Algorithms and Collaborative-care Systems for Depression: Are They Effective and Why?: A Systematic Review

doi:10.1016/j.biopsych.2006.05.010

Biological Psychiatry

Volume 59, Issue 11, 1 June 2006, Pages 1029-1038

https://doi.org/10.1016/j.biopsych.2006.05.010 Get rights and content

Background

: Treatment algorithms and collaborative-care systems are systematic treatment approaches that are designed to improve outcomes by enhancing the quality of care. During the last decade, algorithm research has evolved as a new branch of clinical research that evaluates the clinical and economic impact of algorithm-guided treatment in primary and psychiatric care of patients with depressive disorders.

Methods

: This article discusses the rationale of algorithm development, their risks and limitations, and important elements in their implementation in clinical practice. It further reviews the available studies that have evaluated algorithm-guided treatment for depression.

Results

: Recent studies show that compared with treatment as usual, the use of algorithms and collaborative-care approaches in the care of depressed patients enhances treatment outcomes by modifying practice procedures and treatment processes.

Conclusions

: Treatment algorithms and collaborative-care systems clearly increase the efficacy of applied treatments in the care of depressed patients. However, to what extent the enhanced outcomes are a result of diligent measurement-based care or of the specific treatment steps that are used remains to be resolved. Valid clinical or pharmacogenetic predictors of response are needed to further tailor specific algorithms to individual patients.

Section snippets

Definition of Treatment Algorithms

Treatment algorithms provide three types of guidance: (1) strategies (what treatments to use), (2) tactics (how to implement the treatments), and (3) treatment steps (in what order to implement the different treatments). Typically, algorithms recommend a predefined goal (e.g., remission or response), as well as clinical instruments by which to adjust and assess the results of each treatment (e.g., symptom and side-effect rating scales). They also define critical decision points (CDPs) in the

Issues in Developing Algorithms

The development of algorithms and treatment guidelines usually follows a series of three steps:

Identification and Synthesis of the Evidence

Various methods to develop algorithms and guidelines are available (Gilbert et al 1998). These methods vary according to the selection of the topic, who develops the algorithm, how the quality of available scientific literature is assessed and rated, how gaps in the scientific evidence are filled in the absence of scientific data, how and whether clinical judgment is used, and how trade-offs between costs and clinical efficacy are considered (Woolf 1990, Woolf 1991). Depending on these factors,

Defining Certainty and Applicability of the Evidence

Although the certainty of evidence usually is provided in a guideline or algorithm, the treating physician must evaluate its applicability to the individual patient. Guideline or algorithm recommendations will be suitable for some patients but inappropriate for others. For example, subjects in clinical trials often are required to be free of common concomitant axis I or II conditions, including substance abuse, and to not be treatment resistant. Hence, scientific certainty of the findings from

Specifying the Underlying Assumptions and Tensions

The underlying assumptions that guide the development of the algorithm must be specified (e.g., reduce medication costs, reduce disease management costs, increase remission rates without regard to costs, minimize side-effect burden, etc.). In developing algorithms, one encounters specific tensions or trade-offs that affect the final content of the algorithm (e.g., are off-label treatments recommended, should evidence based on clinical consensus be excluded, etc.).

Issues in Implementing Algorithms

Algorithm users must fully understand the basis for the recommendations and be willing to adapt treatment strategies and clinical decision procedures to individual patients without losing their medical authority.

There are several obstacles to algorithm implementation (Cabana et al 2002). They include administrative hurdles such as procedural barriers that impact clinic visit frequency, clinic visit length, medication choices, access to specific medications, psychotherapies, or somatic

Measuring Symptoms and Side Effects at Critical Decision Points

An essential element of treatment algorithms are predefined CDPs. These usually are scheduled at the end of a particular treatment step to assess its effectiveness. To guide decision making at these CDPs, symptom outcomes are measured by a depression rating scale, and response criteria should be predefined that recommend dose increases, decreases, or no change as well as recommending a switch or augmentation of the current treatment (see Figure 1 for an example). Parallel to symptom

Defining the Best Tactics

Visit frequency determines for how long a single treatment is maintained and how rapidly the treatment plan can be revised. Too-frequent CDPs bear the risk of not allowing a specific treatment to show efficacy, particularly in slower-responding treatment-resistant patients or geriatric patients. However, CDPs that are scheduled too infrequently risk leaving patients on an ineffective treatment for too long. Acute treatment generally is associated with a higher visit frequency than are

Defining the Next Best Treatment Steps

Because of the lack of trials comparing next-step treatments in nonresponders with antidepressant treatment, algorithms that suggest different second or third treatment steps rest largely on clinical consensus or on open uncontrolled trials (Crismon et al 1999, Rosenbaum et al 2001, Rush and Ryan 2002, Thase and Kupfer 1995). Therefore, controlled trials are needed on which next treatment step to recommend after insufficient response to an initial antidepressant (Rosenbaum et al 2001). For

Studies on Algorithm-guided Treatment of Depression

The reviewed studies in this section are summarized in Table 1.

Collaborative-Care Trials

Katon et al (1995) reported on the first study to compare guideline-based, multidimensional care with TAU for outpatients with nonpsychotic major depression (n = 91) or minor depression (n = 126) in a primary-care setting. The study used the treatment guidelines for nonpsychotic major depression (Depression Guideline Panel 1993) that were developed by the Agency for Health Care Policy and Research (AHCPR). This so-called collaborative-care model included the provision of staff to assess

Improving Mood-Promoting Access to Collaborative Treatment Trial

Improving Mood-Promoting Access to Collaborative Treatment (IMPACT) compared a collaborative-care management program (n = 906) including a multistep treatment medication algorithm in primary care settings for elderly outpatients with MDD with TAU (n = 895; Unützer et al 2002). This study provided depression care specialists (DCSs) who screened and assessed patients by using the Patient Health Questionnaire (PHQ; Kroenke et al 2001) at clinic visits. A prespecified three-step medication

Prevention of Suicide in Primary Care Elderly-Collaborative Trial

The Prevention of Suicide in Primary Care Elderly-Collaborative Trial (PROSPECT) evaluated the benefits of an intensive managed-care program in the treatment of a geriatric sample with major and minor depression (n = 598; Mulsant et al 2001). A DCS provided a patient and family psychoeducation program, assessed somatic and psychiatric comorbidities and side effects, monitored patients’ compliance, and evaluated treatment response over 12 months. The treatment provided was based on current

Depression and Diabetes Trials

A recent multicenter primary care study (the Pathways Study) aimed at determining whether a diligent treatment of depression with enhanced quality of care in patients with comorbid diabetes mellitus improved depression and diabetes control (Katon et al 2004). Overall, 329 patients with major depression or dysthymia and comorbid diabetes mellitus were randomly assigned to a systematic case management intervention for depression for up to 12 months, which included an enhanced patient education

Sequenced Treatment Alternatives to Relieve Depression

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (http://www.star-d.org) aims at defining prospectively which of several treatments are most effective for outpatients with nonpsychotic MDD with an unsatisfactory clinical benefit from an initial and, if necessary, subsequent treatments (Fava et al 2003, Rush et al 2004). This recently completed multisite trial, sponsored by the National Institute of Mental Health, included both primary- and psychiatric-care settings

Discussion

It has been shown in small open and controlled studies, as well as in the recent large-scale multicenter studies, that algorithm-guided treatment of depression optimizes treatment outcomes and treatment attrition. Systematic treatment approaches within collaborative-care programs increase remission rates and treatment outcomes during the maintenance treatment phase. Recent studies on collaborative-care approaches for geriatric patients with depression indicate a benefit particularly in this

Future Directions

This review indicates that algorithm-guided care for depression, which includes enhanced and diligent care, is associated with more frequent, more sustained, or faster symptom improvements, as well as with higher satisfaction with treatment and better quality of care. Two major steps are worth pursuing: (1) further improving the delivery of currently available treatments and (2) developing newer treatments, along with evidence as to when and for whom they are indicated.

Future steps in algorithm

References (78)

M.D. Cabana et al.
Implementing practice guidelines for depressionApplying a new framework to an old problem
Gen Hosp Psychiatry
(2002)
E. Ezquiaga et al.
Psychosocial predictors of outcome in major depressionA prospective 12-month study
J Affect Disord
(1999)
M. Fava et al.
Background and rationale for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study
Psychiatr Clin North Am
(2003)
M. Fava et al.
Major depressive subtypes and treatment response
Biol Psychiatry
(1997)
A.J. Flint et al.
The effect of sequential antidepressant treatment on geriatric depression
J Affect Disord
(1996)
M.S. George et al.
A one-year comparison of vagus nerve stimulation with treatment as usual for treatment-resistant depression
Biol Psychiatry
(2005)
C.J. Hawley et al.
Protocol for the pharmacologic treatment of major depression. A field test of a potential prototype
J Affect Disord
(1998)
P.W. Lavori et al.
Strengthening clinical effectiveness trialsEquipoise-stratified randomization
Biol Psychiatry
(2001)
A.J. Rush et al.
Sequenced Treatment Alternatives to Relieve Depression (STAR*D)Rationale and design
Control Clin Trials
(2004)
A.J. Rush et al.
Vagus nerve stimulation for treatment-resistant depressionA randomized, controlled acute phase trial
Biol Psychiatry
(2005)

A.J. Rush et al.

Effects of 12 months of vagus nerve stimulation in treatment-resistant depressionA naturalistic study

Biol Psychiatry

(2005)

A.J. Rush et al.

The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR)A psychometric evaluation in patients with chronic major depression

Biol Psychiatry

(2003)

M. Adli et al.

Is dose escalation of antidepressants a rational strategy after a medium-dose treatment has failed? A systematic review

Eur Arch Psychiatry Clin Neurosci

(2005)

M. Adli et al.

Effectiveness and feasibility of a standardized stepwise drug treatment regimen algorithm for inpatients with depressive disordersResults of a 2-year observational algorithm study

J Clin Psychiatry

(2002)

M. Adli et al.

Evaluation of a Systematic Treatment Algorithm for Depressive DisordersHow Effective Are the Different Treatment Steps?

(2001)

M. Adli et al.

Algorithms for optimizing the treatment of depressionMaking the right decision at the right time

Pharmacopsychiatry

(2003)

G.S. Alexopoulos et al.

Remission in depressed geriatric primary care patientsA report from the PROSPECT study

Am J Psychiatry

(2005)

Practice guideline for the treatment of patients with major depressive disorder (revision)

Am J Psychiatry

(2000)

E. Badamgarav et al.

Effectiveness of disease management programs in depressionA systematic review

Am J Psychiatry

(2003)

M. Bauer et al.

World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders, part 2Maintenance treatment of major depressive disorder and treatment of chronic depressive disorders and subthreshold depressions

World J Biol Psychiatry

(2002)

M. Bauer et al.

World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders, part 1Acute and continuation treatment of major depressive disorder

World J Biol Psychiatry

(2002)

P. Bech et al.

The melancholia scaleDevelopment, consistency, validity and utility

M.L. Bruce et al.

Reducing suicidal ideation and depressive symptoms in depressed older primary care patients

JAMA

(2004)

M.L. Crismon et al.

The Texas Medication Algorithm ProjectReport of the Texas Consensus Conference Panel on Medication Treatment of Major Depressive Disorder

J Clin Psychiatry

(1999)

Depression in Primary Care

(1993)

L.R. Derogatis et al.

SCL-90An outpatient psychiatric rating scale-preliminary report

Psychopharmacol Bull

(1973)

L.R. Derogatis et al.

Conformation of the dimensional structure of the SCL-90A study in construct validity

J Clin Psychol

(1977)

G. Ellrodt et al.

Evidence-based disease management

JAMA

(1997)

M. Fava et al.

Current status of augmentation and combination treatments for major depressive disorderA literature review and a proposal for a novel approach to improve practice

Psychother Psychosom

(2006)

E. Frank et al.

Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence

Arch Gen Psychiatry

(1991)

S.J. Fredman et al.

Partial response, nonresponse, and relapse with selective serotonin reuptake inhibitors in major depressionA survey of current “next-step” practices

J Clin Psychiatry

(2000)

D. Gilbert et al.

Texas Medication Algorithm ProjectDefinitions, rationale, and methods to develop medication algorithms

J Clin Psychiatry

(1998)

J.F. Greden

The burden of disease for treatment-resistant depression

J Clin Psychiatry62

(2001)

D.V. Iosifescu et al.

The impact of medical comorbidity on acute treatment in major depressive disorder

Am J Psychiatry

(2003)

W. Katon et al.

Long-term effects of a collaborative care intervention in persistently depressed primary care patients

J Gen Intern Med 17

(2002)

W.J. Katon et al.

The Pathways StudyA randomized trial of collaborative care in patients with diabetes and depression

Arch Gen Psychiatry

(2004)

W. Katon et al.

Stepped collaborative care for primary care patients with persistent symptoms of depression

Arch Gen Psychiatry

(1999)

W. Katon et al.

Collaborative management to achieve treatment guidelines. Impact on depression in primary care

JAMA

(1995)

G.I. Keitner et al.

12-month outcome of patients with major depression and comorbid psychiatric or medical illness (compound depression)

Am J Psychiatry

(1991)

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Evaluating the efficacy and moderators of algorithm-guided antidepressant treatments of major depressive disorder
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Citation Excerpt :
Algorithm guided treatment strategies (AGTs) have been proposed to improve treatment outcomes and to enhance the quality of care (Adli et al., 2006, 2017; Yoshino et al., 2009). The essential elements of AGTs are strategies (i.e., what treatments to use and how long treatments to implement), tactics (i.e., how to implement the treatments), and standardized clinical assessments at critical decision points (Adli et al., 2006). Specifically, timely dose, duration of treatment, and treatment type adjustments are strictly relying on standardized measurement of clinical practices (i.e., symptom measurement and evaluation of side effects) at each critical decision point.
In spite of numerous options, the most efficacious treatment for major depressive disorder (MDD) remains elusive. Algorithm-guided treatments (AGTs) are proposed to address inadequate remission and optimize treatment delivery. This study aimed to evaluate the clinical benefit of AGTs for MDD, and to explore specific moderators of treatment outcomes for individual patients.
The study recruited 987 patients with MDD across eight hospitals who were randomly assigned to AGT with escitalopram (AGT-E), AGT with mirtazapine (AGT-M), or treatment-as-usual (TAU). The outcomes were symptom remission, response rate, early improvement rate, subsymptom clusters improvement over time, the mean time to first remission, relapse rate at 6-months posttreatment follow-up, quality of life (QOL), and adverse events.
No significant differences were observed across groups in outcome, except that TAU showed significantly poorer QOL, higher relapse rates at 6-months posttreatment follow-up, and marginally significantly worse maximal burden of adverse events than the AGT groups. After 6 weeks of treatment initiation, remission rate did not significantly increase with extended treatment. AGT-M outperformed the TAU and AGT-E in treating sleep symptoms. AGT-E was less effective than AGT-M and TAU in patients with severe depression and somatic symptoms (DSSS). The superiority of TAU over AGTs was observed in recurrent MDD patients.
Although the superiority of AGTs over TAU was limited by failure of alternative subsequent treatment, AGTs outperformed in QOL and relapse rate. Types of disease episode and DSSS were regarded as specific moderators in treatment of depression. These findings might contribute to future research on targeted antidepressant treatment.
Treatment algorithms for treatment-resistant depression
2022, Managing Treatment-Resistant Depression: Road to Novel Therapeutics
Treatment algorithms provide discrete steps to aid in clinical decision making and allow clinicians to consistently and easily practice evidence-based medicine. In psychiatry, treatment algorithms are expected to improve patient outcomes by decreasing prescribing variance, increasing appropriateness of medications and dosages, and preventing “pseudoresistance.” Since the 1990s, several treatment algorithms for depression have been published. While these algorithms show promise, the heterogeneity of depression makes treatment algorithms especially difficult to develop. To develop an effective algorithm, a multidisciplinary development group should be employed, recommendations should be based on randomized-controlled trials and/or metaanalyses, and potential barriers to implementation should be addressed. Importantly, treatment algorithms should not replace clinical expertise but instead aid in clinical decision making, integrated within a multifaceted treatment program. In the future, algorithms may be influenced by precision psychiatry, with clinical, neuroimaging/EEG, and pharmacogenomic input.
The clinical effectiveness of an algorithm-guided treatment program for depression in specialized mental healthcare: A comparison with efficacy trials
2020, Journal of Affective Disorders
Citation Excerpt :
In clinical practice, clinicians might deviate from practice guidelines, resulting in substantial treatment variance. Clinicians’ adherence to treatment guidelines and algorithm-guided treatment (AGT) of depression have been associated with improved treatment outcomes and better quality of care among patients with depression (Adli et al., 2006; Bauer et al., 2009; Guo et al., 2015; Hepner et al., 2007; Melfi et al., 1998; Schneider et al., 2005). Third, in clinical practice the number of treatment sessions completed might vary, unlike in RCTs.
Doubts exist on whether effects found in randomized controlled trials (RCTs) are directly generalizable to daily clinical practice. This study aimed (a) to investigate the effectiveness of treatment options within an algorithm-guided treatment (AGT) program for depression and compare their effectiveness with outcomes of efficacy trials and (b) to assess the relation between treatment continuity and outcomes.
This naturalistic study linked treatment data from January 2012 to November 2014 from a Dutch mental healthcare provider, to routine outcome monitoring (ROM) data (N = 351). Effectiveness of the treatment options (pharmacotherapy, psychotherapy and their combination) was compared to the efficacy reported in the meta-analyses. We included treatment continuity as binary variable “early terminators versus completers of the recommended number of treatment sessions”.
Remission rates for psychotherapy (38% [95% CI: 32–45]), pharmacotherapy (31% [95% CI: 22–42]) and combination therapy (46% [95% CI: 19–75]) were respectively lower, comparable, and comparable to those reported in the meta-analyses. Similarly, response rates were respectively lower (24% [95% CI: 19–30]), lower (21% [95% CI: 13–31]), and comparable (46% [95% CI: 19–75]) to meta-analyses results. A similar share of early terminators and completers achieved remission and response.
A substantial proportion of patients had incomplete ROM data after data linkage. Limited set of patient characteristics to check for selection bias.
Despite the more heterogeneous patient population in clinical practice, the effectiveness of an AGT program, emphasizing strict guideline adherence, approached that found in RCTs. A fixed number of treatment sessions may not suit all individual patients.
The utility of PHQ-9 and CGI-S in measurement-based care for predicting suicidal ideation and behaviors
2020, Journal of Affective Disorders
Measurement-based care (MBC) has emerged as an effective strategy for improving outcomes in patients with mood disorders. Suicide is a particularly devastating outcome of mood disorders. Using data from a new MBC program from the National Network of Depression Centers (NNDC), we examined whether capturing a patient-rated symptom measure, the patient health questionnaire (PHQ-9), along with a provider-rated global functioning measure, the clinical global impression scale (CGI-S), improves identification of patients at risk of suicide over using either measure alone.
A total of 126 adults with mood disorders from nine sites in the NNDC completed the PHQ-9 and CGI-S and had at least one subsequent visit where they completed the Columbia-suicide severity rating scale (C-SSRS). The PHQ-9 (≥10) and CGI-S (≥4) were dichotomized at commonly accepted severity thresholds. Associations of the PHQ-9 and CGI-S with suicidal ideation or behavior were examined using Firth's logistic regression to accommodate small samples while controlling for age, sex, race, and diagnosis.
Patients who scored higher on only the PHQ-9 or CGI-S were not significantly more likely to experience subsequent suicidal ideation or behaviors. However, patients who scored higher on both the PHQ-9 and CGI-S were significantly more likely to experience suicidal ideation (OR = 4.70, p = 0.0005) and suicidal behaviors (OR = 25.38, p = 0.0003).
Information from both patient and clinician-rated measures was better able to identify patients with mood disorders at risk for suicidal ideation and behaviors. Using both together may help reduce the risk of suicide by identifying those patients at greatest risk and allowing for more targeted interventions.
Outcome of a three-phase treatment algorithm for inpatients with melancholic depression
2018, Progress in Neuro-Psychopharmacology and Biological Psychiatry
In patients suffering from major depressive disorder, non-response to initial antidepressant monotherapy is relatively common. The use of treatment algorithms may optimize and enhance treatment outcome.
A single-center 3-phase treatment algorithm was evaluated for inpatients with major depressive disorder, i.e. phase I (n = 85): 7 weeks optimal antidepressant monotherapy (imipramine or venlafaxine); phase II (n = 39): 4 weeks subsequent plasma level-targeted dose lithium addition in case of insufficient improvement of antidepressant monotherapy; and phase III (n = 8): subsequent electroconvulsive therapy in case of insufficient improvement of antidepressant‑lithium treatment. Overall feasibility of the 3-phase algorithm was determined by the number of dropouts, and overall efficacy was evaluated using weekly scores on the 17-item Hamilton Rating Scale for Depression (HAM-D) during the treatment phases of the algorithm. This paper is based on an RCT comparing the two antidepressants in phase I and adding lithium in phase II.
Of the 85 patients analyzed, overall dropout during the 3-phase treatment algorithm was 24 (28%) patients. When analyzing the 3-phase treatment algorithm on a modified intention-to-treat basis, 39 (46%) patients achieved complete remission (HAM-D score ≤ 7) by the end of the algorithm. Regarding response (HAM-D score reduction ≥50%): of the 85 patients, 60 (71%) were responders by the end of the algorithm.
The favorable outcome of the 3-phase treatment algorithm emphasizes the importance of pursuing stepwise antidepressant treatment in patients who are nonresponsive to the first antidepressant.
This study protocol is registered at http://www.controlled-trials.com, “Pharmacological Treatment of Depression” (identifier: ISRCTN73221288).
A standardized stepwise drug treatment algorithm for depression reduces direct treatment costs in depressed inpatients ‐ Results from the German Algorithm Project (GAP3)
2018, Journal of Affective Disorders
In a previous single center study we found that a standardized drug treatment algorithm (ALGO) was more cost effective than treatment as usual (TAU) for inpatients with major depression. This report aimed to determine whether this promising initial finding could be replicated in a multicenter study.
Treatment costs were calculated for two time periods: the study period (from enrolment to exit from study) and time in hospital (from enrolment to hospital discharge) based on daily hospital charges. Cost per remitted patient during the study period was considered as primary outcome.
266 patients received ALGO and 84 received TAU. For the study period, ALGO costs were significantly lower than TAU (ALGO: 7 848 ± 6 065 €; TAU: 10 033 ± 7 696 €; p = 0.04). For time in hospital, costs were not different (ALGO: 14 734 ± 8 329 €; TAU: 14 244 ± 8 419 €; p = 0.617). Remission rates did not differ for the study period (ALGO: 57.9%, TAU: 50.0%; p=0.201). Remission rates were greater in ALGO (83.3%) than TAU (66.2%) for time in hospital (p = 0.002). Cost per remission was lower in ALGO (13 554 ± 10 476 €) than TAU (20 066 ± 15 391 €) for the study period (p < 0.001) and for time in hospital (ALGO: 17 582 ± 9 939 €; TAU: 21 516 ± 12 718 €; p = 0.036).
Indirect costs were not assessed. Different dropout rates in TAU and ALGO complicated interpretation.
Treatment algorithms enhance the cost effectiveness of the care of depressed inpatients, which replicates our prior results in an independent sample.

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ReviewAlgorithms and Collaborative-care Systems for Depression: Are They Effective and Why?: A Systematic Review

Background

Methods

Results

Conclusions

Section snippets

Definition of Treatment Algorithms

Issues in Developing Algorithms

Identification and Synthesis of the Evidence

Defining Certainty and Applicability of the Evidence

Specifying the Underlying Assumptions and Tensions

Issues in Implementing Algorithms

Measuring Symptoms and Side Effects at Critical Decision Points

Defining the Best Tactics

Defining the Next Best Treatment Steps

Studies on Algorithm-guided Treatment of Depression

Collaborative-Care Trials

Improving Mood-Promoting Access to Collaborative Treatment Trial

Prevention of Suicide in Primary Care Elderly-Collaborative Trial

Depression and Diabetes Trials

Sequenced Treatment Alternatives to Relieve Depression

Discussion

Future Directions

Gen Hosp Psychiatry

J Affect Disord

Psychiatr Clin North Am

Biol Psychiatry

J Affect Disord

Biol Psychiatry

J Affect Disord

Biol Psychiatry

Control Clin Trials

Biol Psychiatry

Biol Psychiatry

Biol Psychiatry

Is dose escalation of antidepressants a rational strategy after a medium-dose treatment has failed? A systematic review

Eur Arch Psychiatry Clin Neurosci

Effectiveness and feasibility of a standardized stepwise drug treatment regimen algorithm for inpatients with depressive disordersResults of a 2-year observational algorithm study

J Clin Psychiatry

Evaluation of a Systematic Treatment Algorithm for Depressive DisordersHow Effective Are the Different Treatment Steps?

Algorithms for optimizing the treatment of depressionMaking the right decision at the right time

Pharmacopsychiatry

Remission in depressed geriatric primary care patientsA report from the PROSPECT study

Am J Psychiatry

Practice guideline for the treatment of patients with major depressive disorder (revision)

Am J Psychiatry

Effectiveness of disease management programs in depressionA systematic review

Am J Psychiatry

World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders, part 2Maintenance treatment of major depressive disorder and treatment of chronic depressive disorders and subthreshold depressions

World J Biol Psychiatry

World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders, part 1Acute and continuation treatment of major depressive disorder

World J Biol Psychiatry

The melancholia scaleDevelopment, consistency, validity and utility

Reducing suicidal ideation and depressive symptoms in depressed older primary care patients

JAMA

The Texas Medication Algorithm ProjectReport of the Texas Consensus Conference Panel on Medication Treatment of Major Depressive Disorder

J Clin Psychiatry

Depression in Primary Care

SCL-90An outpatient psychiatric rating scale-preliminary report

Psychopharmacol Bull

Conformation of the dimensional structure of the SCL-90A study in construct validity

J Clin Psychol

Evidence-based disease management

JAMA

Current status of augmentation and combination treatments for major depressive disorderA literature review and a proposal for a novel approach to improve practice

Psychother Psychosom

Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence

Arch Gen Psychiatry

Partial response, nonresponse, and relapse with selective serotonin reuptake inhibitors in major depressionA survey of current “next-step” practices

J Clin Psychiatry

Texas Medication Algorithm ProjectDefinitions, rationale, and methods to develop medication algorithms

J Clin Psychiatry

The burden of disease for treatment-resistant depression

J Clin Psychiatry62

The impact of medical comorbidity on acute treatment in major depressive disorder

Am J Psychiatry

Long-term effects of a collaborative care intervention in persistently depressed primary care patients

J Gen Intern Med 17

The Pathways StudyA randomized trial of collaborative care in patients with diabetes and depression

Arch Gen Psychiatry

Review
Algorithms and Collaborative-care Systems for Depression: Are They Effective and Why?: A Systematic Review