbi.nlm.nih.gov/pubmed/11310816
Trivedi, M. H., Rush, A. J., Crismon, M. L., et al (2004) Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project. Archives of General Psychiatry, 61, 669-680. http://dx.doi.org/10.1001/archpsyc.61.7.669