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Original Article |

Clinical Results for Patients With Major Depressive Disorder in theTexas Medication Algorithm Project FREE

Madhukar H. Trivedi, MD; A. John Rush, MD; M. Lynn Crismon, PharmD; T. Michael Kashner, PhD, JD, MPH; Marcia G. Toprac, PhD; Thomas J. Carmody, PhD; Tracie Key, BSN, RN; Melanie M. Biggs, PhD; Kathy Shores-Wilson, PhD; Bradley Witte, BA; Trisha Suppes, MD, PhD; Alexander L. Miller, MD; Kenneth Z. Altshuler, MD; Steven P. Shon, MD
[+] Author Affiliations

From the Departments of Psychiatry (Drs Trivedi, Rush, Kashner, Biggs,Shores-Wilson, Suppes, and Altshuler, Ms Key, and Mr Witte) and Academic Computing(Dr Carmody), University of Texas Southwestern Medical Center, Dallas; Collegeof Pharmacy, The University of Texas at Austin (Dr Crismon); Health ServicesResearch and Development Service Research Career Scientist Program, Departmentof Veterans Affairs, Dallas (Dr Kashner); Texas Department of Mental Healthand Mental Retardation, Austin (Drs Toprac and Shon); and Department of Psychiatry,University of Texas Health Science Center at San Antonio (Dr Miller). Dr Trivediis a grantee and/or speaker for Abbott Laboratories, Organon Inc (Akzo), Bayer,Bristol-Meyers Squibb Company, Eli Lilly and Company, GlaxoSmithKline, JanssenPharmaceutica Products, Johnson & Johnson, National Institutes of MentalHealth, Mead Johnson and Company, Parke-Davis, Pfizer Inc, Solvay, Wyeth,NARSAD, and Forest Laboratories Inc. Dr Rush is a grantee, consultant, and/orspeaker for the Robert Wood Johnson Foundation, National Institutes of MentalHealth, and Stanley Medical Research Institute, Bristol-Myers Squibb Company,Cyberonics Inc, Eli Lilly and Company, Forest Laboratories Inc, GlaxoSmithKline,Organon Inc (Akzo), and Wyeth. Dr Crismon is a grantee, consultant or advisor,and/or speaker for AstraZeneca, Bristol-Meyers Squibb Company, Eli Lilly andCompany, Forest Laboratories Inc, and Janssen Pharmaceutica Products, PharmaciaPharmaceuticals, McNeil Specialty and Consumer Products, and Pfizer Inc. DrSuppes is a grantee, consultant, and/or speaker or advisor for Abbott Laboraotires,AstraZeneca, Bristol-Meyers Squibb Company, GlaxoSmithKline, Janssen PharmaceuticaProducts, National Institutes of Mental Health, Novartis, Robert Wood JohnsonPharmaceutical Research Institute, Stanley Medical Research Institute, Johnson& Johnson Pharmaceutical Research & Development, Pfizer Inc, PharmaceuticalResearch Institute, Ortho McNeil Pharmaceutical Inc, UCB Pharma, and Novartis.Dr Miller is a grantee, consultant, and/or speaker for AstraZeneca, AbbottLaboratories, Bristol-Myers Squibb Company, Eli Lilly and Company, JanssenPharmaceutica Products, and Pfizer Inc.


Arch Gen Psychiatry. 2004;61(7):669-680. doi:10.1001/archpsyc.61.7.669.
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Published online

Context  The Texas Medication Algorithm Project is an evaluation of an algorithm-based disease management program for the treatment of the self-declared persistently and seriously mentally ill in the public mental health sector.

Objective  To present clinical outcomes for patients with major depressive disorder (MDD) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU).

Design  Effectiveness, intent-to-treat, prospective trial comparing patient outcomes in clinics offering ALGO with matched clinics offering TAU.

Setting  Four ALGO clinics, 6 TAU clinics, and 4 clinics that offer TAU to patients with MDD but provide ALGO for schizophrenia or bipolar disorder.

Patients  Male and female outpatients with a clinical diagnosis of MDD (psychotic or nonpsychotic) were divided into ALGO and TAU groups. The ALGO group included patients who required an antidepressant medication change or were starting antidepressant therapy. The TAU group initially met the same criteria, but because medication changes were made less frequently in the TAU group, patients were also recruited if their Brief Psychiatric Rating Scale total score was higher than the median for that clinic's routine quarterly evaluation of each patient.

Main Outcome Measures  Primary outcomes included (1) symptoms measured by the 30-item Inventory of Depressive Symptomatology–Clinician-Rated scale (IDS-C30) and (2) function measured by the Mental Health Summary score of the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) obtained every 3 months. A secondary outcome was the 30-item Inventory of Depressive Symptomatology–Self-Report scale (IDS-SR30).

Results  All patients improved during the study (P<.001), but ALGO patients had significantly greater symptom reduction on both the IDS-C30 and IDS-SR30 compared with TAU. ALGO was also associated with significantly greater improvement in the SF-12 mental health score (P = .046) than TAU.

Conclusion  The ALGO intervention package during 1 year was superior to TAU for patients with MDD based on clinician-rated and self-reported symptoms and overall mental functioning.

Figures in this Article

Major depressive disorder (MDD) is a prevalent, serious, debilitatingillness that affects 7% to 12% of men and 20% to 25% of women in their lifetime.1,2 The course of MDD is typically chronicor recurrent.3 From 10% to 30% of patientshave major depressive episodes that last loner than 2 years, and 20% to 30%have MDD superimposed on dysthymic disorder (double depression).46 Majordepressive disorder accounts for up to 60% of psychiatric hospitalizations,and 8% to 15% of these patients commit suicide.7,8 Furthermore,depression worsens the morbidity and mortality of several general medicalconditions (eg, cardiac heart disease,911 myocardialinfarction,1214 chronicpain,15 diabetes,16 andasthma).17,18 The direct monetarycost of treatment, combined with the indirect costs from lost productivity,are substantial1921 andhave been estimated to be between $44 and $53 billion per year.2225

Despite the high prevalence of MDD and the wide availability of effectivetreatments, undertreatment is common.8,26,27 Theaim of treatment is symptomatic remission and functional recovery28 with continuation treatment to prevent relapse.2932 Symptomaticimprovement (response) is distinguished from remission (ie, minimal or nosymptoms), because remission, in contrast to a response with residual symptoms,is associated with better functioning33,34 anda better prognosis.3539

Most randomized controlled efficacy trials typically have engaged symptomaticvolunteers with minimal concurrent psychiatric or general medical illnessesand minimal levels of treatment resistance. Consequently, findings from thesestudies may not generalize to self-declared patients seen in clinical psychiatricpractice. Moreover, few studies define how to treat those with an unsatisfactoryclinical response to the initial treatment or compare the benefits of differentmedication options given sequentially.40 Theseefficacy trials indicate that approximately 35% of participants achieve remissionin 6 to 8 weeks,29 although higher remissionrates are found in longer treatment trials.41,42 Inthe longer term, 10% to 30% of patients who do not respond or enter remissionquickly subsequently develop depressive relapses during the ensuing 4 to 6months despite continued pharmacotherapy.43 Becauseno one treatment is a panacea, clinicians often use a sequence of treatmentsteps (either monotherapies or combinations) to increase the likelihood ofresponse or remission. Recent efforts have aimed to define guidelines or algorithmsfor the application of pharmacotherapeutic options for MDD.4450 Decisiontree–based algorithms hold the promise of increased consistency of treatmentacross practitioners, which in turn should lead to better clinical outcomesand more efficient use of health care resources. Algorithm-guided treatmentprovides a basis for improving the quality of treatment in both the publicand private sectors.

To our knowledge, the present study is the first controlled trial toevaluate algorithm-based treatment of depression in a public sector populationtreated by psychiatrists. One open trial51 ofthe impact of algorithm-driven treatment on symptomatic outcomes in a psychiatric(inpatient) population showed effectiveness for an algorithm in the inpatientsetting but lacked a control condition.

A series of studies,5165 includingthose conducted by Katon et al,52,64 haveevaluated clinical outcomes following the use of Agency for Health Care Policyand Research–based, guideline-driven treatments (see the "Comment" section).Katon et al52 conducted a randomized controlledtrial of a guideline-driven intervention vs usual care in the treatment ofpatients with major (n = 91) or minor depression (n = 126) in a primary caresetting. For major but not for minor depression, the intervention was associatedwith greater adherence to adequate medication doses and more favorable ratingsof antidepressant medications benefit, as well as higher ratings of the qualityof care and better symptomatic outcomes. Most other trials conducted amongprimary care settings evaluated broadly defined guideline-driven principles(eg, Did the patient complete the acute-phase trial or not? Was the recommendedvisit frequency achieved?).

The Texas Medication Algorithm Project (TMAP) aimed to compare the clinicaland economic outcomes achieved with the use of prespecified medication algorithmscombined with clinical support and a prespecified patient and family educationalpackage for algorithm-guided treatment (ALGO) with treatment as usual (TAU).To increase the probability of appropriate algorithm implementation, an extensiveprovider support system with additional personnel funded by research moneyswas used.

STUDY DESIGN

This study is an effectiveness, intent-to-treat, prospective trial thatcompares patient outcomes in clinics offering ALGO with matched clinics offeringTAU. Clinics were prematched based on mental health and mental retardationauthority and urban status. Evaluable patients were postmatched based on symptoms(30-item Inventory of Depressive Symptomatology–Clinician-Rated scale[IDS-C30] and 30-item Inventory of Depressive Symptomatology–Self-Reportscale [IDS-SR30] scores) and length of illness (see Rush et al66 for a detailed review of the rationale and design).This multisite study evaluated the clinical benefits of ALGO provided in 4clinics compared with 6 clinics that offered TAU (TAUnonALGO) and an additional4 clinics that offered TAU to patients with MDD but also provided ALGO foreither schizophrenia or bipolar disorder (TAUinALGO). Physicians from all14 clinics had access to the same medications. The TAUinALGO clinics wereintended to assess the effect of an "algorithm culture" associated with theimplementation of any of these algorithms on treatment practices. If no differencesbetween TAU groups were found, they could be combined for comparison withALGO. Randomizing patients among physicians and clinics would require healthcare providers to ignore their algorithm training and consultation interventionswhen treating control patients. To randomize by health care providers withinthe same clinic risked the "water cooler" effects (ie, algorithm physicianswould talk to and affect the practice of TAU physicians). Furthermore, physiciansin a clinic typically cross-covered for each other, further limiting feasibility.

The primary aim was to assess whether ALGO produced better clinicaloutcomes in terms of either an earlier onset and/or a greater overall effectduring a 1-year treatment period. We hypothesized that ALGO would produce(1) a faster and more robust improvement in symptoms, (2) better functioning,and (3) a lower side effect burden than TAU.

The study was conducted in accordance with international guidelinesfor good clinical practice and the Declaration of Helsinki and approved bythe institutional review boards at The University of Texas Southwestern MedicalCenter at Dallas and The University of Texas at Austin. On study entry, symptoms,function, quality of life, side effect severity and burden, and health careservice utilization and treatment costs were evaluated at baseline and quarterlyfor at least a 12-month period for all available participants.

ALGORITHM INTERVENTION

ALGO included 2 consensus-driven, medication management algorithms (oneeach for psychotic and nonpsychotic forms of MDD)48 andexpert consultation (offered on biweekly teleconference) and on-site clinicalsupport from clinical coordinators and a patient and family education programprovided by the clinical coordinators.67 Thisintervention package was intended to optimize pharmacotherapy, thereby enhancingclinical outcomes. Each physician implemented ALGO in close collaborationwith a clinical coordinator. A 7-step medication algorithm for nonpsychoticMDD and a 5-step algorithm for psychotic MDD were provided (Figure 1 and Figure 2).Most steps in each algorithm included multiple treatment options, with earliersteps including those treatment options with the most evidence and the bestrisk-benefit ratios.

Place holder to copy figure label and caption
Figure 1.

Strategies for the treatment ofnonpsychotic major depressive disorder. Asterisk indicates consider TCA/VLFif not tried; dagger, lithium, thyroid, buspirone; double dagger, skip iflithium augmentation has already failed; section mark, most studied combination.BUPSR indicates bupropion sustained release; cital, citalopram;fluox, fluoxetine; MAOI, monoamine oxidase inhibitor; MRT, mirtazapine; SSRI,selective serotonin reuptake inhibitor; TCA, tricyclic antidepressants; VLFXR, venlafaxine extended release. This figure is published with permissionfrom the Texas Department of Mental Health and Mental Retardation and is partof a state-funded project.

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Place holder to copy figure label and caption
Figure 2.

Strategies for the treatment ofpsychotic major depressive disorder. ECT indicates electroconvulsive therapy;SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant;VLF, venlafaxine. This figure is published with permission from the TexasDepartment of Mental Health and Mental Retardation and is part of a state-fundedproject.

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Multiple tools were used to enhance adherence to the algorithm. A detailedtreatment manual was used for initial didactic training and ongoing consultationswith clinicians (available at: http://www.mhmr.state.tx.us/centraloffice/medicaldirector/timamddman.pdf).68 The manual identified critical decisionpoints (eg, weeks 4, 6, 8, 10, and 12) for each medication when revisionsin treatment strategies or tactics were to be undertaken based on degree ofsymptom change and side effect burden (Figure1 and Figure 2).

Symptom severity and side effect burden were routinely monitored ateach treatment visit to guide treatment implementation, with the aim of ensuringan adequate duration and dose of medication. Clinical assessments at eachvisit included a global assessment of symptoms and associated symptoms, IDS-C30 and IDS-SR30, and side effect burden by a 10-point globalscale. A standard clinical record form was completed at each clinic visitby those implementing the ALGO intervention. The symptom severity assessmentswere conducted by clinical coordinators before the physician visits.

Each ALGO patient also received a stepwise education package that providedinformation about the disease, prognosis, treatment options, and medicationside effects. This package encouraged patients to participate in treatmentdecisions and adhere to the treatment.67

PATIENT SELECTION

Male and female outpatients 18 years or older with a clinical diagnosisof MDD (psychotic or nonpsychotic) were eligible for the study. Patients enteredALGO if their treating physician judged that they required an antidepressantmedication change or were starting antidepressant therapy. Entrance into TAUinitially used the same criteria. However, because medication changes weremade less frequently in TAU, patients were also recruited if their quarterly,routinely administered 24-item Brief Psychiatric Rating Scale (BPRS-24)6971 total score was higherthan the median for that clinic's routine quarterly evaluation of each patient.Once approached, another BPRS-24 interview was conducted. Patients with BPRS-24total scores no more than 1 SD below enrolled ALGO patient average scoreswere asked to participate. This procedure ensured a minimal level of symptomseverity for participation in TAU in the absence of a medication change. Thus,in both ALGO and TAU clinics, a combination of procedure-cued and clinician-cuedmethods was used.

Exclusion criteria were minimal. Patients were excluded if they hadschizophrenic, bipolar, or schizoaffective disorder or a primary diagnosisof an obsessive-compulsive or eating disorder (anorexia nervosa or bulimianervosa). Also excluded were patients who required inpatient hospitalizationfor detoxification at the time of study entry, received mental retardationservices, or participated in an Assertive Community Treatment program.72Table 1 givesthe ethnic composition and characteristics of the participating sites andclinics.

Table Graphic Jump LocationTable 1. Ethnic Composition and Characteristics of Participating Sitesand Clinics
STUDY PROCEDURES

Study participants provided demographic and medical history at baselineand during outcome assessments every 3 months for at least 12 months. Enrollmentfor the study occurred throughout 13 months. Research coordinators not blindto treatment assignment but not involved in providing any treatment conductedthe research outcome assessments.

RESEARCH ASSESSMENTS

The clinical rating of depressive symptoms by the research coordinatorswith the IDS-C3073 was the primaryoutcome. Confirmatory symptom measures included the IDS-SR3073 and the BPRS-24.6971 Health-relatedquality of life was assessed using the Medical Outcomes Study 12-item Short-FormHealth Survey (SF-12).74 Participants wereasked about the burden of side effects from medication during the past monththat "bothered or interfered with daily functioning." Respondents were consideredto have no significant side effects if they reported "no side effects" or"only mild side effects, not really significant" and to have significant sideeffects whenever side effects " . . . bothered me, but could tolerate them"or " . . . really bothered me, I either need to change my medication or takesomething for the side effects" or " . . . was so severe I had to be hospitalized."

Demographic information was obtained from a patient questionnaire administeredduring the face-to-face baseline interview. Alcohol and other drug use wasassessed quarterly using the Drug Abuse Screening Test75 (scores>5 indicate drug abuse) and the Michigan Alcoholism Screening Test76 (scores ≥5 indicate alcohol abuse). The PatientPerception of Benefits (T.M.K., unpublished data, 2000) is a 10-item, self-reportinstrument developed for this study, with scores ranging from 0 (belief) to40 (disbelief) that indicate whether the patient will see improved functioningif he/she gets needed care.

STATISTICAL ANALYSES

Hierarchical linear models77 were adaptedto assess the impact of ALGO on clinical outcomes based on declining effectsanalyses developed for this study by Kashner et al.78,79 Decliningeffects models are growth curves with dependent outcome variables representedas change scores. Independent variables include dichotomous treatment, timesince follow-up began, time × treatment interaction terms, and a constantterm. This approach takes into account repeated observations nested withinpatients; missing observations; varying intervals between follow-up observations;effect sizes that vary with time; heteroscedastic, autocorrelated, and othercomplex level 1 covariance structures; and continuous, bivariate, or ordinalvalued outcome variables. Parameter estimates were computed using HLM version5 software.80

Estimates were computed separately to assess the impact of treatmenton ALGO and TAU patients. These estimates included an initial change in outcomesbetween baseline and the first 3 months and a growth rate in outcomes duringthe subsequent 9-month follow-up period. Growth rates were measured in termsof change in outcomes per quarter. To assess differences in the impact ofALGO vs TAU on outcomes, we measured differences in both initial changes andgrowth rates between ALGO and TAU patients. All estimates were adjusted toreflect baseline differences in starting values (change scores) and baselinecharacteristics (covariates) with respect to baseline need (IDS-C30,length of illness in years), enabling (family size, disposable income), predisposing(years of education, Patient Perception of Benefits total score), and otherfactors (African American and Hispanic status). Program effects were computedby taking differences between ALGO and TAU with respect to initial changes(initial effect) and growth rates (growth rate effect). The growth rate effectwas used to determine if any initial ALGO advantage (initial effect) realizedduring the first quarter increased, remained constant, or declined duringthe 9-month follow-up. Declining effects were expected if, following an initialALGO advantage, TAU patients began to catch up to their ALGO counterparts(see Rush et al66). To adjust for regressionto the mean due to baseline differences in reported side effect burden, patientswere divided for analyses between those reporting and not reporting significantside effects at baseline.

DEVELOPMENT OF THE ANALYTIC SAMPLE

A total of 634 patients met study entry criteria and signed informedconsent. Of these, 21 did not complete the baseline assessment, 62 failedto report for any postbaseline visit, and 4 had such a visit but did not completeat least 1 postbaseline primary outcome (IDS-C30). The remaining547 evaluable patients completed the primary symptom measure for at least1 postbaseline visit, including ALGO (n = 181), TAUinALGO (n = 212), and TAUnonALGO(n = 154).

Preliminary analysis revealed that patients attending TAUinALGO clinicswere achieving numerically but not statistically larger initial reductionsin symptoms following intake than their TAUnonALGO counterparts (IDS-C30 adjusted D = −1.89, SE = 1.62, t1080 = 1.17, P<.24). The larger reductionswere expected if clinics that participated in ALGO programs targeting otherdisorders also improved care for MDD patients. On the other hand, TAUinALGOpatients were found to have only slightly lower severity of baseline symptoms(mean IDS-C30 score = 36.0 ± 13.8) than their TAUnonALGOcounterparts (mean IDS-C30 score = 37.9 ± 13.2), althoughthe difference was not statistically significant (D = −1.9, t338 = 1.4, P = .18, with equalvariances not assumed).

Although change scores prevent factor loading baseline differences ontoestimates of effect sizes, issues of regression to the mean remain, leadingto upward biases of ALGO effect estimates. Because regression to the meanposed a more serious problem, we constructed a final analytic sample by matchingeach ALGO patient with the best match (without replacement) from either theTAUinALGO or TAUnonALGO groups with respect to baseline IDS-C30 score(≤2), IDS-SR30 score (≤10), and whenever possible, lengthof illness (≤20 years) independent of and blind to any outcomes. The approachis conservative, because including TAUinALGO patients would likely bias againstfinding an ALGO effect on reducing symptoms.

Comparing patients assigned to ALGO (n = 182) vs TAUnonALGO (n = 154),the unadjusted estimate for the ALGO initial effect is −5.58 ±1.45 for IDS-C30 (t1245 = 3.85, P<.001) or −3.25 ± 1.49 for IDS-C30 (t1236 = 2.19, P = .03) if adjusted to reflect baseline differences. These estimatescompare with −4.42 ± 1.36 for IDS-C30 (t1320 = 3.24, P = .002) when calculationsare based on comparing ALGO (n = 175) with its matched TAU (n = 175) samples.The underlying hierarchical linear model is stable, because the adjusted estimatedinitial effect and robust standard error (IDS-C30 D = −4.42± 1.36) are comparable to their least-squares equivalent (IDS-C30 D = −4.80 ± 1.20).

Of the 181 evaluable ALGO patients, 175 (96.7%) from 4 clinics (38%,24%, 21%, and 17%, respectively) were matched successfully to 175 TAU patientsfrom 10 clinics (18%, 14%, 13%, 13%, 12%, 9%, 8%, 6%, 4%, and 3%, respectively),including 100 (47.2%) from TAUinALGO and 75 (48.7%) from TAUnonALGO. As expected,the final analytic ALGO (n = 175) and TAU (n = 175) samples had comparablebaseline IDS-C30 scores (42.0 ± 13.1 vs 41.7 ± 12.7, Δ= 0.22, t348 = 0.16, P<.87) and were comparable on most other demographic and healthvariables (Table 2).

Table Graphic Jump LocationTable 2. Baseline Characteristics of the Analytic Sample by TreatmentGroup*

Although resolving regression to the mean issues was important, theimpact of poststudy matching on external validity is unclear. Specifically,compared with unmatched evaluable patients (n = 197), the final analytic sample(n = 350) had more depressive symptoms (IDS-C30 total score = 41.8vs 33.2; Δ = 8.7; t545 = 7.4; P<.001), poorer mental functioning (SF-12 Mental HealthSummary [MHS] score = 27.8 vs 32.5; Δ = −4.7; t527 = 5.3; P<.001), and shorterlength of illness (13.2 vs 19.8 years; Δ = −6.6; t533 = 6.0; P<.001) and wereslightly younger (41.4 vs 43.7 years; Δ = −2.3; t543 = 2.3; P<.02). No otherbaseline features distinguished these 2 groups.

Baseline covariates were not statistically significant predictors ofchange scores. Thus, potential biases introduced by these factors are likelyto be small. These analyses were limited to primary outcome measures overall analytic patients and not broken down by baseline symptom scores. Thisunderscores our finding of a difference between ALGO vs TAU on both initialand growth rate effects. Neither family size nor disposable income predictedoutcome. Medicaid and other public assistance variables, when included intothe existing model, did not improve the exploratory power of the includedcovariates.

CLINICAL EFFICACY

The percentage of patients available for analyses at 3, 6, 9, and 12months were 100%, 99.5%, 83.2%, and 75.9%, respectively. As such, retentionfor the analyzable sample was considered excellent. The efficacy analyseswere conducted on the analytic sample of 350 patients (n = 175 each from ALGOand TAU). For the primary outcome measure, both TAU and ALGO groups had significantdecreases in IDS-C30 scores during the first 3 months, with continuingreductions during the subsequent 9 months. The initial decline was significantlygreater for ALGO than TAU during the first 3 months. This advantage for ALGOover TAU persisted throughout the ensuing 3 quarters (ie, there was no catch-upby TAU) (Figure 3). Table 3 gives changes in IDS-C30 scores in subgroupsdetermined by baseline level of symptom severity.

Place holder to copy figure label and caption
Figure 3.

Adjusted mean symptoms for allpatients according to the 30-item Inventory of Depressive Symptomatology–Clinician-Ratedscale (IDS-C30) during 12-month algorithm-guided treatment (ALGO)compared with treatment as usual (TAU) (N = 350).

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Table Graphic Jump LocationTable 3. Baseline IDS-C30 Score–Adjusted Estimatesof Initial Effect, Growth Rate Effect, and Initial Effect × Growth RateEffect Differences Between ALGO and TAU Patients With Major Depressive Disorder*

To explore whether ALGO effects varied depending on baseline symptomseverity, we further subdivided the sample into (1) very severe, (2) severe,and (3) mild/moderate baseline symptoms by IDS-C30 score defineda priori. These analyses revealed that the effects obtained with ALGO werelargely accounted for by patients with severe and very severe baseline IDS-C30 symptoms. The study was powered only to address the comparison betweenALGO and TAU for the overall groups. Therefore, these analyses comparing subgroups(based on different severity) are hypothesis generating rather than definitive.

The IDS-SR30 revealed that ALGO was associated with significantlygreater symptom reduction during the first 3 months than was TAU. Both groupscontinued to improve during the subsequent 9 months, although TAU patientsshowed no evidence of catching up to their ALGO counterparts (Table 4 and Figure 4). Table 4 gives the overall changes in IDS-SR30scores and changes based on subgroups defined by baseline symptomseverity using the IDS-SR30. The differences between ALGO and TAUwere statistically significant for the very severely ill (IDS-SR30 score,≥58) and for the severely ill (IDS-SR30 score, 30-57) groups.

Table Graphic Jump LocationTable 4. Baseline IDS-SR30 Score–Adjusted Estimatesof Initial Effect, Growth Rate Effect, and Initial Effect × Growth RateEffect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
Place holder to copy figure label and caption
Figure 4.

Adjusted mean symptoms for allpatients according to the 30-item Inventory of Depressive Symptomatology–Self-Reportscale (IDS-SR30) during 12-month algorithm-guided treatment (ALGO)compared with treatment as usual (TAU) (N = 350).

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The BPRS24 total scores revealed that both groups had significantsymptomatic improvements after the first quarter, with continued improvementduring the subsequent 9 months (data not shown). Mental functioning, as measuredby the SF-12 MHS score, improved initially and over time for both the ALGOand TAU groups, although the ALGO group experienced a significantly greaterinitial improvement, with no discernible catch up for their TAU counterparts(Table 5 and Figure 5). This effect was most profound for those with the lowestbaseline SF-12 MHS score. No significant between-group differences were observedfor the SF-12 MHS score for either group (data not shown).

Table Graphic Jump LocationTable 5. Baseline SF-12 MHS Score–Adjusted Estimates of InitialEffect, Growth Rate Effect, and Initial Effect × Growth Rate EffectDifferences Between ALGO and TAU Patients With Major Depressive Disorder*74
Place holder to copy figure label and caption
Figure 5.

Adjusted mean function for allpatients according to the Mental Health Summary (MHS) score of the MedicalOutcomes Study 12-item Short-Form Health Survey (SF-12) during 12-month algorithm-guidedtreatment (ALGO) compared with treatment as usual (TAU) (N = 340).

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ALGO patients with significant side effects at baseline (n = 77) tendedto report more side effects at the end of 3 months than their TAU counterparts(n = 76) (odds ratio [OR], 1.85; 95% confidence interval [CI], 0.99-3.47; t567 = 1.94; P<.053).Differences in growth rates in side effects during follow-up were not significant(OR, 0.82 per quarter; 95% CI, 0.71-1.07; t567 = 1.35; P<.18). Furthermore, ALGO patients(n = 87) who reported no significant side effects at baseline did not differfrom their TAU counterparts (n = 95) either after the first quarter (OR, 0.98;95% CI, 0.55-1.73; t629 = 0.08; P<.94) or in growth rates during follow-up (OR, 1.02per quarter; 95% CI, 0.82-1.26; t629 =0.16; P<.87).

This is the first study, to our knowledge, to assess the short- andlonger-term effects of treatment algorithms in the care of psychiatric patientswith MDD in the public mental health sector. At baseline, this patient populationwas characterized by substantial symptom severity, poor daily functioning,significant concurrent general medical conditions, and frequent alcohol andother substance abuse.

The ALGO intervention was associated with statistically and clinicallysignificantly better clinical outcomes than TAU in the primary (and most secondary)efficacy assessments, including IDS-C30, IDS-SR30, andSF-12 MHS scores. The magnitude of the difference between ALGO and TAU wasrobust (mean IDS-C30 difference = 4.5 points; mean IDS-SR30 difference = 7.5 points). The significant advantage for ALGO wasseen in the first quarter, with no evidence that TAU patients caught up withtheir ALGO counterparts during the ensuing 9-month period. Exploratory analysessuggested that ALGO was superior to TAU in those with greater symptom severityor worse function (SF-12) at baseline. The magnitude of the difference betweenTAU and ALGO is clinically substantial. By the clinician rating, twice asmuch (and by the self-report, 3 times as much) symptom reduction occurredin ALGO than in TAU. A 4.4-point difference in IDS-C30 is roughlyequivalent to a 3-point difference on the Hamilton Rating Scale for Depression,which is the difference typically found in drug-placebo comparisons, yet herewe are comparing 2 active treatments (TAU and ALGO).

Results are generalizable to public sector patients with psychotic ornonpsychotic MDD. This population is characterized by substantial socioeconomicdisadvantages, long-standing depressive illness, and likely varying degreesof treatment resistance. Whether similar results would be found with employed,better-educated depressed patients seen in private practice is unclear.

Despite robust benefits attributable to ALGO, even among the responders,substantial symptoms remained. The fact that significant symptoms and functionalimpairment persisted points to the severity, comorbidity, chronicity, or possibletreatment resistance in this population. Results also raise the question ofwhether the outcomes would be different (more robust) if ALGO was used inless severely and chronically ill populations or in different treatment environments.On the other hand, those patients with particularly high health service utilizationmight accrue even greater benefits than patients with less complicated illnesses.

Furthermore, the study intervention was directed only toward optimizingpharmacotherapy and patient adherence. These results suggest the need to studythe effects of a broader-based intervention that would integrate evidence-basedpsychotherapy with evidence-based pharmacotherapy, as well as changes in thehealth service provision systems, to enhance physician adherence to evidence-basedtreatments.

Also, ALGO physicians likely demonstrated varying levels of algorithmadherence. It is possible that the clinical impact would be greater if physicianalgorithm adherence was monitored more closely and facilitated with real-timefeedback provided to clinicians to enhance decision making and algorithm adherence.In future analyses, we will examine whether physician adherence to the algorithmsis related to patient outcomes. Physicians' average patient load was not alteredduring the study and thus could have negatively affected their adherence tothe algorithms.

Limitations to the present study include the fact that although thestudy clinics were matched, the clinics, patients, and physicians were notrandomly assigned to the study groups (ie, ALGO or TAU), which may have introduceda bias. The outcome assessors were not blinded to treatment assignment andcould have biased the results in favor of the ALGO group. However, self-reports(IDS-SR30, SF-12) corroborated clinician ratings of the benefitsof ALGO. In addition, varying degrees of algorithm adherence were accepted.

Despite its limitations, the results of this study have significantimplications for the provision of mental health care. Our findings, togetherwith reports of enhanced outcomes reported in primary care66,8186 settingswith the use of enriched disease management programs, suggest ways to enhanceclinical practice and care systems that might improve clinical outcomes andpositively affect health care utilization. Evidence to date indicates thatcare systems and practice procedures that attempt to apply practice guidelines,improve the consistency of care provided, and improve patient adherence appearto provide improved patient outcomes (both depressive symptoms and function).

Future studies need to evaluate how we can ensure more consistent implementationof disease management programs. To accomplish behavioral change, these issuesneed to be examined at both the practitioner and organizational levels. Atthe practitioner level, we need to explore mechanisms to increase algorithmadherence, including academic detailing, continuous quality improvement, andcomputerized decision support systems.8789 Atthe organizational level, we need to explore modes that more efficiently implementchange and more effectively allow practitioners to provide care. As notedin the Institute of Medicine's report, "Crossing the Quality Chasm: A NewHealth System for the 21st Century,"90 improvingthe quality of health care in the United States requires not only changinghealth care professionals and organizations but also better methods of disseminatinginformation, application of technology, communication systems, and the creationof payment systems that reward positive performance. This is obviously anevolutionary process. We hope that this study provides a step toward additionalresearch to improve practice procedures and to provide care to enhance theoutcomes for individuals with depressive disorders.

Correspondence: Madhukar H. Trivedi, MD, Department of Psychiatry,University of Texas Southwestern Medical Center, 6363 Forest Park Rd, Suite1300, Dallas, TX 75235 (madhukar.trivedi@utsouthwestern.edu).

Submitted for publication June 11, 2002; final revision received December31, 2003; accepted January 28, 2004.

This research was supported by National Institute of Mental Health (NIMH)grant MH-53799; NIMH R01 No. R01MH064062-01A2 (Dr Trivedi); the Robert WoodJohnson Foundation (Princeton, NJ); the Meadows Foundation (Dallas); the Lightner-SamsFoundation (Dallas); the Nanny Hogan Boyd Charitable Trust (Dallas); the TexasDepartment of Mental Health and Mental Retardation (Austin); the Center forMental Health Services (Washington, DC); the Department of Veterans Affairs(Washington, DC); Health Services Research and Development Research CareerScientist Award (RCS92-403); the Betty Jo Hay Distinguished Chair in MentalHealth and the Rosewood Corporation Chair in Biomedical Science (Dr Rush);the United States Pharmacopoeia Convention, Inc (Rockville, Md); Mental HealthConnections, a partnership between Dallas County Mental Health and MentalRetardation and the Department of Psychiatry of the University of Texas SouthwesternMedical Center, which receives funding from the Texas State Legislature andthe Dallas County Hospital District and the University of Texas at AustinCollege of Pharmacy; and the Southwestern Drug Corporation Centennial Fellowshipin Pharmacy (Dr Crismon). The following pharmaceutical companies providedunrestricted educational grants: Abbott Laboratories (Abbott Park, Ill), AstraZenecaPLC (Wilmington, Del), Bristol-Myers Squibb (New York, NY), Eli Lilly &Company (Indianapolis, Ind), Forest Laboratories Inc (New York), GlaxoSmithKline(Research Triangle Park, NC), Janssen Pharmaceutica (Titusville, NJ), NovartisInternational AG (Basel, Switzerland), Organon International Inc (Roseland,NJ), Pfizer Inc (New York), and WyethAyerst Laboratories Inc (Madison, NJ).

Results of this study were presented in part at the annual meeting ofthe American College of Neuropsychopharmacology; December 12, 2000; SanJuan, Puerto Rico; the annual meeting of the American PsychiatricAssociation; May 7, 2001; New Orleans, La; the annual meeting of theAmerican College of Clinical Pharmacy; October 21-24, 2001; Tampa, Fla;and the annual meeting of the American Psychiatric Association; May 21,2002; Philadelphia, Pa.

We deeply appreciate the consultations provided by Barbara Burns, PhD,Robert Drake, MD, Susan Essock, PhD, William Hargreaves, PhD, Teh-wei Hu,PhD, Anthony Lehman, MD, and Greg Teague, PhD, without whose expertise thisstudy could not have been accomplished. We also thank the National and TexasAlliance for the Mentally Ill and the National and Texas Depressive and ManicDepressive Association for providing patient and family educational materials.We appreciate greatly the assistance of Gus Sicard, PhD, for his translationof the educational, clinical, and research evaluation materials into Spanish.We thank Karla Starkweather, BJ (TMAP communications director, Texas Departmentof Mental Health and Mental Retardation, Austin). Most important, we wishto express our gratitude to the personnel at each community mental healthcenter for allowing us to conduct this research with their staff and patients,including Andrews Center, Tyler; Center for Health Care Services, San Antonio;Life Management Center, El Paso; Lubbock Regional Mental Health and MentalRetardation Center; Mental Health and Mental Retardation Authority of HarrisCounty, Houston; Tri-County Mental Health and Mental Retardation Services,Conroe; and Tropical Texas Mental Health and Mental Retardation Services,Edinburgh, Harlingen, and Brownsville. We appreciate the secretarial supportof Fast Word Inc (Dallas) and David Savage and thank Andrew Sedillo, MD, forestablishing the TMAP Web site (http://www.mhmr.state.tx.us/centraloffice/medicaldirector/tmap.html). We also thank the assistant module directors, Sherwood Brown, MD,PhD, John Chiles, MD, and Teresa Pigott, MD, and the algorithm managementteam coordinators, Judith Chiles, BSN, Ellen Dennehy, PhD, Ellen Habamacher,BS, and Tracie Key, BSN, RN.

Depression Guideline Panel, Clinical Practice Guideline Number 5: Depressionin Primary Care Volume 1: Detection and Diagnosis.  Rockville, Md US Dept of Health & Human Services, Public HealthService, Agency for Health Care Policy & Research1993;AHCR publication93-0550
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition.  Washington, DC American Psychiatric Association1994;
Keller  MBBoland  RJ Implications of failing to achieve successful long-term maintenancetreatment of recurrent unipolar major depression. Biol Psychiatry. 1998;44348- 360
PubMed Link to Article
Keller  MBLavori  PW Double depression, major depression, and dysthymia: distinct entitiesor different phases of a single disorder? Psychopharmacol Bull. 1984;20399- 402
PubMed
Keller  MBLavori  PWEndicott  JCoryell  WKlerman  GL "Double depression": two-year follow-up. Am J Psychiatry. 1983;140689- 694
PubMed
Keller  MBHirschfeld  RMHanks  D Double depression: a distinctive subtype of unipolar depression. J Affect Disord. 1997;4565- 73
PubMed Link to Article
Guze  SBRobins  E Suicide in primary affective disorders. Br J Psychiatry. 1970;117437- 438
PubMed Link to Article
Nierenberg  AA Current perspectives on the diagnosis and treatment of major depressivedisorder. Am J Manag Care. 2001;7suppl 11S353- S366
PubMed
Carney  RMSaunders  RDFreedland  KEStein  PRich  MWJaffe  AS Association of depression with reduced heart rate variability in coronaryartery disease. Am J Cardiol. 1995;76562- 564
PubMed Link to Article
Stein  PKCarney  RMFreedland  KESkala  JAJaffe  ASKleiger  RERottman  JN Severe depression is associated with markedly reduced heart rate variabilityin patients with stable coronary heart disease. J Psychosom Res. 2000;48493- 500
PubMed Link to Article
Penninx  BWBeekman  ATHonig  ADeeg  DJSchoevers  RAvan Eijk  JTvan Tilburg  W Depression and cardiac mortality: results from a community-based longitudinalstudy. Arch Gen Psychiatry. 2001;58221- 227
PubMed Link to Article
Frasure-Smith  NLésperance  FTalajic  M Depression following myocardial infarction: impact on 6-month survival. JAMA. 1993;2701819- 1825
PubMed Link to Article
Bush  DEZiegelstin  RCTayback  MRichter  DStevens  SZahalsky  HFauerbach  JA Even minimal symptoms of depression increase mortality risk after acutemyocardial infarction. Am J Cardiol. 2001;88337- 341
PubMed Link to Article
Lésperance  FFrasure-Smith  NTalajic  MBourassa  MG Five-year risk of cardiac mortality in relation to initial severityand one-year changes in depression symptoms after myocardial infarction. Circulation. 2002;1051049- 1053
PubMed Link to Article
Fishbain  DACutler  RRosomoff  HLRosomoff  RS Chronic pain-associated depression: antecedent or consequence of pain?a review. Clin J Pain. 1997;13116- 137
PubMed Link to Article
Egede  LEZheng  DSimpson  K Comorbid depression is associated with increased health care use andexpenditures in individuals with diabetes. Diabetes Care. 2002;25464- 470
PubMed Link to Article
Zielinski  TABrown  ESNejtek  VAKhan  DAMoore  JJRush  AJ Depression in asthma: prevalence and clinical implications. Prim Care Companion J Clin Psychiatry. 2000;2153- 158
PubMed Link to Article
Mancuso  CARincon  MMcCulloch  CECharlson  ME Self-efficacy, depressive symptoms, and patients' expectations predictoutcomes in asthma. Med Care. 2001;391326- 1338
PubMed Link to Article
Mintz  JMintz  LIArruda  MJHwang  SS Treatments of depression and the functional capacity to work. Arch Gen Psychiatry. 1992;49761- 768
PubMed Link to Article
Rupp  A The economic community consequences of not treating depression. Br J Psychiatry. 1995;16629- 33
PubMed Link to Article
Berndt  ERFinkelstein  SNGreenberg  PEHowland  RHKeith  ARush  AJRussell  JKeller  MB Workplace performance effects from chronic depression and its treatment. J Health Econ. 1998;17511- 535
PubMed Link to Article
Greenberg  PEStiglin  LEFinkelstein  SNBerndt  ER The economic burden of depression in 1990. J Clin Psychiatry. 1993;54405- 418
PubMed
Greenberg  PELeong  SABirnbaum  HG Cost of depression: current assessment and future directions. Exp Rev Pharmacoeconom Outcomes Res. 2001;169- 76
Link to Article
Stoudemire  AFrank  RHedemark  N The economic burden of depression. Gen Hosp Psychiatry. 1986;8387- 394
PubMed Link to Article
Rice  DPMiller  LS The economic burden of affective disorders. Adv Health Econ Health Serv Res. 1993;1437- 53
PubMed
Keller  MBHarrison  WFawcett  JAGelenberg  AHirschfeld  RMAKlein  DKocsis  JMcCullough  JPRush  AJSchatzberg  AThase  ME Treatment of chronic depression with sertraline or imipramine: preliminaryblinded response rates and high rates of undertreatment in the community. Psychopharmacol Bull. 1995;31205- 212
PubMed
Hirschfeld  RMKeller  MBPanico  SArons  BSBarlow  DDavidoff  FEndicott  JFroom  JGoldstein  MGorman  JMMarek  RGMaurer  TAMeyer  RPhillips  KRoss  JSchwenk  TLSharfstein  SSThase  MEWyatt  RJ The National Depressive and Manic-Depressive Association consensusstatement on the undertreatment of depression. JAMA. 1997;277333- 340
PubMed Link to Article
Rush  AJTrivedi  MH Treating depression to remission. Psychiatr Ann. 1995;25704- 705, 709
Link to Article
Depression Guideline Panel, Clinical Practice Guideline Number 5: Depressionin Primary Care Volume 2: Treatment of Major Depression.  Rockville, Md US Dept of Health & Human Services, Public HealthService, Agency for Health Care Policy & Research.1993;AHCPR publication93-0551.
American Psychiatric Association, Practice guideline for major depressive disorder in adults. Am J Psychiatry. 1993;150suppl 41- 26
PubMed
American Psychiatric Association, Practice guideline for the treatment of patients with major depressivedisorder (revision). Am J Psychiatry. 2000;1574 suppl1- 45
PubMed
Frank  EKarp  JFRush  AJ Efficacy of treatments for major depression. Psychopharmacol Bull. 1993;29457- 475
PubMed
Miller  IWKeitner  GISchatzberg  AFKlein  DNThase  MERush  AJMarkowitz  JCSchlager  DSKornstein  SGDavis  SMHarrison  WMKeller  MB The treatment of chronic depression, part 3: psychosocial functioningbefore and after treatment with sertraline or imipramine. J Clin Psychiatry. 1998;59608- 619
PubMed Link to Article
Scott  JTeasdale  JDPaykel  ESJohnson  ALAbbott  RHayhurst  HMoore  RGarland  A Effects of cognitive therapy on psychological symptoms and social functionin residual depression. Br J Psychiatry. 2000;177440- 446
PubMed Link to Article
Paykel  ES Remission and residual symptomatology in major depression. Psychopathology. 1998;315- 14
PubMed Link to Article
Paykel  ESRamana  RCooper  ZHayhurst  HKerr  JBarocka  A Residual symptoms after partial remission: an important outcome indepression. Psychol Med. 1995;251171- 1180
PubMed Link to Article
Paykel  ESScott  JTeasdale  JDJohnson  ALGarland  AMoore  RJenaway  ACornwall  PLHayhurst  HAbbott  RPope  M Prevention of relapse in residual depression by cognitive therapy:a controlled trial. Arch Gen Psychiatry. 1999;56829- 835
PubMed Link to Article
Judd  LLAkiskal  HSMaser  JDZeller  PJEndicott  JCoryell  WPaulus  MPKunovac  JLLeon  ACMueller  TIRice  JAKeller  MB A prospective 12-year study of subsyndromal and syndromal depressivesymptoms in unipolar major depressive disorders. Arch Gen Psychiatry. 1998;55694- 700
PubMed Link to Article
Judd  LLAkiskal  HSMaser  JDZeller  PJEndicott  JCoryell  WPaulus  MPKunovac  JLLeon  ACMueller  TIRice  JAKeller  MB Major depressive disorder: a prospective study of residual subthresholddepressive symptoms as predictor of rapid relapse. J Affect Disord. 1998;5097- 108
PubMed Link to Article
Fava  MRush  AJTrivedi  MHNierenberg  AAThase  MESackeim  HAQuitkin  FMWisniewski  SLavori  PWRosenbaum  JFKupfer  DJSTAR*D Investigators Group, Background and rationale for the Sequenced Treatment Alternatives toRelieve Depression (STAR*D) study. Psychiatr Clin North Am. 2003;26457- 494
PubMed Link to Article
Kocsis  JHFriedman  RAMarkowitz  JCLeon  ACMiller  NLGniwesch  LParides  M Maintenance therapy for chronic depression: a controlled trial of desipramine. Arch Gen Psychiatry. 1996;53769- 776
PubMed Link to Article
Koran  LMGelenberg  AJKornstein  SGHowland  RHFriedman  RADeBattista  CKlein  DKocsis  JHSchatzberg  AFThase  MERush  AJHirschfeld  RMALaVange  LMKeller  MB Sertraline versus imipramine to prevent relapse in chronic depression. J Affect Disord. 2001;6527- 36
PubMed Link to Article
Hirschfeld  RM Clinical importance of long-term antidepressant treatment. Br J Psychiatry. 2001;179suppl 42S4- S8
Link to Article
Rush  AJTrivedi  MHSchriger  DPetty  F The development of clinical practice guidelines for the diagnosis andtreatment of depression. Gen Hosp Psychiatry. 1992;14230- 236
PubMed Link to Article
Amsterdam  JDHornig-Rohan  M Treatment algorithms in treatment-resistant depression. Psychiatr Clin North Am. 1996;19371- 386
PubMed Link to Article
Trivedi  MHDeBattista  CFawcett  JNelson  COsser  DNStein  DJobson  K Developing treatment algorithms for unipolar depression in Cyberspace:International Psychopharmacology Algorithm Project (IPAP). Psychopharmacol Bull. 1998;34355- 359
PubMed
Gilbert  DAAltshuler  KZRago  WVShon  SPCrismon  MLToprac  MGRush  AJ Texas Medication Algorithm Project: definitions, rationale, and methodsto develop medication algorithms. J Clin Psychiatry. 1998;59345- 351
PubMed Link to Article
Crismon  MLTrivedi  MHPigott  TARush  AJHirschfeld  RMAKahn  DADeBattista  CNelson  JCNierenberg  AASackeim  HAThase  METexas Consensus Conference Panel, The Texas Medication Algorithm Project: Report of the Texas ConsensusConference Panel on medication treatment of major depressive disorder. J Clin Psychiatry. 1999;60142- 156
PubMed Link to Article
Rush  AJRago  WVCrismon  MLToprac  MGShon  SPSuppes  TMiller  ALTrivedi  MHSwann  ACBiggs  MMShores-Wilson  KKashner  TMPigott  TChiles  JAGilbert  DAAltshuler  KZ Medication treatment for the severely and persistently mentally ill:the Texas Medication Algorithm Project. J Clin Psychiatry. 1999;60284- 291
PubMed Link to Article
Rush  AJCrismon  MLToprac  MGShon  SSRago  WVMiller  ALSuppes  TTrivedi  MHBiggs  MMShores-Wilson  KKashner  TMAltshuler  KZ Implementing guidelines and systems of care: Experiences with the TexasMedication Algorithm Project (TMAP). J Pract Psychiatry Behav Health. 1999;575- 86
Adli  MBerghofer  ALinden  MHelmchen  HMuller-Oerlinghausen  BMackert  AStamm  TBauer  M Effectiveness and feasibility of a standardized stepwise drug treatmentregimen algorithm for inpatients with depressive disorders: results of a 2-yearobservational algorithm study. J Clin Psychiatry. 2002;63782- 790
PubMed Link to Article
Katon  WVon Korff  MLin  EWalker  ESimon  GEBush  TRobinson  PRusso  J Collaborative management to achieve treatment guidelines: impact ondepression in primary care. JAMA. 1995;2731026- 1031
PubMed Link to Article
Passik  SDKirsh  KLTheobald  DDonaghy  KHoltsclaw  EEdgerton  SDugan  W Use of a depression screening tool and a fluoxetine-based algorithmto improve the recognition and treatment of depression in cancer patients:a demonstration project. J Pain Symptom Manage. 2002;24318- 327
PubMed Link to Article
Rollman  BLHanusa  BHLowe  HJGilbert  TKapoor  WNSchulberg  HC A randomized trial using computerized decision support to improve treatmentof major depression in primary care. J Gen Intern Med. 2002;17493- 503
PubMed Link to Article
Unutzer  JRubenstein  LKaton  WJTang  LDuan  NLagomasino  ITWells  KB Two-year effects of quality improvement programs on medication managementfor depression. Arch Gen Psychiatry. 2001;58935- 942
PubMed Link to Article
van Os  TWvan den Brink  RHJenner  JAvan der Meer  KTiemens  BGOrmel  J Effects on depression pharmacotherapy of a Dutch general practitionertraining program. J Affect Disord. 2002;71105- 111
PubMed Link to Article
Williams  JW  JrBarrett  JOxman  TFrank  EKaton  WSullivan  MCornell  JSengupta  A Treatment of dysthymia and minor depression in primary care: a randomizedcontrolled trial in older adults. JAMA. 2000;2841519- 1526
PubMed Link to Article
Meredith  LSJackson-Triche  MDuan  NRubenstein  LVCamp  PWells  KB Quality improvement for depression enhances long-term treatment knowledgefor primary care clinicians. J Gen Intern Med. 2000;15868- 877
PubMed Link to Article
Brody  DSDietrich  AJdeGruy  F  IIIKroenke  K The depression in primary care tool kit. Int J Psychiatry Med. 2000;3099- 110
PubMed Link to Article
Banazak  DAMullan  PBGardner  JCRajagopalan  S Practice guidelines and late-life depression assessment in long-termcare. J Gen Intern Med. 1999;14438- 440
PubMed Link to Article
Rollman  BLGilbert  TLowe  HJKapoor  WNSchulberg  HC The electronic medical record: its role in disseminating depressionguidelines in primary care practice. Int J Psychiatry Med. 1999;29267- 286
PubMed Link to Article
Carney  PADietrich  AJEliassen  MSOwen  MBadger  LW Recognizing and managing depression in primary care: a standardizedpatient study. J Fam Pract. 1999;48965- 972
PubMed
Glasser  MGravdal  JA Assessment and treatment of geriatric depression in primary care settings. Arch Fam Med. 1997;6433- 438
PubMed Link to Article
Katon  WRobinson  PVon Korff  MLin  EBush  TLudman  ESimon  GWalker  E A multifaceted intervention to improve treatment of depression in primarycare. Arch Gen Psychiatry. 1996;53924- 932
PubMed Link to Article
Schulberg  HCBlock  MRMadonia  MJRodriguez  EScott  CPLave  J Applicability of clinical pharmacotherapy guidelines for major depressionin primary care settings. Arch Fam Med. 1995;495- 96
PubMed Link to Article
Rush  AJCrismon  MLKashner  TMToprac  MGCarmody  TJTrivedi  MHSuppes  TMiller  ALBiggs  MMShores-Wilson  KWitte  BPShon  SPRago  WVAltshuler  KZTMAP Research Group, Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale andstudy design. J Clin Psychiatry. 2003;64357- 369
PubMed Link to Article
Toprac  MGRush  JAConner  TMCrismon  MLDees  MHopkins  CRowe  VShon  SP The Texas Medication Algorithm Project Patient and Family EducationProgram: a consumer-guided initiative. J Clin Psychiatry. 2000;61477- 486
PubMed Link to Article
Texas Implementation of Medication Algorithms (TIMA), Guidelines for Treating Major Depressive Disorder: TIMA Physician ProceduralManual. Available at:http://www.mhmr.state.tx.us/centraloffice/medicaldirector/timamddman.pdfJuly 23, 2003.
Overall  JEGorham  DR Introduction: the Brief Psychiatric Rating Scale (BPRS): recent developmentsin ascertainment and scaling. Psychopharmacol Bull. 1988;2497- 99
Ventura  JGreen  MFShaner  ALiberman  RP Training and quality assurance with the Brief Psychiatric Rating Scale:"the drift busters”. Int J Methods Psychiatr Res. 1993;3221- 244
Ventura  JNuechterlein  KHSubotnik  KGilbert  E Symptom dimensions in recent-onset schizophrenia: the 24-item expandedBPRS. Schizophr Res. 1995;1522
Link to Article
Stein  LTest  M Alternative to mental hospital treatment, I: conceptual model, treatmentprogram, and clinical evaluation. Arch Gen Psychiatry. 1980;37392- 397
PubMed Link to Article
Rush  AJGullion  CMBasco  MRJarrett  RBTrivedi  MH The inventory of depressive symptomatology (IDS): psychometric properties. Psychol Med. 1996;26477- 486
PubMed Link to Article
Ware  JEKosinski  MKeller  SD A 12-item Short-Form Health Survey (SF-12): construction of scalesand preliminary tests of reliability and validity. Med Care. 1996;34220- 233
PubMed Link to Article
Skinner  HA The Drug Abuse Screening Test. Addict Behav. 1982;7363- 371
PubMed Link to Article
Selzer  MJ The Michigan Alcoholism Screening Test: the quest for a new diagnosticinstrument. Am J Psychiatry. 1971;1271653- 1658
PubMed
Bryk  ASRaudenbush  SW Hierarchical Linear Models: Applications and DataAnalysis Methods. Advanced Quantitative Techniques in the Social Sciences, Volume 1. Newbury Park, Calif SAGE Publications1992;
Kashner  TMCarmody  TJSuppes  TRush  AJCrismon  MLMiller  ALToprac  MTrivedi  MH Catching-up on health outcomes: the Texas Medication Algorithm Project. Health Serv Res. 2003;38311- 331
PubMed Link to Article
Kashner  TMRosenheck  RCampinell  ABSurís  ACrandall  RGarfield  NJLapuc  PPyrcz  KSoyka  TWicker  A Impact of work therapy on health status among homeless, substance dependentveterans: a randomized controlled trial. Arch Gen Psychiatry. 2002;59938- 944
PubMed Link to Article
Bryk  ASRaudenbush  SWCongdon  RT  Jr HLM – Hierarchical Linear Modeling (Version5.0).  St Paul, Minn Assessment Systems Corp2001;
Wells  KBSturm  RSherbourne  CDMeredith  LS Caring for Depression.  Cambridge, Mass Harvard University Press1996;
Katon  WVon Korff  MLin  EUnutzer  JSimon  GWalker  ELudman  EBush  T Population-based care of depression: effective disease management strategiesto decrease prevalence. Gen Hosp Psychiatry. 1997;19169- 178
PubMed Link to Article
Unützer  JRubenstein  LKaton  WTang  LDuan  NLagomasino  ITWells  KB Two-year effects of quality improvement programs on medication managementfor depression. Arch Gen Psychiatry. 2001;58935- 942
PubMed Link to Article
Hunkeler  EMMeresman  JFHargreaves  WAFireman  BBerman  WHKirsch  AJGroebe  JHurt  SWBraden  PGetzell  MFeigenbaum  PAPeng  TSalzer  M Efficacy of nurse telehealth care and peer support in augmenting treatmentof depression in primary care. Arch Fam Med. 2000;9;700- 708
PubMed
Unützer  JKaton  WCallahan  CMWilliams  JW  JrHunkeler  EHarpole  LHoffing  MPenna  RD DellaNoel  PHLin  EHArean  PAHegel  MDTang  LBelin  TROishi  SLangston  C Collaborative care management of late-life depression in the primarycare setting: a randomized controlled trial. JAMA. 2002;2882836- 2845
PubMed Link to Article
Liu  CFHedrick  SCChaney  EFHeagert  PFelker  BHasanberg  NFihn  SKaton  W Cost-effectiveness of collaborative care for depression in a primarycare veteran population. Psychiatr Serv. 2003;54698- 704
PubMed Link to Article
Trivedi  MHKern  JKBaker  SMAltshuler  KZ Computerizing medication algorithms and decision support systems formajor psychiatric disorders. J Psychiatr Pract. 2000;6237- 246
Link to Article
Trivedi  MHKern  JKVoegtle  TMBaker  SMAltshuler  KZ Computerized medication algorithms in behavioral health care. Dewan  NALorenzi  NRiley  RBhattacharya  SRedsBehavioral Healthcare Informatics. New York, NY Springer-Verlag2001;53- 66
Trivedi  MHKern  JKMarcee  AGrannemann  BKleiber  BBettinger  TAltshuler  KZMcClelland  A Development and implementation of computerized clinical guidelines:barriers and solutions. Methods Inf Med. 2002;41435- 442
PubMed
Institute of Medicine Committee on Quality of Health Care in America, Crossing the Quality Chasm: A New Health System forthe 21st Century.  Washington, DC National Academy Press2001;

Figures

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Figure 1.

Strategies for the treatment ofnonpsychotic major depressive disorder. Asterisk indicates consider TCA/VLFif not tried; dagger, lithium, thyroid, buspirone; double dagger, skip iflithium augmentation has already failed; section mark, most studied combination.BUPSR indicates bupropion sustained release; cital, citalopram;fluox, fluoxetine; MAOI, monoamine oxidase inhibitor; MRT, mirtazapine; SSRI,selective serotonin reuptake inhibitor; TCA, tricyclic antidepressants; VLFXR, venlafaxine extended release. This figure is published with permissionfrom the Texas Department of Mental Health and Mental Retardation and is partof a state-funded project.

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Figure 2.

Strategies for the treatment ofpsychotic major depressive disorder. ECT indicates electroconvulsive therapy;SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant;VLF, venlafaxine. This figure is published with permission from the TexasDepartment of Mental Health and Mental Retardation and is part of a state-fundedproject.

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Figure 3.

Adjusted mean symptoms for allpatients according to the 30-item Inventory of Depressive Symptomatology–Clinician-Ratedscale (IDS-C30) during 12-month algorithm-guided treatment (ALGO)compared with treatment as usual (TAU) (N = 350).

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Figure 4.

Adjusted mean symptoms for allpatients according to the 30-item Inventory of Depressive Symptomatology–Self-Reportscale (IDS-SR30) during 12-month algorithm-guided treatment (ALGO)compared with treatment as usual (TAU) (N = 350).

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Place holder to copy figure label and caption
Figure 5.

Adjusted mean function for allpatients according to the Mental Health Summary (MHS) score of the MedicalOutcomes Study 12-item Short-Form Health Survey (SF-12) during 12-month algorithm-guidedtreatment (ALGO) compared with treatment as usual (TAU) (N = 340).

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Tables

Table Graphic Jump LocationTable 1. Ethnic Composition and Characteristics of Participating Sitesand Clinics
Table Graphic Jump LocationTable 2. Baseline Characteristics of the Analytic Sample by TreatmentGroup*
Table Graphic Jump LocationTable 3. Baseline IDS-C30 Score–Adjusted Estimatesof Initial Effect, Growth Rate Effect, and Initial Effect × Growth RateEffect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
Table Graphic Jump LocationTable 4. Baseline IDS-SR30 Score–Adjusted Estimatesof Initial Effect, Growth Rate Effect, and Initial Effect × Growth RateEffect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
Table Graphic Jump LocationTable 5. Baseline SF-12 MHS Score–Adjusted Estimates of InitialEffect, Growth Rate Effect, and Initial Effect × Growth Rate EffectDifferences Between ALGO and TAU Patients With Major Depressive Disorder*74

References

Depression Guideline Panel, Clinical Practice Guideline Number 5: Depressionin Primary Care Volume 1: Detection and Diagnosis.  Rockville, Md US Dept of Health & Human Services, Public HealthService, Agency for Health Care Policy & Research1993;AHCR publication93-0550
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition.  Washington, DC American Psychiatric Association1994;
Keller  MBBoland  RJ Implications of failing to achieve successful long-term maintenancetreatment of recurrent unipolar major depression. Biol Psychiatry. 1998;44348- 360
PubMed Link to Article
Keller  MBLavori  PW Double depression, major depression, and dysthymia: distinct entitiesor different phases of a single disorder? Psychopharmacol Bull. 1984;20399- 402
PubMed
Keller  MBLavori  PWEndicott  JCoryell  WKlerman  GL "Double depression": two-year follow-up. Am J Psychiatry. 1983;140689- 694
PubMed
Keller  MBHirschfeld  RMHanks  D Double depression: a distinctive subtype of unipolar depression. J Affect Disord. 1997;4565- 73
PubMed Link to Article
Guze  SBRobins  E Suicide in primary affective disorders. Br J Psychiatry. 1970;117437- 438
PubMed Link to Article
Nierenberg  AA Current perspectives on the diagnosis and treatment of major depressivedisorder. Am J Manag Care. 2001;7suppl 11S353- S366
PubMed
Carney  RMSaunders  RDFreedland  KEStein  PRich  MWJaffe  AS Association of depression with reduced heart rate variability in coronaryartery disease. Am J Cardiol. 1995;76562- 564
PubMed Link to Article
Stein  PKCarney  RMFreedland  KESkala  JAJaffe  ASKleiger  RERottman  JN Severe depression is associated with markedly reduced heart rate variabilityin patients with stable coronary heart disease. J Psychosom Res. 2000;48493- 500
PubMed Link to Article
Penninx  BWBeekman  ATHonig  ADeeg  DJSchoevers  RAvan Eijk  JTvan Tilburg  W Depression and cardiac mortality: results from a community-based longitudinalstudy. Arch Gen Psychiatry. 2001;58221- 227
PubMed Link to Article
Frasure-Smith  NLésperance  FTalajic  M Depression following myocardial infarction: impact on 6-month survival. JAMA. 1993;2701819- 1825
PubMed Link to Article
Bush  DEZiegelstin  RCTayback  MRichter  DStevens  SZahalsky  HFauerbach  JA Even minimal symptoms of depression increase mortality risk after acutemyocardial infarction. Am J Cardiol. 2001;88337- 341
PubMed Link to Article
Lésperance  FFrasure-Smith  NTalajic  MBourassa  MG Five-year risk of cardiac mortality in relation to initial severityand one-year changes in depression symptoms after myocardial infarction. Circulation. 2002;1051049- 1053
PubMed Link to Article
Fishbain  DACutler  RRosomoff  HLRosomoff  RS Chronic pain-associated depression: antecedent or consequence of pain?a review. Clin J Pain. 1997;13116- 137
PubMed Link to Article
Egede  LEZheng  DSimpson  K Comorbid depression is associated with increased health care use andexpenditures in individuals with diabetes. Diabetes Care. 2002;25464- 470
PubMed Link to Article
Zielinski  TABrown  ESNejtek  VAKhan  DAMoore  JJRush  AJ Depression in asthma: prevalence and clinical implications. Prim Care Companion J Clin Psychiatry. 2000;2153- 158
PubMed Link to Article
Mancuso  CARincon  MMcCulloch  CECharlson  ME Self-efficacy, depressive symptoms, and patients' expectations predictoutcomes in asthma. Med Care. 2001;391326- 1338
PubMed Link to Article
Mintz  JMintz  LIArruda  MJHwang  SS Treatments of depression and the functional capacity to work. Arch Gen Psychiatry. 1992;49761- 768
PubMed Link to Article
Rupp  A The economic community consequences of not treating depression. Br J Psychiatry. 1995;16629- 33
PubMed Link to Article
Berndt  ERFinkelstein  SNGreenberg  PEHowland  RHKeith  ARush  AJRussell  JKeller  MB Workplace performance effects from chronic depression and its treatment. J Health Econ. 1998;17511- 535
PubMed Link to Article
Greenberg  PEStiglin  LEFinkelstein  SNBerndt  ER The economic burden of depression in 1990. J Clin Psychiatry. 1993;54405- 418
PubMed
Greenberg  PELeong  SABirnbaum  HG Cost of depression: current assessment and future directions. Exp Rev Pharmacoeconom Outcomes Res. 2001;169- 76
Link to Article
Stoudemire  AFrank  RHedemark  N The economic burden of depression. Gen Hosp Psychiatry. 1986;8387- 394
PubMed Link to Article
Rice  DPMiller  LS The economic burden of affective disorders. Adv Health Econ Health Serv Res. 1993;1437- 53
PubMed
Keller  MBHarrison  WFawcett  JAGelenberg  AHirschfeld  RMAKlein  DKocsis  JMcCullough  JPRush  AJSchatzberg  AThase  ME Treatment of chronic depression with sertraline or imipramine: preliminaryblinded response rates and high rates of undertreatment in the community. Psychopharmacol Bull. 1995;31205- 212
PubMed
Hirschfeld  RMKeller  MBPanico  SArons  BSBarlow  DDavidoff  FEndicott  JFroom  JGoldstein  MGorman  JMMarek  RGMaurer  TAMeyer  RPhillips  KRoss  JSchwenk  TLSharfstein  SSThase  MEWyatt  RJ The National Depressive and Manic-Depressive Association consensusstatement on the undertreatment of depression. JAMA. 1997;277333- 340
PubMed Link to Article
Rush  AJTrivedi  MH Treating depression to remission. Psychiatr Ann. 1995;25704- 705, 709
Link to Article
Depression Guideline Panel, Clinical Practice Guideline Number 5: Depressionin Primary Care Volume 2: Treatment of Major Depression.  Rockville, Md US Dept of Health & Human Services, Public HealthService, Agency for Health Care Policy & Research.1993;AHCPR publication93-0551.
American Psychiatric Association, Practice guideline for major depressive disorder in adults. Am J Psychiatry. 1993;150suppl 41- 26
PubMed
American Psychiatric Association, Practice guideline for the treatment of patients with major depressivedisorder (revision). Am J Psychiatry. 2000;1574 suppl1- 45
PubMed
Frank  EKarp  JFRush  AJ Efficacy of treatments for major depression. Psychopharmacol Bull. 1993;29457- 475
PubMed
Miller  IWKeitner  GISchatzberg  AFKlein  DNThase  MERush  AJMarkowitz  JCSchlager  DSKornstein  SGDavis  SMHarrison  WMKeller  MB The treatment of chronic depression, part 3: psychosocial functioningbefore and after treatment with sertraline or imipramine. J Clin Psychiatry. 1998;59608- 619
PubMed Link to Article
Scott  JTeasdale  JDPaykel  ESJohnson  ALAbbott  RHayhurst  HMoore  RGarland  A Effects of cognitive therapy on psychological symptoms and social functionin residual depression. Br J Psychiatry. 2000;177440- 446
PubMed Link to Article
Paykel  ES Remission and residual symptomatology in major depression. Psychopathology. 1998;315- 14
PubMed Link to Article
Paykel  ESRamana  RCooper  ZHayhurst  HKerr  JBarocka  A Residual symptoms after partial remission: an important outcome indepression. Psychol Med. 1995;251171- 1180
PubMed Link to Article
Paykel  ESScott  JTeasdale  JDJohnson  ALGarland  AMoore  RJenaway  ACornwall  PLHayhurst  HAbbott  RPope  M Prevention of relapse in residual depression by cognitive therapy:a controlled trial. Arch Gen Psychiatry. 1999;56829- 835
PubMed Link to Article
Judd  LLAkiskal  HSMaser  JDZeller  PJEndicott  JCoryell  WPaulus  MPKunovac  JLLeon  ACMueller  TIRice  JAKeller  MB A prospective 12-year study of subsyndromal and syndromal depressivesymptoms in unipolar major depressive disorders. Arch Gen Psychiatry. 1998;55694- 700
PubMed Link to Article
Judd  LLAkiskal  HSMaser  JDZeller  PJEndicott  JCoryell  WPaulus  MPKunovac  JLLeon  ACMueller  TIRice  JAKeller  MB Major depressive disorder: a prospective study of residual subthresholddepressive symptoms as predictor of rapid relapse. J Affect Disord. 1998;5097- 108
PubMed Link to Article
Fava  MRush  AJTrivedi  MHNierenberg  AAThase  MESackeim  HAQuitkin  FMWisniewski  SLavori  PWRosenbaum  JFKupfer  DJSTAR*D Investigators Group, Background and rationale for the Sequenced Treatment Alternatives toRelieve Depression (STAR*D) study. Psychiatr Clin North Am. 2003;26457- 494
PubMed Link to Article
Kocsis  JHFriedman  RAMarkowitz  JCLeon  ACMiller  NLGniwesch  LParides  M Maintenance therapy for chronic depression: a controlled trial of desipramine. Arch Gen Psychiatry. 1996;53769- 776
PubMed Link to Article
Koran  LMGelenberg  AJKornstein  SGHowland  RHFriedman  RADeBattista  CKlein  DKocsis  JHSchatzberg  AFThase  MERush  AJHirschfeld  RMALaVange  LMKeller  MB Sertraline versus imipramine to prevent relapse in chronic depression. J Affect Disord. 2001;6527- 36
PubMed Link to Article
Hirschfeld  RM Clinical importance of long-term antidepressant treatment. Br J Psychiatry. 2001;179suppl 42S4- S8
Link to Article
Rush  AJTrivedi  MHSchriger  DPetty  F The development of clinical practice guidelines for the diagnosis andtreatment of depression. Gen Hosp Psychiatry. 1992;14230- 236
PubMed Link to Article
Amsterdam  JDHornig-Rohan  M Treatment algorithms in treatment-resistant depression. Psychiatr Clin North Am. 1996;19371- 386
PubMed Link to Article
Trivedi  MHDeBattista  CFawcett  JNelson  COsser  DNStein  DJobson  K Developing treatment algorithms for unipolar depression in Cyberspace:International Psychopharmacology Algorithm Project (IPAP). Psychopharmacol Bull. 1998;34355- 359
PubMed
Gilbert  DAAltshuler  KZRago  WVShon  SPCrismon  MLToprac  MGRush  AJ Texas Medication Algorithm Project: definitions, rationale, and methodsto develop medication algorithms. J Clin Psychiatry. 1998;59345- 351
PubMed Link to Article
Crismon  MLTrivedi  MHPigott  TARush  AJHirschfeld  RMAKahn  DADeBattista  CNelson  JCNierenberg  AASackeim  HAThase  METexas Consensus Conference Panel, The Texas Medication Algorithm Project: Report of the Texas ConsensusConference Panel on medication treatment of major depressive disorder. J Clin Psychiatry. 1999;60142- 156
PubMed Link to Article
Rush  AJRago  WVCrismon  MLToprac  MGShon  SPSuppes  TMiller  ALTrivedi  MHSwann  ACBiggs  MMShores-Wilson  KKashner  TMPigott  TChiles  JAGilbert  DAAltshuler  KZ Medication treatment for the severely and persistently mentally ill:the Texas Medication Algorithm Project. J Clin Psychiatry. 1999;60284- 291
PubMed Link to Article
Rush  AJCrismon  MLToprac  MGShon  SSRago  WVMiller  ALSuppes  TTrivedi  MHBiggs  MMShores-Wilson  KKashner  TMAltshuler  KZ Implementing guidelines and systems of care: Experiences with the TexasMedication Algorithm Project (TMAP). J Pract Psychiatry Behav Health. 1999;575- 86
Adli  MBerghofer  ALinden  MHelmchen  HMuller-Oerlinghausen  BMackert  AStamm  TBauer  M Effectiveness and feasibility of a standardized stepwise drug treatmentregimen algorithm for inpatients with depressive disorders: results of a 2-yearobservational algorithm study. J Clin Psychiatry. 2002;63782- 790
PubMed Link to Article
Katon  WVon Korff  MLin  EWalker  ESimon  GEBush  TRobinson  PRusso  J Collaborative management to achieve treatment guidelines: impact ondepression in primary care. JAMA. 1995;2731026- 1031
PubMed Link to Article
Passik  SDKirsh  KLTheobald  DDonaghy  KHoltsclaw  EEdgerton  SDugan  W Use of a depression screening tool and a fluoxetine-based algorithmto improve the recognition and treatment of depression in cancer patients:a demonstration project. J Pain Symptom Manage. 2002;24318- 327
PubMed Link to Article
Rollman  BLHanusa  BHLowe  HJGilbert  TKapoor  WNSchulberg  HC A randomized trial using computerized decision support to improve treatmentof major depression in primary care. J Gen Intern Med. 2002;17493- 503
PubMed Link to Article
Unutzer  JRubenstein  LKaton  WJTang  LDuan  NLagomasino  ITWells  KB Two-year effects of quality improvement programs on medication managementfor depression. Arch Gen Psychiatry. 2001;58935- 942
PubMed Link to Article
van Os  TWvan den Brink  RHJenner  JAvan der Meer  KTiemens  BGOrmel  J Effects on depression pharmacotherapy of a Dutch general practitionertraining program. J Affect Disord. 2002;71105- 111
PubMed Link to Article
Williams  JW  JrBarrett  JOxman  TFrank  EKaton  WSullivan  MCornell  JSengupta  A Treatment of dysthymia and minor depression in primary care: a randomizedcontrolled trial in older adults. JAMA. 2000;2841519- 1526
PubMed Link to Article
Meredith  LSJackson-Triche  MDuan  NRubenstein  LVCamp  PWells  KB Quality improvement for depression enhances long-term treatment knowledgefor primary care clinicians. J Gen Intern Med. 2000;15868- 877
PubMed Link to Article
Brody  DSDietrich  AJdeGruy  F  IIIKroenke  K The depression in primary care tool kit. Int J Psychiatry Med. 2000;3099- 110
PubMed Link to Article
Banazak  DAMullan  PBGardner  JCRajagopalan  S Practice guidelines and late-life depression assessment in long-termcare. J Gen Intern Med. 1999;14438- 440
PubMed Link to Article
Rollman  BLGilbert  TLowe  HJKapoor  WNSchulberg  HC The electronic medical record: its role in disseminating depressionguidelines in primary care practice. Int J Psychiatry Med. 1999;29267- 286
PubMed Link to Article
Carney  PADietrich  AJEliassen  MSOwen  MBadger  LW Recognizing and managing depression in primary care: a standardizedpatient study. J Fam Pract. 1999;48965- 972
PubMed
Glasser  MGravdal  JA Assessment and treatment of geriatric depression in primary care settings. Arch Fam Med. 1997;6433- 438
PubMed Link to Article
Katon  WRobinson  PVon Korff  MLin  EBush  TLudman  ESimon  GWalker  E A multifaceted intervention to improve treatment of depression in primarycare. Arch Gen Psychiatry. 1996;53924- 932
PubMed Link to Article
Schulberg  HCBlock  MRMadonia  MJRodriguez  EScott  CPLave  J Applicability of clinical pharmacotherapy guidelines for major depressionin primary care settings. Arch Fam Med. 1995;495- 96
PubMed Link to Article
Rush  AJCrismon  MLKashner  TMToprac  MGCarmody  TJTrivedi  MHSuppes  TMiller  ALBiggs  MMShores-Wilson  KWitte  BPShon  SPRago  WVAltshuler  KZTMAP Research Group, Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale andstudy design. J Clin Psychiatry. 2003;64357- 369
PubMed Link to Article
Toprac  MGRush  JAConner  TMCrismon  MLDees  MHopkins  CRowe  VShon  SP The Texas Medication Algorithm Project Patient and Family EducationProgram: a consumer-guided initiative. J Clin Psychiatry. 2000;61477- 486
PubMed Link to Article
Texas Implementation of Medication Algorithms (TIMA), Guidelines for Treating Major Depressive Disorder: TIMA Physician ProceduralManual. Available at:http://www.mhmr.state.tx.us/centraloffice/medicaldirector/timamddman.pdfJuly 23, 2003.
Overall  JEGorham  DR Introduction: the Brief Psychiatric Rating Scale (BPRS): recent developmentsin ascertainment and scaling. Psychopharmacol Bull. 1988;2497- 99
Ventura  JGreen  MFShaner  ALiberman  RP Training and quality assurance with the Brief Psychiatric Rating Scale:"the drift busters”. Int J Methods Psychiatr Res. 1993;3221- 244
Ventura  JNuechterlein  KHSubotnik  KGilbert  E Symptom dimensions in recent-onset schizophrenia: the 24-item expandedBPRS. Schizophr Res. 1995;1522
Link to Article
Stein  LTest  M Alternative to mental hospital treatment, I: conceptual model, treatmentprogram, and clinical evaluation. Arch Gen Psychiatry. 1980;37392- 397
PubMed Link to Article
Rush  AJGullion  CMBasco  MRJarrett  RBTrivedi  MH The inventory of depressive symptomatology (IDS): psychometric properties. Psychol Med. 1996;26477- 486
PubMed Link to Article
Ware  JEKosinski  MKeller  SD A 12-item Short-Form Health Survey (SF-12): construction of scalesand preliminary tests of reliability and validity. Med Care. 1996;34220- 233
PubMed Link to Article
Skinner  HA The Drug Abuse Screening Test. Addict Behav. 1982;7363- 371
PubMed Link to Article
Selzer  MJ The Michigan Alcoholism Screening Test: the quest for a new diagnosticinstrument. Am J Psychiatry. 1971;1271653- 1658
PubMed
Bryk  ASRaudenbush  SW Hierarchical Linear Models: Applications and DataAnalysis Methods. Advanced Quantitative Techniques in the Social Sciences, Volume 1. Newbury Park, Calif SAGE Publications1992;
Kashner  TMCarmody  TJSuppes  TRush  AJCrismon  MLMiller  ALToprac  MTrivedi  MH Catching-up on health outcomes: the Texas Medication Algorithm Project. Health Serv Res. 2003;38311- 331
PubMed Link to Article
Kashner  TMRosenheck  RCampinell  ABSurís  ACrandall  RGarfield  NJLapuc  PPyrcz  KSoyka  TWicker  A Impact of work therapy on health status among homeless, substance dependentveterans: a randomized controlled trial. Arch Gen Psychiatry. 2002;59938- 944
PubMed Link to Article
Bryk  ASRaudenbush  SWCongdon  RT  Jr HLM – Hierarchical Linear Modeling (Version5.0).  St Paul, Minn Assessment Systems Corp2001;
Wells  KBSturm  RSherbourne  CDMeredith  LS Caring for Depression.  Cambridge, Mass Harvard University Press1996;
Katon  WVon Korff  MLin  EUnutzer  JSimon  GWalker  ELudman  EBush  T Population-based care of depression: effective disease management strategiesto decrease prevalence. Gen Hosp Psychiatry. 1997;19169- 178
PubMed Link to Article
Unützer  JRubenstein  LKaton  WTang  LDuan  NLagomasino  ITWells  KB Two-year effects of quality improvement programs on medication managementfor depression. Arch Gen Psychiatry. 2001;58935- 942
PubMed Link to Article
Hunkeler  EMMeresman  JFHargreaves  WAFireman  BBerman  WHKirsch  AJGroebe  JHurt  SWBraden  PGetzell  MFeigenbaum  PAPeng  TSalzer  M Efficacy of nurse telehealth care and peer support in augmenting treatmentof depression in primary care. Arch Fam Med. 2000;9;700- 708
PubMed
Unützer  JKaton  WCallahan  CMWilliams  JW  JrHunkeler  EHarpole  LHoffing  MPenna  RD DellaNoel  PHLin  EHArean  PAHegel  MDTang  LBelin  TROishi  SLangston  C Collaborative care management of late-life depression in the primarycare setting: a randomized controlled trial. JAMA. 2002;2882836- 2845
PubMed Link to Article
Liu  CFHedrick  SCChaney  EFHeagert  PFelker  BHasanberg  NFihn  SKaton  W Cost-effectiveness of collaborative care for depression in a primarycare veteran population. Psychiatr Serv. 2003;54698- 704
PubMed Link to Article
Trivedi  MHKern  JKBaker  SMAltshuler  KZ Computerizing medication algorithms and decision support systems formajor psychiatric disorders. J Psychiatr Pract. 2000;6237- 246
Link to Article
Trivedi  MHKern  JKVoegtle  TMBaker  SMAltshuler  KZ Computerized medication algorithms in behavioral health care. Dewan  NALorenzi  NRiley  RBhattacharya  SRedsBehavioral Healthcare Informatics. New York, NY Springer-Verlag2001;53- 66
Trivedi  MHKern  JKMarcee  AGrannemann  BKleiber  BBettinger  TAltshuler  KZMcClelland  A Development and implementation of computerized clinical guidelines:barriers and solutions. Methods Inf Med. 2002;41435- 442
PubMed
Institute of Medicine Committee on Quality of Health Care in America, Crossing the Quality Chasm: A New Health System forthe 21st Century.  Washington, DC National Academy Press2001;

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