Empirical Testing of Two Models for Staging Antidepressant Treatment Resistance

Petersen, Timothy; Papakostas, George I.; Posternak, Michael A.; Kant, Alexis J.; Guyker, Wendy M.; Iosifescu, Dan V.; Yeung, Albert; Nierenberg, Andrew A.; Fava, Maurizio

doi:10.1097/01.jcp.0000169036.40755.16

Cited by 98 publications

(56 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fava: The sequential data from our group suggest that the chances of remitting with sequential monotherapy are meager. 10 They certainly confirm the impression that many clinicians have that the switch data in the literature involved patients whose level of treatment resistance was not prospectively ascertained, and for that reason it is unclear whether these people were truly resistant or not.…”

Section: Dr Trivedisupporting

confidence: 60%

“…Fava: It is a very high rate and may be explained by considering that if there is a 50% chance of remission after every trial of monotherapy, 3 sequential monotherapy trials would have a high cumulative rate of remission. We have done a similar study 10 that was truly sequential, and we found that the remission rates were about 20%. If you look at real-world populations, the chances of remission are very low after 3 sequential monotherapies.…”

Section: Algorithm Treatment Options Monotherapy Versus Combination Tmentioning

confidence: 89%

See 1 more Smart Citation

Use of Treatment Algorithms for Depression

Trivedi¹,

Fava

Marangell³

et al. 2006

J. Clin. Psychiatry

Self Cite

View full text Add to dashboard Cite

The effectiveness of treatment algorithms rests on 3 principles. The 3 broad categories requiring special attention are (1) the determination of the need for sequential treatment algorithms; (2) what should be the placement of various treatment interventions in treatment algorithms with sequenced treatments, e.g., at what point should combination treatments and somatic treatments be included; and finally, (3) what the process or procedure is to implement algorithms in the clinical setting, e.g., the use of recently developed measurement-based care. 1 Dr. Osser: It is clear to me that there are significant differences between the likely outcome of people receiving treatment as usual (TAU) as opposed to algorithm-driven treatment. In the Texas Medication Algorithm Project (TMAP), 2 that difference was seen primarily in the first 3 months. An important factor in that difference was that the care coordinator identified patients who were not doing well and needed to move to the next step in the algorithm. For the next 9 months of the TMAP study, there was no widening of the difference between the algorithm group and the TAU group, suggesting that, from that point on, both groups did reasonably well (Figure 1). Dr. Trivedi: There is also evidence in primary care from Katon and colleagues, 3 which showed that guideline-driven care produces better outcome than TAU. Unützer and coworkers 4 found similar results. All of these studies have used care coordinators in the algorithm-driven treatment groups. The question becomes whether the better outcomes are related to the use of the care coordinator.Dr. Shelton: Dr. Trivedi, according to your TMAP article from 2004, 2 I agree that the first quarter is where the majority of change happens, but if you look at the end of a year of treatment, there still is a substantial difference between the TAU and the algorithm treatment patients. TAU patients did not catch up by the end of a year (see Figure 1), but the differences that are achieved are achieved primarily up front and then maintained throughout the year. Dr. Marangell: The data are fairly consistent in showing that algorithms result in not just better response rates, but improved patient satisfaction as well. Katon et al. 3 found that response rates were 74% with the algorithm versus 44% with TAU and that patients who received algorithmdriven treatment were more likely than patients who received TAU to rate their quality of care as good or excellent (93% vs. 75%).Dr. Fava: If you ask me whether there is a need for treatment algorithms in depression, I would say absolutely, but I question if we truly

show abstract

Section: Dr Trivedisupporting

confidence: 60%

Section: Algorithm Treatment Options Monotherapy Versus Combination Tmentioning

confidence: 89%

Use of Treatment Algorithms for Depression

Trivedi¹,

Fava

Marangell³

et al. 2006

J. Clin. Psychiatry

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, a new staging method to measure treatment resistance was developed, which is termed as the “Dutch Measure for quantification of Treatment Resistance in Depression” (DM‐TRD) (Peeters et al., 2016). In contrast to previous staging methods (Fekadu, Wooderson, Donaldson, et al., 2009; Fekadu, Wooderson, Markopoulou, & Cleare, 2009; Petersen et al., 2005; Souery et al., 1999; Thase & Rush, 1997), the DM‐TRD not only focuses on biological treatments but also includes evidence‐based psychological treatments. For these reasons, we have chosen to apply the criteria of the DM‐TRD for treatment resistance in the current study.…”

Section: Introductionmentioning

confidence: 99%

Mindfulness-based cognitive therapy for patients with chronic, treatment-resistant depression: A pragmatic randomized controlled trial

et al. 2018

View full text Add to dashboard Cite

BackgroundChronic and treatment‐resistant depressions pose serious problems in mental health care. Mindfulness‐based cognitive therapy (MBCT) is an effective treatment for remitted and currently depressed patients. It is, however, unknown whether MBCT is effective for chronic, treatment‐resistant depressed patients.MethodA pragmatic, multicenter, randomized‐controlled trial was conducted comparing treatment‐as‐usual (TAU) with MBCT + TAU in 106 chronically depressed outpatients who previously received pharmacotherapy (≥4 weeks) and psychological treatment (≥10 sessions).ResultsBased on the intention‐to‐treat (ITT) analysis, participants in the MBCT + TAU condition did not have significantly fewer depressive symptoms than those in the TAU condition (–3.23 [–6.99 to 0.54], d = 0.35, P = 0.09) at posttreatment. However, compared to TAU, the MBCT + TAU group reported significantly higher remission rates (χ 2(2) = 4.25, φ = 0.22, P = 0.04), lower levels of rumination (–3.85 [–7.55 to –0.15], d = 0.39, P = 0.04), a higher quality of life (4.42 [0.03–8.81], d = 0.42, P = 0.048), more mindfulness skills (11.25 [6.09–16.40], d = 0.73, P < 0.001), and more self‐compassion (2.91 [1.17–4.65], d = 0.64, P = 0.001). The percentage of non‐completers in the MBCT + TAU condition was relatively high (n = 12, 24.5%). Per‐protocol analyses revealed that those who completed MBCT + TAU had significantly fewer depressive symptoms at posttreatment compared to participants receiving TAU (–4.24 [–8.38 to –0.11], d = 0.45, P = 0.04).ConclusionAlthough the ITT analysis did not reveal a significant reduction in depressive symptoms of MBCT + TAU over TAU, MBCT + TAU seems to have beneficial effects for chronic, treatment‐resistant depressed patients in terms of remission rates, rumination, quality of life, mindfulness skills, and self‐compassion. Additionally, patients who completed MBCT showed significant reductions in depressive symptoms. Reasons for non‐completion should be further investigated.

show abstract

“…In total, we retrieved 327 full electronic references, 16 of which met our selection criteria. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] …”

Section: Literature Search and Reviewmentioning

confidence: 99%

Definition, Assessment, and Staging of Treatment—Resistant Refractory Major Depression: A Review of Current Concepts and Methods

2007

View full text Add to dashboard Cite

Up to 15% of depression patients eventually present with treatment-resistant or refractory depression (TRD), a condition that causes significant social and economic burdens. Our paper aims to summarize the current medical literature on the conceptual and methodologic issues involved in the definition, assessment, and staging of TRD. Method: We reviewed the recently published medical literature to identify papers that specifically discuss TRD. For this, we searched MEDLINE, EMBASE, and PsycINFO for potentially relevant English-language articles published between January 1996 and June 2006. Results: Recent methodologic and conceptual advances have contributed to the achievement of an acceptable level of theoretical consensus on the general meaning of TRD. Accordingly, depression is usually considered resistant or refractory when at least 2 trials with antidepressants from different pharmacologic classes (adequate in terms of dosage, duration, and compliance) fail to produce a significant clinical improvement. Regarding diagnostic assessments, an accurate and systematic evaluation should be made to elicit the potential role of several contributing factors, such as medical and psychiatric comorbidity. Conclusion: Recently, 3 staging methods for TRD have been described, but they currently require extensive empirical support. Future research on TRD should include prospective studies addressing the validity of the proposed criteria, the impact of depression comorbid with other psychiatric disorders and (or) physical conditions, and the possible predictors of treatment outcome. There is an important and clear need for studies that empirically test current definitions, assessment strategies, and staging methods of TRD.

show abstract

Empirical Testing of Two Models for Staging Antidepressant Treatment Resistance

Cited by 98 publications

References 18 publications

Use of Treatment Algorithms for Depression

Use of Treatment Algorithms for Depression

Mindfulness-based cognitive therapy for patients with chronic, treatment-resistant depression: A pragmatic randomized controlled trial

Definition, Assessment, and Staging of Treatment—Resistant Refractory Major Depression: A Review of Current Concepts and Methods

Contact Info

Product

Resources

About