Current Psychiatry Reports

, Volume 14, Issue 4, pp 370–375 | Cite as

Feasible Evidence-Based Strategies to Manage Depression in Primary Care

  • Benji T. Kurian
  • Bruce Grannemann
  • Madhukar H. Trivedi
Psychiatry in Primary Care (BN Gaynes, Section Editor)

Abstract

According to the World Health Organization, major depressive disorder (MDD) is a leading cause of disability-adjusted life years worldwide. However, recent evidence suggests depression remains undertreated in primary care settings. Measurement-based care (MBC) is an evidence-based strategy that can feasibly assist primary care physicians in managing patients with MDD. Utilizing health information technology tools, such as computer decision support software, can improve adherence to evidence-based treatment guidelines and MBC at the point of care.

Keywords

Depression Primary care Evidence-based guidelines Measurement-based care Clinical assessments Clinical decision support systems Electronic health records Barriers to implementation Residual symptoms Patient-centered outcomes Suicidal ideation Suicide-related behavior Medication side effect Medication adherence Treatment Remission Computer decision support systems 

Introduction

Depression is a common, often chronic, recurrent disease affecting a vast number of people worldwide [1, 2]. The longitudinal effects of major depressive disorder (MDD) are wide-ranging and can include disruptions in physical and social function resulting in disability. Over the past 20 years new treatments have been developed to improve symptomatology and evidence-based guidelines have been introduced to inform treatment decisions. Unfortunately, recent evidence suggests that in real-world clinical practice settings these new treatments and guidelines are not being implemented effectively [3, 4, 5].

Targeting Remission

There is considerable evidence that incomplete remission is the rule and results in significant residual symptoms and psychosocial dysfunction [6, 7]. The consequence of a partial response (i.e., not achieving remission) is manifested through residual depressive symptoms, which increase the risk for, and shorten the time to relapse of, depressive symptoms [7]. In addition, the frequency and severity of residual depressive symptoms correlates with worsening psychosocial functioning [8]. Even amongst patients achieving remission, based on research standards [i.e., 17-item Hamilton Rating Scale for Depression17 (HRSD17) ≤7] those with one or more residual depressive symptoms have greater psychosocial impairment than remitters with no residual symptoms [8, 9].

Most commonly residual symptoms are described as one or more continued manifestations of core depressive symptomatology: sleep, interest, guilt, energy, concentration, appetite, psychomotor activity, suicidal ideation, and depressed mood. In fact, a number of agents are specifically targeted at alleviating some of these specific symptoms, such as insomnia and fatigue.

Treatment strategies for residual symptoms (i.e., partial and non-responders to antidepressant medications) consist of either switching to another agent or augmenting the original antidepressant with an additional medication. Recently, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, which was a “real-world” effectiveness study, revealed that both strategies are efficacious, and the decision to switch or augment should be based on treatment response, tolerability (i.e., side effects), and patient preference [10]. However, in routine clinical practice the decision to switch antidepressant treatment or augment treatment is a decision that is not made with the benefit of carefully measured depressive symptoms and medication side effects. Practitioners also differ in how they assess the outcomes of treatment (symptomatology and tolerability), utilizing global, rather than specific, assessments, despite evidence identifying the reduced accuracy of the former [11]. Consistently measuring specific symptoms can be accomplished by utilizing corresponding assessment tools and can provide a framework for symptom improvement in the context of medication tolerability. Thus, a set of pragmatic strategies to implement evidence-based, clinical practice guidelines for depression in real-world settings can assist in overcoming some of the wide treatment variations seen in clinical practice and assist practitioners in targeting, and achieving, remission for their patients.

Measurement-Based Care

A simple, practical way of achieving remission in real-world settings is to utilize the strategy of measurement-based care (MBC) [12]. Developed as part of the STAR*D trial, MBC is a clinical practice guideline designed to facilitate the implementation of evidence-based depression care in real-world settings [13]. The components that make up MBC include standard assessments of symptoms, side effects, and treatment adherence ( measured at each visit), correlating with an evidence-based treatment algorithm for MDD with associated critical decision points. Although symptom and function measures are consistently used to drive the treatment of other chronic disorders (e.g., diabetes), the use of symptoms and side effect measurement to assist with decision-making during the treatment of depression has been surprisingly absent in clinical practice, as well as research settings.

How Does MBC Work?

The first priority of measurement-based care is to promote decision-making through the routine use of validated rating scales (clinician-administered or patient self-report) that measure symptoms, treatment side effects, and medication adherence at every clinic visit. The next facet of MBC is to integrate these measurements as part of a treatment algorithm at defined intervals, called critical decision points (CDPs), to guide treatment decisions (i.e., dosage modification and treatment duration) until remission is achieved (see Table 1 for details).
Table 1

Components and strategies for measurement based care (MBC)

MBC components

Sample scales

Goal of treatment

When to assess

Guidelines

Depressive symptoms

QIDS-SR, PHQ-9

Remission: QIDS ≤5 PHQ-9 <5

All components of MBC should be measured at each visit. CDP’s occur at weeks 0, 4, 6, and 9 (for acute phase)b

• Assuming SE tolerability and adherence, increase dose until remission

Associated symptoms

HAMA, PSQI, CHRT, CAST

Mild symptoms: HAMA <17, PSQI <5, CHRT, CASTa

• Switch vs Augmentationc

Side effects

FIBSER, SAFTEE

Tolerable SE: FIBSER ≤4

Adherence

PAQ

Adherence: Taken ATD >75 %

aCHRT and CAST are tools utilized to measure suicidal behavior. The goal of treatment should be the absence of suicidal behavior.

bCDPs are designed to make treatment decisions (dose titration, switch, or augment) based on symptoms, side effects, and adherence.

cIn general, assuming side effects are tolerable and the patient is adequately adherent to treatment, the decision to switch or augment is recommended to be made at week 9.

QIDS-SR, Quick Inventory of Depression Symptomatology – Self-Report; PHQ-9, 9-item Patient Health Questionnaire; HAMA, Hamilton Anxiety Rating Scale; PSQI, Pittsburgh Sleep Quality Index; CHRT, Concise Health Risk Tracking; CAST, Concise Associated Symptom Tracking; FIBSER, Frequency, Intensity, Burden of Side Effects Rating; SAFTEE, Systematic Assessment for Treatment Emergent Events; PAQ, patient adherence questionnaire; ATD, antidepressant; CDP, critical decision points; SE, side effect.

Specific details of MBC and how it is integrated in clinical practice have been outlined elsewhere [12, 14••, 15], but it is important to note that, in addition to depressive symptoms, the concept of MBC can also be applied to related and residual symptoms (such as anxiety, insomnia, and suicidal behaviors), and corresponding treatments (see Table 2 for details).
Table 2

Residual symptoms present with major depressive disorder and their associated measurement scales

Symptom/subtype

Prevalence (%)

Associated measurement scale

Anxiety

46–60a

Hamilton Anxiety Rating Scale (HAM-A)

Insomnia

82

Pittsburgh Sleep Quality Index (PSQI)

Decreased concentration

71

Massachusetts General Hospital Cognitive and Physical Fatigue Questionnaire (CPFQ)

Irritability

34–66

Anger Attack Questionnaire

Suicidal ideation/behavior

17a

Concise Health Risk Tracking (CHRT)

Concise Associated Symptom Tracking (CAST)

aPrevalence reflects % co-occurring with MDD, not necessarily as residual symptom.

Symptoms

With regard to measuring depressive symptom severity, STAR*D utilized the Quick Inventory of Depressive Symptomatology (QIDS) [16, 17, 18], a 16-item questionnaire based on the nine Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV text revision (TR) core-symptom criteria for major depressive disorder. The QIDS is quick, easy to use, translated into other languages, and available in both clinician (QIDS-C16) and self-rated (QIDS-SR16) versions, which can be downloaded for free (http://www.ids-qids.org/). Both clinician and self-report versions are reliable and have been validated [16, 17, 18]. Measurement-based care does not mandate the use of the QIDS, but rather recognizes that other, similar, alternative measures, for example the Patient Health Questionare-9 (PHQ-9) [19] and the Beck Depression Inventory-II (BDI-II) [20], are also valid depressive symptom severity questionnaires. Similarly, other symptom constructs can also be incorporated as part of MBC. For instance, for patients with anxiety, insomnia, or suicidal behaviors can have the Hamilton Anxiety Rating Scale (HAM-A) [21], Pittsburgh Sleep Quality Index (PSQI) [22], Concise Health Risk Tracking (CHRT) [23], or Concise Associated Symptom Tracking (CAST) [24] scales administered and measured at treatment visits.

Side Effects

Another important aspect of MBC is the systematic measurement of side effects, as they specifically pertain to antidepressant medications. MBC guidelines, based on STAR*D methodology, recommend using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) [25]. Recent emphasis on personalizing treatment to manage side effects indicates possible benefits of more systematic self-rated instruments, such as the Systematic Assessment for Treatment Emergent Events (SAFTEE) [26].

Patient Adherence

Adherence was a multi-tiered component of STAR*D, which included assessing both patient adherence to their prescribed antidepressant medication and physician adherence to the algorithm’s recommended guidelines [14••]. For the purpose of completing measurement-based care visits, we focused on the former—patient adherence. In general, patient self-reported adherence has been shown to accurately reflect pharmacy claims [27]. As such, we advocated the routine use of patient self-reports aimed at assessing adherence to treatment over the past 14 days. More complicated approaches to assessing adherence may possibly be superior, but require more complicated measurements and may not be easily deployed in practice.

MBC: An Overview

Medication adherence, clinical status (i.e., remission, partial response), and side effect tolerability at a given critical decision point determines the treatment plan for that visit. Using depressive symptoms as an example, remission is defined by a QIDS-C16 ≤5, a partial response is a QIDS-C16 = 6–8, and an inadequate response is a QIDS-C16 ≥9. Comparable cutoffs are available for other measures, such as the PHQ-9 [19]. In STAR*D, critical decision point visits were recommended at weeks 0, 4, 6, 9, and 12 at each treatment stage (level) or until remission or an adequate treatment response was obtained.

A MBC approach is an essential component of clinical decision-making in the treatment of MDD, allowing the physician to individualize decisions about care for the patient based on the patient’s progress and ability to tolerate the medication [12, 15]; however, to be effectively used with patients, practitioners must adopt this guideline and continue to use it in their practices. As an effectiveness trial focused on outcomes, STAR*D used multiple strategies to implement MBC but did not evaluate the impact of these strategies on the process of care, such as decision-making, consistent with the guideline or patient outcomes. STAR*D also did not measure the sustained use of the guideline following acute treatment or evaluate the relationship of organizational factors to the care process or outcomes.

Ensuring Fidelity to Evidence-Based Care Strategies

A recent Institute of Medicine (IOM) report report describes a quality chasm between the health care received by patients and the care that is possible [28]. Changes are especially needed, according to the report, in the care of chronic conditions—including depression. One of the IOM’s conclusions is that implementation of “carefully designed, evidence-based care processes, supported by automated clinical information and decision support systems, offer the greatest promise of achieving the best outcomes.”

The IOM outlines six challenges to the healthcare system, including: (1) the redesign of care processes to treat chronic illness; (2) the effective use of information technology; (3) the incorporation of care process and outcome measures into the practice setting; (4) the expanding knowledge base and its application to care provider skill levels; (5) coordination of care; and (6) continual improvement in team effectiveness [28].

Clinical practice guidelines have the potential to make a substantial contribution to the IOM challenges in improving the delivery of care for depression. Such guidelines, one of a multitude of evidence-based care practices, have been available to clinicians for some time, yet the implementation or adoption of this information in actual clinical practices lags far behind. Despite wide promotion, clinical practice guidelines have had limited effect in changing physician behavior [29, 30, 31]. Failure to initiate or intensify treatment when indicated has been called clinical inertia [32, 33]. It is also clear that knowledge is insufficient to generate behavior change, even though passive dissemination of evidence-based approaches is the most frequently used approach in practice settings [34]. Cabana and colleagues conducted an extensive literature review to assess barriers to implementing guidelines in clinical practice [35]. Essentially, they classify barriers to guidelines adherence in two categories: internal barriers (lack of awareness, lack of familiarity, lack of agreement, lack of self-efficacy, lack of outcome expectancy, the inertia of prior practice) and external barriers (time limitations, patient preference, environmental constraints). The authors conclude that barriers to adherence are varied and system dependent, and, ultimately, it is important to identify the potential barriers specific to a given clinical setting.

Computer Decision Support Software

With recent advances in health information technology, and the increasing availability and utilization of electronic health records (EHRs), it is becoming increasingly imperative to electronically incorporate any treatment guideline tools. Web-based computer decision support systems (CDSSs) can exist as stand-alone systems or can be integrated with EHRs to assist clinicians with their delivery of pharmacological treatment for patients with chronic mental illnesses. For the treatment of MDD, these tools were developed and implemented as part of several studies utilizing MBC and evidence-based guidelines to provide feedback to clinicians at the point of care [5, 36•]. Data from the latter of these studies [36•] supports the effectiveness of CDSSs in reducing symptomatology for depressed patients in a primary care setting.

The concept of MBC for CDSSs is to systematically assess a wide range of symptoms, side effects, and treatment adherence at defined intervals using validated ratings, and suggest evidence-based treatment guidelines accordingly. As with most chronic illnesses, the goal of treatment for patients is to achieve remission (i.e., free of symptoms) in the short term and recovery over the long term.

A unique aspect of utilizing a CDSS that separates it from prior treatment guidelines is the role that patients have in their care. Increasingly, it is important to engage patients in their care [37], and with recent technological advances it is possible to do so for patients and integrate results as part of their routine outpatient visits. These visits incorporate a shared decision-making model, in that both patients and clinicians complete validated rating scales that inform treatment decisions. More specifically, prior to seeing their clinician a patient is expected to complete computerized MBC assessments forms that detail symptoms, side effects, and adherence specific to their treatment visit. Information from these self-reports will then be available to the clinician, so that the completed rating scales can be validated by the practitioner and can further guide the treatment visit.

Garg and colleagues [38] conducted a literature review assessing the impact that CDSSs have in clinical practice. Their review included 100 studies—the majority of which found that the CDSS did improve practitioner performances. However, the review did not support the assumption that the CDSS improves patient-level outcomes (only 7 out of 52 trials reported improvement). Whilst many of the studies were underpowered to detect a clinically significant difference [39•], this may partly be due, once again, to barriers in implementation, including: failure of use, guideline disagreement, and external barriers (i.e., workflow limitations) [38].

Conclusions

In summary, recent clinical trial data from real-world practice settings reveal that the majority of patients will not achieve full remission of their depressive symptoms by the end of their first treatment [13] and multiple treatment efforts are likely needed. To improve the likelihood of patients receiving adequate treatment and, accordingly, better outcomes, clinicians should apply feasible evidence-based treatment strategies that involve systematic and objective monitoring of symptom severity, side-effect burden, and medication adherence. These three domains constitute the treatment construct known as MBC [12, 14••, 15]. In addition, MBC can also provide monitoring for residual symptoms associated with non-remission (insomnia, anxiety, suicidal behavior). Specifically, as it relates to suicidal behavior, implementing patient-self reports, such as the CHRT and the CAST that assess for risk and associated symptoms, respectively, is an important tool for practitioners to monitor and assess for treatment-emergent suicidal ideation [23, 24].

Furthermore, integrating MBC as part of a CDSS can maximize implementation- and utilization-recommended treatment guidelines, ensuring fidelity to up-to-date, evidence-based practices. However, prior to implementing a CDSS two items should be considered: (1) the identification of potential barriers to CDSS adherence; and (2) integration of the CDSS as part of the clinical workflow. For example, if a clinical practice is utilizing an EHR the CDSS should ideally be integrated as part of the EHR, such that double data entry is avoided. Similarly, depression, and associated mental disorders and residual symptoms rely on patient self-reports to guide clinical practice. As such, sufficient time should be built into a clinical visit such that patients are able to complete assessments prior to their face-to-face visit with the practitioner, and that the visit can then be utilized to jointly review treatment guidelines and decisions. In fact, when integrating a CDSS into clinical practice engaging the practitioners or end-users is an effective strategy to prevent barriers prior to implementation [39•]. Utilizing a shared-decision model in which patients engage in the assessment and reporting of their depressive symptoms in primary care settings through a CDSS provides an opportunity for patient self-management and for guiding the dialogue for treatment visits, and it can improve adherence to evidence-based treatment guidelines and MBC at the point of care.

Notes

Disclosure

Dr Kurian has received research funding from National Institute of Mental Health (NIMH), Feinstein Institute for Medical Research, and Agency for Healthcare Research and Quality (AHRQ), and research support from Targacept, Pfizer, Johnson & Johnson, Evotec, Rexahn, Naurex, and Forest Pharmaceuticals.

Mr Grannemann reported no potential conflicts of interest relevant to this article.

Madhukar H. Trivedi is or has been an advisor/consultant to, or on the Speakers’ Bureaus for: Abbott Laboratories, Inc., Abdi Ibrahim, Akzo (Organon Pharmaceuticals Inc.), Alkermes, AstraZeneca, Axon Advisors, Bristol-Myers Squibb Company, Cephalon, Inc., Eli Lilly & Company, Evotec, Fabre Kramer Pharmaceuticals, Inc., Forest Pharmaceuticals, GlaxoSmithKline, Janssen Pharmaceutica Products, LP, Johnson & Johnson PRD, Libby, Lundbeck, Meade Johnson, MedAvante, Medtronic, Naurex, Neuronetics, Otsuka Pharmaceuticals, Pamlab, Parke-Davis Pharmaceuticals, Inc., Pfizer Inc., PgxHealth, Rexahn Pharmaceuticals, Sepracor, SHIRE Development, Sierra, SK Life and Science, Takeda, Tal Medical/Puretech Venture, Transcept, VantagePoint, and Wyeth-Ayerst Laboratories. In addition, he has received research support from: Agency for Healthcare Research and Quality (AHRQ), Corcept Therapeutics, Inc., Cyberonics, Inc., Merck, National Alliance for Research in Schizophrenia and Depression, National Institute of Mental Health, National Institute on Drug Abuse, Novartis, Pharmacia & Upjohn, Predix Pharmaceuticals (Epix), Solvay Pharmaceuticals, Inc., Targacept, and Valient.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Benji T. Kurian
    • 1
  • Bruce Grannemann
    • 1
  • Madhukar H. Trivedi
    • 1
  1. 1.Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasUSA

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