Challenges and Algorithm-Guided Treatment in Major Depressive Disorder
Richard C. Shelton, MD (Chair)
Department of Psychiatry, Vanderbilt School of Medicine, Nashville, Tennessee
Madhukar H. Trivedi, MD
Mood Disorders Research Program and Clinic, University of Texas Southwestern Medical Center, Dallas
Challenges in the Treatment of MDD
MDD is a common, recurrent, and often chronic condition with a lifetime prevalence of approximately 16% in US adults.1 One study2 found that 85% of patients who recovered from an index episode of major depression experienced at least 1 recurrence during a 15-year follow-up period. Additionally, each successive episode dramatically increases the risk for another (1 episode = a 60% chance of a second, 2 episodes = a 70% chance of a third, and 3 episodes = a 90% chance of a fourth).3 Complicating the overall MDD picture is the complex etiology of the disorder. Researchers have identified genetic and environmental risk factors for MDD, but the underlying cause of depression has not yet been discovered.4 Further, MDD is heterogeneous in course and treatment response, making treatment selection for individual patients difficult.
Despite the high prevalence of major depression, the disorder is underdiagnosed and undertreated. A meta-analysis5 examining the diagnosis of depression in mostly urban primary care settings found that less than 50% of clinical depression cases were identified by physicians. When depression is correctly diagnosed, only a minority of patients receive adequate treatment.1
Even among MDD patients who are treated with an appropriately dosed antidepressant for an adequate duration, response is limited and remission rates are low. According to the STAR*D study,6 only about half of patients achieved clinical response, with about one-third or less of those patients achieving remission after an initial antidepressant trial. Patients partially responding or not responding to a first-line treatment may respond or remit after subsequent interventions are employed. However, response and remission rates decrease with each new treatment attempt,7 showing that individualizing first-line options is crucial for the long-term success of antidepressant therapy. When patients do respond or remit, relapse rates are still high. The STAR*D study7 found that one-third of patients achieving remission and nearly two-thirds of patients achieving response with a first-line antidepressant relapsed within 1 year (AV 1).
Factors affecting antidepressant treatment outcomes, aside from medication efficacy, include tolerability of adverse events and the related issue of noncompliance with prescribed medication regimens. The chronic and recurrent nature of MDD, the common underdiagnosis and undertreatment of the disorder, the high rates of relapse, and the poor efficacy and frequent adverse effects of available antidepressants make the successful treatment of MDD extremely challenging. Treatment algorithms can improve care for patients with MDD by assisting clinicians in selecting appropriate first-line treatments and subsequent therapy options should the first-line selection prove insufficient.
Treatment Algorithms in MDD
The use of algorithms can enhance the quality of patient care and standardize the treatment of depression, eliminating variability in treatment across populations.9 The adoption of an algorithm can also help to integrate new science into clinical settings and facilitate the implementation of measurement-based care, which is the systematic measurement of the major components of a disorder and the current status of the patient. Algorithms provide clinicians with timeframes at which treatment decisions should be made, ie, critical decision points. For example, at 4, 6, or 8 weeks after initiating an antidepressant, clinicians are prompted to assess response using a standardized assessment tool and to adjust treatment as necessary if sufficient progress has not been made. If changes are needed, algorithms provide treatment options according to individual patient characteristics. A benefit inherent in the use of algorithms is improved documentation of disease management, which is helpful for clinicians, particularly those using a measurement-based treatment approach.
The TMAP10 algorithms attempted to improve patient outcomes through reducing the existing variance in patient care.9 By providing a framework for clinical decision-making with standardized treatment strategies, consistent treatment could be delivered across clinicians, patients, and settings. Trivedi et al11 compared TMAP algorithm-guided treatment with treatment as usual in patients with MDD. All patients in the study improved, but significantly greater reductions in symptom scores were achieved by patients receiving algorithm-guided treatment as measured by the IDS-SR-30. Other studies of algorithms have found similar results. For example, 2 studies12,13 of other MDD algorithms found increased remission rates and reduced time to remission among patients receiving algorithm-based care compared with those receiving treatment as usual (AV 2).
While helpful in the treatment of depression, algorithms have limitations. New science is constantly emerging, so if clinicians do not use the most recently updated algorithms or if the algorithms themselves are not regularly updated, patients may inadvertently receive suboptimal care. Choosing the most appropriate antidepressant treatment strategy for a given patient is a complicated procedure, and an algorithm may not be able to incorporate all the components of treatment-related decision-making. The algorithm format itself requires clinicians to select treatments from a menu of options, which is not always helpful in individualizing treatment from patient to patient.
MDD, with its lifetime chronicity, high rates of relapse, and low response rates to available antidepressants, is a difficult-to-treat disorder. However, changes in treatment approaches can improve the quality of care and patient outcomes compared with what are generally seen with treatment as usual. The use of standardized assessments and algorithms at all treatment stages is essential for the optimal management of acute depression and the prevention of depressive relapse in the long-term. As opposed to the traditional method of simply asking patients how they have been doing—an approach which often provides an incomplete picture because the patient’s answer is usually limited to a recollection of the past several days—the implementation of measurement-based care will help clinicians reliably track disease progression and treatment response. By regularly receiving reliable feedback about the patient’s status, the clinician can make decisions in a timely manner so that patients do not wait weeks or months before changes to inadequate medication regimens are made.
For Clinical Use
- Use measurement-based care to systematically track symptoms, medication-induced side effects, and treatment response in patients with MDD
- Use treatment algorithms to assist in selecting and individualizing first-line treatment selection and subsequent treatment options for patients with MDD
IDS-SR-30 = 30-item Inventory of Depressive Symptomatology–Self-Report
MDD = major depressive disorder
STAR*D = Sequenced Treatment Alternatives to Relieve Depression
TMAP = Texas Medication Algorithm Project
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- Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–3105.
- Mueller TI, Leon AC, Keller MB, et al. Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. Am J Psychiatry. 1999;156(7):1000–1006.
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Washington, DC: American Psychiatric Association; 2000.
- Nemeroff CB, Vale WW. The neurobiology of depression: inroads to treatment and new drug discovery. J Clin Psychiatry. 2005;66(suppl 7):5–13.
- Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet. 2009;374(9690):609–619.
- Trivedi MH, Rush AJ, Wisniewski SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40.
- Rush AJ, Trivedi JH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):1905–1917.
- Nemeroff CB. Improving antidepressant adherence. J Clin Psychiatry. 2003;64(suppl 18):25–30.
- Gilbert DA, Altshuler KZ, Rago WV, et al. Texas Medication Algorithm Project: definitions, rationale, and methods to develop medication algorithms. J Clin Psychiatry. 1998;59(7):345–351.
- Suehs BT, Argo TR, Bendele SD, et al. Texas Medication Algorithm Project Procedural Manual: Major Depressive Disorder Algorithms. Austin, TX: Texas Department of State Health Services; 2008. http://www.dshs.state.tx.us/mhprograms/disclaimer.shtm. Accessed January 21, 2011.
- Trivedi MH, Rush AJ, Crismon ML, et al. Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project. Arch Gen Psychiatry. 2004;61(7):669–680.
- Bauer M, Pfennig A, Linden M, et al. Efficacy of an algorithm-guided treatment compared with treatment as usual: a randomized, controlled study of inpatients with depression. J Clin Psychopharmacol. 2009;29(4):327–333.
- Yoshino A, Sawamura T, Kobayashi N, et al. Algorithm-guided treatment versus treatment as usual for major depression. Psychiatry Clin Neurosci. 2009;63(5):652–657.