Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

Mood Disorders Program, Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX 75390, USA.
Drug and Alcohol Dependence (Impact Factor: 3.42). 06/2007; 88 Suppl 2:S61-71. DOI: 10.1016/j.drugalcdep.2007.01.007
Source: PubMed


Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

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    • "Another potential challenge is the need for an understanding of the logistics and procedures necessary for administration and review of validated measures, as well as for using MBC to guide clinical decision-making. Although clinicians may be able to independently implement MBC, training efforts and organizational support to (a) establish the utility of self-report assessment, (b) delineate frequency of administration and review of measures, (c) identify methods for optimizing data collection (i.e., use of technology), and (d) establish procedures for using MBC to make clinical judgments and guide treatment may be important to ensure successful implementation of MBC (Slade, Thornicroft, & Glover, 1999; Trivedi et al., 2007). Organizations seeking to implement MBC procedures may wish to consider providing targeted training to optimize MBC's utility. "
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    ABSTRACT: Measurement-based care (MBC) can be defined as the practice of basing clinical care on client data collected throughout treatment. MBC is considered a core component of numerous evidence-based practices (e.g., Beck & Beck, 2011; Klerman, Weissman, Rounsaville, & Chevron, 1984) and has emerging empirical support as an evidence-based framework that can be added to any treatment (Lambert et al., 2003, Trivedi et al., 2007). The observed benefits of MBC are numerous. MBC provides insight into treatment progress, highlights ongoing treatment targets, reduces symptom deterioration, and improves client outcomes (Lambert et al.). Moreover, as a framework to guide treatment, MBC has transtheoretical and transdiagnostic relevance with broad reach across clinical settings. Although MBC has primarily focused on assessing symptoms (e.g., depression, anxiety), MBC can also be used to assess valuable information about (a) symptoms, (b) functioning and satisfaction with life, (c) putative mechanisms of change (e.g., readiness to change), and (d) the treatment process (e.g., session feedback, working alliance). This paper provides an overview of the benefits and challenges of MBC implementation when conceptualized as a transtheoretical and transdiagnostic framework for evaluating client therapy progress and outcomes across these four domains. The empirical support for MBC use is briefly reviewed, an adult case example is presented to serve as a guide for successful implementation of MBC in clinical practice, and future directions to maximize MBC utility are discussed.
    Cognitive and Behavioral Practice 02/2014; 22(1). DOI:10.1016/j.cbpra.2014.01.010 · 1.33 Impact Factor
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    • "Self-report measures are also used in clinical contexts for the measurement of severity on admission, to monitor change during treatment or as a tool for follow up. Frequently, the use of self-report measures is endorsed in practice guidelines, for example Practice guidelines Depression, third edition, published by the American Psychiatric Association (2010) and the Swedish national guidelines for the treatment of depression and anxiety disorders from The National Board of Health and Welfare (Socialstyrelsen 2010), and models for measurement based care have been proposed (Trivedi & Daly 2007). "

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    • "Failure to respond to treatment at any one step is commonly followed by “sequential treatment” in which a subsequent treatment is utilized either alone in combination,12-14 followed by another period of watchful waiting. In most studies, only about 15% of patients will ultimately fail to benefit from sequential medication treatment, but it may take 1 to 2 years to identify the treatment that will get a patient well - and many discontinue treatment before they can recover.15 "
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    ABSTRACT: Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, long and unsuccessful antidepressant trials may diminish patient expectations, reinforce negative cognitions, and condition patients not to respond during subsequent antidepressant trials, thus contributing to further treatment resistance. For these reasons, it is critical to identify reliable predictors of antidepressant treatment response that can be used to shorten or eliminate lengthy and ineffective trials. Research on possible endophenotypic as well as genomic predictors has not yet yielded reliable predictors. The most reliable predictors identified thus far are symptomatic and physiologic characteristics of patients that emerge early in the course of treatment. We propose here the term "response endophenotypes" (REs) to describe this class of predictors, defined as latent measurable symptomatic or neurobiologic responses of individual patients that emerge early in the course of treatment, and which carry strong predictive power for individual patient outcomes. Use of REs constitutes a new paradigm in which medication treatment trials that are likely to be ineffective could be stopped within 1 to 2 weeks and other medication more likely to be effective could be started. Data presented here suggest that early changes in symptoms, quantitative electroencephalography, and gene expression could be used to construct effective REs. We posit that this new paradigm could lead to earlier recovery from depressive illness and ultimately produce profound health and economic benefits.
    Dialogues in clinical neuroscience 12/2009; 11(4):435-46.
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