In my last 2 columns, I discussed the problems associated with clinical practice guidelines and described how some physicians use them in practice. In this column, I will review studies on the validation of clinical algorithms, which are 1 of the 2 main types of practice guidelines. Algorithms are detailed, step-by-step flow charts that outline the recommended treatment for patients with a specific disorder.1
Initial studies—such as the stepped collaborative care intervention, Texas Medication Algorithm Project (TMAP), and German Algorithm Project (GAP) phase 2—predominantly investigated whether following an expert opinion-based clinical algorithm (irrespective of the content of the algorithm) led to a better outcome than treatment as usual did.2-4 Subsequent studies, including the Sequenced Treatment Alter na tives to Relieve Depression (STAR*D) and GAP phase 3, focused on comparing the effectiveness of different treatments that were provided at each step in a sequenced algorithm.5,6 The former studies tell us something about the process of care, or how treatment should be provided. The latter tell us about the content of care, or which treatment should be provided at each stage in the algorithm.
Clinical algorithm studies
Several studies suggest that patients with a major depressive or bipolar disorder who are treated according to an accepted clinical algorithm (ALGO) have more successful outcomes than those treated as usual (TAU).6 Some of these studies, and their results, are discussed below.
Stepped collaborative care study
Katon and colleagues3,7 studied the effect of a stepped collaborative care intervention for primary care patients who had persistent or treatment-resistant depression that did not respond to several weeks of conventional antidepressant treatment. Patients were divided into 2 groups: an ALGO group, treated using a "stepped-care" algorithm that included education and structured clinical interventions, and a TAU group, who were simply told to speak with their primary care physician about treatment for depression. Depression was measured using the 20-question depression subscale of the Hopkins Symptom Checklist-90. Patients in the ALGO group who had moderately severe depression—but not those who had severe depression—were significantly improved compared with patients in the TAU group (P = .004) after several months of treatment.
In the GAP, Adli and colleagues8 developed a standardized stepwise drug treatment regimen (SSTR) for treatment-resistant depression. In a randomized controlled study, they used the Bech-Rafaelsen Melancholia Scale (BRMS) to compare the outcome of patients treated by SSTR (n = 74) and TAU (n = 74).4 SSTR-treated patients had a better outcome than TAU patients (BRMS scores: SSTR = 5.4, TAU = 9.5; P < .01). The SSTR group also had a significantly higher dropout rate (45%) than the TAU group (16%). The authors commented that 33% of the dropouts were due to physician non compliance with algorithm rules.
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.