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Treatment algorithms for treatment-resistant depression
Introduction
An algorithm is a stepwise solution used for problem solving. In medicine, the clinical algorithm, most commonly represented as a flow chart, is used in clinical decision-making processes to address diagnosis and treatment ( ). Since the 1990s, many clinical algorithms have emerged as a consequence of evidence-based medicine (EBM), which is defined as the integration of “individual clinical expertise with the best available external clinical evidence from systematic research” ( ). The “EBM movement” took off in the early 1990s after the term was coined by Gordon Guyatt in 1991 at McMaster University, Canada ( ), and several highly-influential texts were subsequently published on the topic by David Sackett and colleagues ( ; ; ). Over the past few decades, there has been a push toward the use of EBM in mainstream clinical practice. While efficacy and cost-effectiveness of practicing EBM is well-established, there is still a challenge to ensure adherence in clinical settings ( ). It is especially difficult for researchers to replicate the results of systematically-designed randomized-controlled trials in real-world clinical practice ( ). The simplicity and ease-of-use of treatment algorithms may present a solution.
Algorithms differ from clinical practice guidelines (CPGs) in that CPGs provide users with a synthesis of up-to-date data and clinical recommendations to direct their decision making for a specific disease or disorder, whereas algorithms apply this information to create a comprehensive step-by-step formula for the user to follow. While CPGs provide general recommendations and information about the safety and efficacy of potential treatment modalities, algorithms are highly specialized and standardized to provide specific strategies and steps for the treatment of a clinical condition based on information patterns ( ). Often, CPGs will include algorithms to synthesize data for ease of use ( ; ; ). Algorithms in psychiatry aim to minimize inconsistencies in prescribing patterns among clinicians, increase likelihood of remission, minimize side effects, and decrease overall costs for the healthcare system.
In certain medical fields, such as oncology, the integration of genetic and other biological markers to identify diagnostic subtypes has enhanced sensitivity and specificity of treatment selection ( ; ; ). However, the heterogeneity of mood disorder etiology and symptomatology makes it significantly more difficult to apply these techniques in the treatment of depression. Failure to identify valid and reliable biomarkers of MDD, that may be used in conjunction with symptom-based features, has hindered the progress of precision psychiatry and therefore the development of reliable treatment algorithms with high sensitivity and specificity ( ; ; ). In the case of treatment-resistant depression (TRD), failure to develop a gold-standard, universal definition of TRD has further hindered this progress ( ). Nevertheless, in the past two decades, several TRD treatment algorithms have been developed, many of which address treatment strategies across varying stages of treatment resistance.
In this chapter, we summarize the major treatment algorithms of the past three decades, including the seminal algorithms of the 1990s and early 2000s, as well as more modern approaches. The potential benefits of using treatment algorithms, both on an individual patient and societal/economic level, are then examined. Further, we will discuss important considerations when developing and implementing treatment guidelines to treat depression in clinical practice. We end the chapter by considering the future of treatment algorithms for depression, particularly the potential role of pharmacogenetics and computational analysis to enhance algorithm sensitivity and specificity in the world of precision psychiatry.
Early treatment algorithms: GAP, TMAP, and STAR*D
The German Algorithm Project
Established in 1990, the German Algorithm Project (GAP) was the first attempt by a clinical research group to systematically create a treatment algorithm for depression ( ; ). The algorithm was developed by a consensus group of psychiatrists in the Department of Psychiatry at the Free University of Berlin ( Freie Universität Berlin ), combining available literature at the time with clinical expertise, and was specifically designed for inpatient treatment. This was achieved through the development of a “standardized stepwise drug treatment regimen” (SSTR; ; ) ( Fig. 6.1 ).
Among 119 patients enrolled in an observational study to test the efficacy, feasibility, and acceptance of this SSTR, 34% achieved remission (Bech-Rafaelsen Melancholia Scale (BMRS) score ≤ 5), and an additional 34% met response criteria (change in BMRS score ≥ 50%) ( ). However, there was no control group to compare the SSTR to treatment-as-usual (TAU), limiting the generalizability of these results. Further, this study was limited by low enrolment and high dropout rates: more than half of patients meeting initial inclusion criteria were not enrolled because their individual treatment needs could not be met by the SSTR, suggesting a reluctance on the part of their physicians to accept algorithm-guided treatment; and one-third of participants dropped-out of the study, most commonly due to intolerable drug side effects ( ). The SSTR was developed and tested at a single university site which may have led to bias due to lack of diversity among the algorithm developers. Finally, the algorithm only provided a single treatment option at each stage, not allowing for adjustments or differences in specific drug choices based on individual differences ( ).
Addressing several of these issues, two additional phases of the GAP study were completed. The second phase finished in 2000 and compared SSTR to TAU ( ). This study integrated knowledge and insights developed during GAP Phase 1 into an updated SSTR, and also included novel therapeutic agents that had been approved since GAP1, specifically paroxetine and venlafaxine. Participants in the SSTR group achieved remission significantly earlier than TAU (7 vs 12 weeks, on average). However, there were no significant differences in depressive severity scores between the two groups at any time point, neither was there a significant difference between remission rates. A higher drop-out rate in the SSTR versus TAU group (45% vs 16%) was primarily due to physician noncompliance in the SSTR group; withdrawals for physician noncompliance were only applicable to the SSTR group, leading to a biased drop-out rate ( ).
The third phase of GAP was a large-scale, naturalistic study that included participants from 10 psychiatric departments across Germany and tested three different SSTRs against each other and TAU ( ). If the first antidepressant treatment failed, one of the three SSTRs were randomly prescribed: lithium augmentation, dose escalation, or antidepressant switch. Although patients treated according to any SSTR achieved remission more quickly than TAU, the lithium-augmentation group performed the worst of the three SSTRs. However, there was no difference in remission or response rates at any time point between SSTR and TAU. Similar to the previous two phases, drop-out rates were high in the algorithm-treated groups (41%–43%) compared to TAU (19%) ( ).
Texas Medication Algorithm Project
Addressing many of the limitations of the German Algorithm Project, the Texas Medication Algorithm Project (TMAP) began in 1995 and was the first controlled trial to evaluate the effectiveness of algorithm-based treatment in clinical practice ( ; ; ). The developers hypothesized that algorithm-guided treatment would lead to faster and more significant symptom improvement, better functioning, and a lower side effect burden over 1 year, compared to TAU ( ). The TMAP algorithm was developed using a formal consensus, where academic psychiatrists, psychopharmacology specialists, other physicians, mental health consumers, and family members met two discuss the sequence of treatment over a two-and-a-half day conference ( ). Importantly, the TMAP algorithm suggests clinicians take into account individual patient factors, allowing for more flexibility and clinical expertise when determining the next-step for treatment ( ; ; ; ).
A total of 547 participants diagnosed with MDD were enrolled in the original TMAP study ( ). While both algorithm-treated (ALGO) and TAU groups improved after 3 months of treatment, there was a significantly greater reduction in depression severity in ALGO compared to TAU, and this difference remained significant at every follow-up point, up to 1 year (the last timepoint measured). Algorithm-driven treatment was especially effective in comparison to TAU among participants with more severe depressive symptoms and greater functional impairment at baseline.
However, there were limitations to this study design ( ). There was no measurement of physician adherence to the algorithm. The TMAP guidelines required extra and ongoing training of physicians, making algorithm-guided treatment more time consuming for physicians to use compared to TAU (e.g., because of extra paperwork), which may have been detrimental to physician adherence. Further, no randomization techniques were used to assign participants, clinics, or physicians to ALGO or TAU ( ).
The Sequenced Treatment Alternatives to Relieve Depression
Sequenced Treatment Alternatives to Relieve Depression (STAR*D) is an influential study in the field of psychiatry. The goal of this trial was to determine the most effective treatment for individuals with MDD who had an unsatisfactory outcome following initial and subsequent treatment trials ( ; ). An important aspect of this study was the decision to use “remission” (defined as a score of ≤ 5 on the Quick Inventory of Depressive Symptomatology—Clinician Rating 16-item [QIDS-C]) instead of “response” as the primary outcome measure ( ; ). Participants who responded to a treatment trial (defined as a ≥ 50% reduction in QIDS score), but did not achieve remitter status, were combined with the “nonresponders” and moved on to the next treatment level. In other studies where response is used as the primary outcome, responders with clinically significant residual symptoms would be combined with those who achieved remission and not further treated for residual symptoms. STAR*D represents an important shift in psychiatry, particularly in the treatment of mood disorders, where the goal of treatment transitioned from antidepressant response to remission of symptoms and a subsequent return to premorbid levels of functioning ( ; ; ).
Citalopram was selected as the first treatment (level 1) because of its low risk of discontinuation-related side effects and good representation of the SSRI class ( ; ; ). Individuals who did not achieve remission with citalopram were randomized to receive one of a variety of switching or augmentation strategies during level 2 ( ; ). This pattern was repeated twice more in nonremitters. While the Berlin Algorithm Project and TMAP evaluated the clinical benefit of a specific, predeveloped treatment algorithm, STAR*D set out to determine comparative efficacy of specific agents or treatment strategies, using an algorithm-like approach ( ; ).
Nonremitters during level 1 who tolerated citalopram received augmentation with bupropion, buspirone, or cognitive therapy (CT) ( ; ). The use of bupropion as an adjunct was supported by a survey of 400 psychiatrists who endorsed bupropion as their first-choice adjunctive strategy with an SSRI ( ). At the time of STAR*D’s publication, the evidence for this combination strategy was based on anecdotal reports, case series, and small open-label trials ( ; ). STAR*D presented the opportunity to empirically test the effectiveness of bupropion using a large study cohort. Today (2020), while still widely prescribed as an adjunct antidepressant to SSRIs, the evidence supporting bupropion as an adjunct remains weak ( ). At the time of STAR*D, preliminary evidence supported the efficacy of buspirone as an adjunctive agent in those resistant to SSRIs ( ; ; ) however, in a subsequent metaanalysis, there was no evidence of efficacy for buspirone augmentation ( ). At the time STAR*D was launched, the evidence appeared insufficient to justify adding an atypical antipsychotic agent.
Nonremitters during level 1 who did not tolerate citalopram were switched to bupropion, venlafaxine, sertraline, or CT. These medications were selected to test both intraclass (SSRI-to-SSRI [sertraline]) and interclass (SSRI to non-SSRI [bupropion or venlafaxine]) switching strategies ( ; ). A switch to CT is supported in the literature as a reasonable next-step option following antidepressant nonresponse ( ; ; ). Overall, these stage 2 strategies were selected to test prevalent clinical beliefs and common clinical practices for initial SSRI nonresponse ( ; ).
In treatment level 3, those who failed to remit in the previous treatment stage received either adjunctive lithium or triiodothyronine/T 3 augmentation (among the earliest reported antidepressant augmentation strategies) ( ; ), or were switched to mirtazapine or nortriptyline, in an attempt to study how these treatment options compare ( ; ). Finally, level 4 compared treatment strategies that are typically reserved for more treatment-resistant cases, namely the monoamine oxidase inhibitor (MAOI) tranylcypromine or mirtazapine plus venlafaxine. This combination was expected to have a lower side effect burden and greater safety profile compared to the MAOI ( ; ; ; ). An important limitation of this study is that participants were allowed to select their own treatment options for steps 2–4, which may have biased the outcome. Further, the attrition rate was quite high, at 37% overall (from Level 1 to Level 4) ( ).
While STAR*D did not test a specific algorithm per se, it did provide valuable data on the effectiveness of antidepressants and augmentation agents that could be used for future algorithm development. With no clear “winner,” a definitive “best treatment algorithm” could not be developed. The cumulative remission rate in this study was 67% ( ), with likelihood of remission decreasing significantly with number of failed treatments: 37% and 31% of participants remitted after one or two treatment trials, respectively, but the remission rate dropped to 14% and 13% after three and four antidepressant trials, respectively ( ).
The results from STAR*D have informed many subsequent guidelines, algorithms, and other publications on the treatment of mood disorders ( ; ; ).
Subsequent treatment algorithms: 2015–20
Since the seminal TMAP and STAR*D studies, interest in treatment algorithms for depression seems to have waned, with attention shifting to CPGs. Guidelines incorporate more detailed descriptions of clinical evidence and grade recommendations according to efficacy and safety considerations, while allowing clinicians to use these data to create their own treatment plans. However, these are often biased toward depression in the primary-care setting and do not provide progressive steps to deal with treatment-resistance.
Among treatment guidelines published since 2015, several have included algorithms to synthesize data presented in the CPG. The most influential algorithms include the Royal Australian and New Zealand College of Physicians (RANZCP), Canadian Network for Mood and Anxiety Treatments (CANMAT), and the Maudsley Prescribing Guidelines (MPG) algorithms ( ; ; ). Other treatment algorithms published since 2015 are summarized ( Table 6.1 ), as well as CPGs without algorithms ( Table 6.2 ) ( ; ; ; ; ; ; ; ; ; ; ).
Table 6.1
Other treatment algorithms for depression (since 2015).
| World Federation of Societies of Biological Psychiatry ( ) |
| Choose initial AD based on individual patient factors: (a) intolerance: switch to another AD with evidence of better tolerability; (b) inadequate response after 2–4 weeks: increase dose (where appropriate); (c) inadequate response: augmentation strategy* if initial antidepressant is an SSRI, first try combining with a presynaptic autoreceptor inhibitor (e.g., mirtazapine) before trying an augmentation strategy. Inadequate response: AD switch or consider ECT * 1st choice: lithium, quetiapine, or aripiprazole; 2nd choice: T 3 , T 4 , or olanzapine + fluoxetine. Note: Consider adding psychotherapy at any time during treatment |
| Department of Veterans Affairs and Department of Defence ( ) |
| (a) Mild/moderate MDD: monotherapy or pharmacotherapy + psychotherapy; (b) severe/complicated MDD: refer to speciality care or pharmacotherapy + psychotherapy; (c) remission: continuation, maintenance treatment, and relapse prevention; (d) no remission: provide referral to higher level of care/speciality care |
| Florida Medicaid Drug Therapy Management Program ( ) |
| Psychotherapy*, SSRI, SNRI, vortioxetine, bupropion, or mirtazapine for 4 weeks: (a) partial response: continue for another 2–4 weeks or treat as no response; (B) no response: dose optimization, evaluate adherence, AD switch, monotherapy + psychotherapy, or combine current AD with aripiprazole, brexpiprazole, or another AD. No response and/or poor tolerability: evaluate adherence, seek psychiatric consultation, and/or try TCA, MAOI, ECT, TMS, or (SSRI or SNRI) + (quetiapine or lithium or T 3 or l -methylfolate or SAMe). No response and/or poor tolerability: reevaluate diagnosis, switch to MAOI, l -methylfolate augmentation, other neuromodulatory approach, intravenous ketamine, or one of the following triple-drug combinations: (SSRI or SNRI) + mirtazapine + bupropion (SSRI or SNRI) + mirtazapine + lithium (SSRI or SNRI) + bupropion + atypical antipsychotic * Evidence-based, i.e., CBT, IPT, or behavioral activation |
| Korean Society for Affective Disorders ( ) |
| (a) Mild/moderate MDD: AD monotherapy; (b) severe MDD: AD monotherapy or AD + atypical antipsychotic. No response: AD switch, AD combination, or augment with atypical antipsychotic. No response: AD combination, augment with or switch to atypical antipsychotic, or try another augmentation strategy |
| Psychopharmacology Algorithm Project at Harvard South Shore Program ( ) |
| Nonmelancholic depression sertraline, escitalopram, or bupropion (if not previously tried). No/partial response: switch to another of the above ADs, a dual-action agent (venlafaxine, mirtazapine), TMS, SAMe, or St. John’s wort; or augment with omega-3 fatty acid, l -methylfolate, SAMe, light therapy, quetiapine, risperidone, aripiprazole, bupropion, mirtazapine, lithium or T 3 . No/partial response: switch to TCA or venlafaxine + mirtazapine, augment with ECT, or try another of the above options. If the patient has atypical features, first try an MAOI (selegiline or phenelzine) or SSRI + aripiprazole. Severe melancholic depression. Determine if there is an urgent indication for ECT (a) urgent indication: try ECT, or ketamine if ECT is refused or unsuccessful; (b) no urgent indication: try venlafaxine, mirtazapine, or TCA. No/partial response: try another of venlafaxine, mirtazapine, or TCA; or augment with lithium or T 3 |
AD , antidepressant; SSRI , selective serotonin reuptake inhibitor; ECT , electroconvulsive therapy; T3/T4 , thyroid hormone; MDD , major depressive disorder; SNRI , serotonin-norepinephrine reuptake inhibitor; TCA , tricyclic antidepressant; TCA , tricyclic antidepressant; MAOI , monoamine oxidase inhibitor; TMS , transcranial magnetic stimulation; SAMe , S -adenosyl- l -methionine; CBT , cognitive behavioral therapy; IPT , interpersonal therapy.
Table 6.2
Clinical guidelines without algorithms (since 2010).
| American Psychiatric Association |
| Based on literature review, developed by psychiatrists, and reviewed by 15 organizations and an Independent Review Panel Provides guidance for acute, continuation, and maintenance treatment, as well as treatment discontinuation Addresses pharmacotherapy, somatic therapies, psychotherapy, and complementary and alternative medicine Guidelines for optimization, switching, augmentation, and combination strategies Guidelines for special populations |
| British Association for Psychopharmacology ( ) |
| Based on literature review and consensus meeting with experts in depressive disorders Provides guidance on acute treatment with antidepressants, psychological and behavioral treatments, physical treatments, and complementary and alternative medicine Guidelines for treatment nonresponse and relapse prevention/treatment Guidelines for special populations |
| American College of Physicians ( ) |
| Based on randomized-controlled trial data Reviews psychotherapies, complementary and alternative medicine, exercise, and second-generation antidepressants Discusses briefly switching and augmentation strategies |
| The National Institute for Health and Care Excellence ( ) |
| Recommends tailoring care and treatment based on depressive severity Discusses general principles of care and first-line treatments Guidelines for addressing poor response to treatment, electroconvulsive therapy, and relapse prevention |
| Chinese Society of Psychiatry ( ) |
| Based on recent literature and other, international guidelines Discusses diagnosis and acute, continuation, and maintenance treatment |
| French Association for Biological Psychiatry and Neuropsychopharmacology ( ) |
| Specifically developed for treatment-resistant depression Based on scientific evidence and expert clinicians’ opinions Discusses identification of treatment resistance, indications for hospitalization, and relapse prevention Guidelines for antidepressant medications, switching strategies, adjuvant treatments, combination strategies, psychotherapy, and brain stimulation |
The Royal Australian and New Zealand College of Physicians clinical practice guidelines for mood disorders (RANZCP)
The RANZCP guidelines are based on both literature review and expert consensus ( ). Their treatment algorithm recommends psychosocial and psychological interventions as first and second treatment steps, primarily addressing community primary-care strategies for MDD. Further, these guidelines reflect regional differences in practice patterns in treating more resistant depression. For example, the authors include an MAOI as a third-line monotherapy before considering augmentation strategies, in contrast to other recent guidelines, where adjunctive atypical antipsychotics are recommended before an MAOI, due to the tolerability and safety issues, as well as the general lack of experience among prescribers in using MAOIs ( ; ; ; ).
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