An increasing amount of attention has been paid to treatment resistant depression. Although it is quite common to observe nonremission to not just one but consecutive antidepressant treatments during a major depressive episode, a relationship between the likelihood of achieving remission and one's degree of resistance is not clearly known at this time. This study was undertaken to empirically test 2 recent models for staging treatment resistance.
Psychiatrists from 2 academic sites reviewed charts of patients on their caseloads. Clinical Global Impressions-Severity (CGI-S) and Clinical Global Impressions-Improvement (CGI-I) scales were used to measure severity of depression and response to treatment, and 2 treatment-resistant staging scores were classified for each patient using the Massachusetts General Hospital staging method (MGH-S) and the Thase and Rush staging method (TR-S).
Out of the 115 patient records reviewed, 58 (49.6%) patients remitted at some point during treatment. There was a significant positive correlation between the 2 staging scores, and logistic regression results indicated that greater MGH-S scores, but not TR-S scores, predicted nonremission.
This study suggests that the hierarchical manner in which the field has typically gauged levels of treatment resistance may not be strongly supported by empirical evidence. This study suggests that the MGH staging model may offer some advantages over the staging method by Thase and Rush, as it generates a continuous score that considers both number of trials and intensity/optimization of each trial.
"In a systematic review of nine outcomes studies (n = 1279), including cases with highly probable TRD, the condition was shown to be highly recurrent, with up to 80% of patients who required multiple treatments experiencing relapse within 1 year of remission (Fekadu et al., 2009b). Similarly, a retrospective study of the records of 115 patients found that 50.4% of patients did not achieve remission at any time during their treatment (Petersen et al., 2005). Data from the STAR*D study have shown that there is a general increase in relapse rates and a decline in remission rates with each successive treatment step (Figure 4) (Warden et al., 2007; Rush et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: Treatment-resistant depression (TRD) presents many challenges for both patients and physicians. This review aims to evaluate the current status of the field of TRD and reflects the main findings of a consensus meeting held in September 2009. Literature searches were also conducted using PubMed and EMBASE. Abstracts of the retrieved articles were reviewed independently by the authors for inclusion. Evaluation of the clinical evidence in TRD is complicated by the absence of a validated definition, and there is a need to move away from traditional definitions of remission based on severity of symptoms to one that includes normalisation of functioning. One potential way of improving treatment of TRD is through the use of predictive biomarkers and clinical variables. The advent of new treatments may also help by focusing on neurotransmitters other than serotonin. Strategies such as the switching of antidepressants, use of combination therapy with lithium, atypical antipsychotics and other pharmacological agents can improve outcomes, and techniques such as deep brain stimulation and vagus nerve stimulation have shown promising early results. Despite consistent advances in the pharmacotherapy of mood disorders in the last decade, high rates of TRD are still a challenging aspect of overall management.
Journal of Psychopharmacology 01/2012; 26(5):587-602. DOI:10.1177/0269881111431748 · 3.59 Impact Factor
"On the other hand, it has been observed that it may help in identifying possible therapeutic strategies for treatment resistant cases. For instance, Petersen et al. (2005) compared two different methods for staging treatment resistance in major depressive disorder patients. "
[Show abstract][Hide abstract] ABSTRACT: The introduction of "dual diagnosis" had the merit of drawing attention on substance use among patients with mental illness. In due course, as what often happens with innovations, the concept of dual diagnosis displayed considerable limitations and was progressively replaced by comorbidity. This paper critically reviews the limitations of dual diagnosis and comorbidity and formulates an alternative proposal based on clinimetric methods. In many instances of diagnostic reasoning in psychiatry and in clinical psychology, the process ends with the identification of the disorders and their diagnoses. However, diagnostic end-points, the customary guidance of diagnostic reasoning, should be replaced by the conceptualization of disorders as "transfer stations," which are amenable to longitudinal verification and modification. Indeed, diagnoses might encompass a wide range of manifestations, seriousness, prognosis, and response to treatment that need to be evaluated. A new clinimetric approach which takes advantage of clinimetric methods (including macro-analysis, micro-analysis, staging, and evaluation of subclinical symptoms) is proposed. This approach may allow an accurate analysis of the different problem areas of each patient and their hierarchical organization and may yield important implications for mental health and substance abuse clinics.
"An important challenge facing PET neuroimaging of depressive disorders resides, therefore, in determining which aspects of depressive disorders to study next. We propose that particular attention be given to studying antidepressant non-response by PET, because that condition remains a major challenge for medical and social resources, with 25 – 50% of people suffering from major depressive disorder never recovering fully (Rush et al. 2003b;Rush et al. 2008;Fava 2003;Petersen et al. 2005;Berlim and Turecki 2007). Severe aberrations in molecular mechanisms at multiple cerebral sites may be involved in antidepressant non-response (Krishnan and Nestler 2008;Berton and Nestler 2006;Ressler and Mayberg 2007;Drevets et al. 2008). "
[Show abstract][Hide abstract] ABSTRACT: We thank everybody at the Center for Psychiatric Research and the PET Center of Aarhus University for providing a positive atmosphere in which to work. DFS thanks the Danish Medical Research Council for research funding, and PWM is grateful to the EPSRC for the award of a Life Sciences Interface fellowship (EP/E039278/1).
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