Treatment Algorithms in Treatment-Resistant Depression

As many as 30% of depressed patients fail to respond to treatment with an antidepressant and at least 60% to 75% may fail to achieve complete remission. 57 In its broadest form, treatment-resistant depression may characterize the majority of depressed patients in therapy and may contribute to the overwhelming morbidity and mortality rates associated with affective illness, 36 , 38 and may account for a disproportionate amount of physician treatment time. The paucity of attention given to treatment-resistant depression has led to inconsistent and confusing treatment approaches that all too often have resulted in a sense of “therapeutic nihilism” among clinicians and patients alike. Moreover, recent data have suggested the possibility that repeated drug trials per se may contribute to treatment-resistant depression. Thus, we recently found that the number of prior drug treatments negatively influenced the response to each succeeding antidepressant treatment in patients with treatment-resistant depression (odds ratio = 0.80). This indicates that the odds of responding to the next antidepressant treatment declines by a factor of approximately 15% to 20% for each prior failed drug treatment. 4 Although many patients with treatment-resistant depression do respond eventually to some drug combination or less conventional drug augmentation strategy, many of these therapies may be associated with an increased risk of drug-drug interactions and certainly require an extended time frame to accommodate a “trial and error” approach. To optimize treatment and to choose more rationally among the vast array of drug combinations and augmentation strategies for treatment-resistant depression, a series of empirically derived treatment algorithms have begun to emerge that may represent a more systematic and less haphazard treatment approach. In a systematic approach, a particular treatment strategy should be (1) shown to be either superior to a comparative treatment in controlled drug trials or superior to other treatments in a series of uncontrolled case reports and (2) based on sound psychopharmacologic theory. In every instance, the risk-to-benefit ratio must be assessed and found to be in the patient's best interest. This article lays a groundwork for efficiently diagnosing and treating the patient with treatment-resistant depression by applying empirically based, systematic treatment algorithms. In this fashion, a safe and aggressive approach can be undertaken in the treatment of treatment-resistant depression in order to reduce the substantial illness-related morbidity and mortality rates.