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Psychiatric Times. Vol. 23 No. 13
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Algorithm-based Treatment

By Michael A. Fauman, MD, PhD | November 1, 2006

Discussion
What conclusions can we draw from these comprehensive, well-designed, and well-executed studies? Like most complex investigations, they resolve some important questions and raise others. The collaborative care, TMAP, and GAP studies repeatedly indicated that ALGO treatment produces a statistically better clinical response than TAU. Unfortunately, it is not exactly clear what this means because we do not know any of the details of how the TAU patients were treated. For example, it would be useful to know whether physicians who treated patients without algorithms made specific types of mistakes that physicians who did use them avoided. Did they routinely treat patients with too little medication for too short a length of time? If so, those mistakes might easily be corrected. In any case, it would be useful to study the TAU group to determine what errors in treatment decreased the potential effectiveness of their care.

Investigators have suggested 2 possible reasons for the improved outcomes seen in patients treated with algorithms: it could be the content of the ALGO treatments or the diligent, organized man ner in which they are provided to patients.6 The former seems less likely since the sequence and—to some extent—the content of each step in the TMAP and GAP algorithms were different, yet each algorithm demonstrated superior effectiveness over TAU.

The second and more likely reason for the superiority of ALGO treatment is the consistent and organized manner in which care was provided. Trivedi and colleagues11 referred to this diligent care as "measurement-based care" and defined it as "the routine mea sure ment of symptoms and side effects at each treatment visit" followed by an appropriate adjustment of medication. It is possible that the simple act of following the algorithm, like a to-do list, continually reminds the physician of the necessary components of care. It is also possible that there were more resources devoted to ALGO patients and that the use of these resources was responsible for the patients' higher rate of remission.

The STAR*D studies tell us something interesting about the choice of specific treatments at each level in the care of patients with treatment-resistant depression—the treatments are apparently equivalent. It made little difference in level 2 treatment whether a patient was given sustained-release bupropion, sertraline(Drug information on sertraline), or extended-release venlafaxine or whether the initial citalopram(Drug information on citalopram) treatment was augmented with sustained-release bupropion or buspirone(Drug information on buspirone). The same was true in level 3 treatment, where mirtazapine(Drug information on mirtazapine) and nortriptyline(Drug information on nortriptyline) produced similar responses. The results suggest that clinicians could have given patients sus tained-release bupropion or sertraline as the initial medicine, rather than citalopram, and obtained the same results. This is not surprising given the observation noted earlier that even though the TMAP and GAP algorithms were different, ALGO patients in each study did better than TAU patients.

Assessments of the importance of the STAR*D trials vary. Insel15 highlighted the importance of the real-world disease management approach of the STAR*D study, its focus on remission rather than response, and the early results showing that patients required higher doses of citalopram for longer periods than previously thought to achieve remission. Menza16 evaluated several STAR*D reports and suggested that the project only incrementally added to our knowledge of how to use antidepressant medications and would probably not have a great impact on how clinicians prescribe these medications. The results do, however, confirm the findings of other studies and the observations of many clinicians about the management of treatment-resistant depression.

It is well worth noting that, despite the comprehensive approach adopted in the STAR*D and GAP phase 3 studies, only half of the patients involved actually achieved remission.6,17 Clearly, psychiatry has much more to learn about how to treat depression. It is also important to note that even in the ALGO groups, some physicians did not strictly follow the algorithm.8,10 If this occurs in a controlled clinical study, what will happen in general psychiatric practice?

As a group, current studies of the algorithm-based management of treatment-resistant depression may say as much about the process of care as its content. Perhaps the best conclusion that can be drawn from the research thus far is that conscientious, organized, and consistent measurement-based care is as important as, if not more important than, the specific medications used to manage the disorder.

Dr Fauman is the author of Negotiating Managed Care and Study Guide to DSM-IV-TR, both recently published by American Psychiatric Publishing, Inc. He is adjunct clinical associate professor of psychiatry at the University of Michigan and medical director of Magellan Behavioral of Michigan in Farmington Hills.

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References
1. Suppes T, Dennehy EB, Hirschfeld RM, et al. The Texas implementation of medication algorithms: update to the algorithms for treatment of bipolar I disorder. J Clin Psychiatry. 2005;66:870-886.
2. 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;61:669-680.
3. Katon W, Russo J, Von Korff M, et al. Long-term effects of a collaborative care intervention in persistently depressed primary care patients. J Gen Intern Med. 2002;17:741-748.
4. Adli M, Rush AJ, Moller HJ, Bauer M. Algorithms for optimizing the treatment of depression: making the right decision at the right time. Pharmacopsychiatry. 2003;36(suppl 3):S222-S229.
5. Rush AJ, Fava M, Wisniewski SR, et al. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials. 2004;25:119-142.
6. Adli M, Bauer M, Rush AJ. Algorithms and collaborative-care systems for depression: are they effective and why? A systematic review. Biol Psychiatry. 2006;59: 1029-1038.
7. Katon W, Von Korff M, Lin E, et al. Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Arch Gen Psychiatry. 1999;56:1109-1115.
8. Adli M, Berghofer A, Linden M, et al. Effectiveness and feasibility of a standard stepwise drug treatment regimen algorithm for inpatients with depressive disorders: results of a 2-year observational algorithm study. J Clin Psychiatry. 2002;63:782-790.
9. Suppes T, Rush AJ, Dennehy EB, et al. Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania. J Clin Psychiatry. 2003; 64:370-382.
10. Dennehy EB, Suppes T, Rush AJ, et al. Does provider adherence to a treatment guideline change clinical outcomes for patients with bipolar disorder? Results from the Texas Medication Algorithm Project. Psychol Med. 2005;35:1695-1706.
11. 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;163:28-40.
12. Rush AJ, Trivedi MH, Wisniewski SR, et al. Bupropion-SR, sertraline, or venlafaxine-XR after failure of SSRIs for depression. N Engl J Med. 2006;354:1231-1242.
13. Trivedi MH, Fava M, Wisniewski SR, et al. Medication augmentation after the failure of SSRIs for depression. N Engl J Med. 2006;354:1243-1252.
14. Fava M, Rush AJ, Wisniewski SR, et al. A comparison of mirtazapine and nortriptyline following two consecutive failed medication treatments for depressed outpatients: a STAR*D report. Am J Psychiatry. 2006; 163:1161-1172.
15. Insel TR. Beyond efficacy: the STAR*D trial. Am J Psychiatry. 2006;163:5-7.
16. Menza M. STAR*D: the results begin to roll in. Am J Psychiatry. 2006;163:1123.
17. Rubinow DR. Treatment strategies after SSRI failure—good news and bad news. N Engl J Med. 2006; 354:1305-1307.


 
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