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Clinical Results for Patients With Major Depressive Disorder in the Texas Medication Algorithm Project
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
Arch Gen Psychiatry. 2004;61:669-680.
ABSTRACT
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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.
INTRODUCTION
Major depressive disorder (MDD) is a prevalent, serious, debilitating illness that affects 7% to 12% of men and 20% to 25% of women in their lifetime.1-2 The course of MDD is typically chronic or recurrent.3 From 10% to 30% of patients have major depressive episodes that last loner than 2 years, and 20% to 30% have MDD superimposed on dysthymic disorder (double depression).4-6 Major depressive 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 medical conditions (eg, cardiac heart disease,9-11 myocardial infarction,12-14 chronic pain,15 diabetes,16 and asthma).17-18 The direct monetary cost of treatment, combined with the indirect costs from lost productivity, are substantial19-21 and have been estimated to be between $44 and $53 billion per year.22-25
Despite the high prevalence of MDD and the wide availability of effective treatments, undertreatment is common.8, 26-27 The aim of treatment is symptomatic remission and functional recovery28 with continuation treatment to prevent relapse.29-32 Symptomatic improvement (response) is distinguished from remission (ie, minimal or no symptoms), because remission, in contrast to a response with residual symptoms, is associated with better functioning33-34 and a better prognosis.35-39
Most randomized controlled efficacy trials typically have engaged symptomatic volunteers with minimal concurrent psychiatric or general medical illnesses and minimal levels of treatment resistance. Consequently, findings from these studies may not generalize to self-declared patients seen in clinical psychiatric practice. Moreover, few studies define how to treat those with an unsatisfactory clinical response to the initial treatment or compare the benefits of different medication options given sequentially.40 These efficacy trials indicate that approximately 35% of participants achieve remission in 6 to 8 weeks,29 although higher remission rates are found in longer treatment trials.41-42 In the longer term, 10% to 30% of patients who do not respond or enter remission quickly subsequently develop depressive relapses during the ensuing 4 to 6 months despite continued pharmacotherapy.43 Because no one treatment is a panacea, clinicians often use a sequence of treatment steps (either monotherapies or combinations) to increase the likelihood of response or remission. Recent efforts have aimed to define guidelines or algorithms for the application of pharmacotherapeutic options for MDD.44-50 Decision tree–based algorithms hold the promise of increased consistency of treatment across practitioners, which in turn should lead to better clinical outcomes and more efficient use of health care resources. Algorithm-guided treatment provides a basis for improving the quality of treatment in both the public and private sectors.
To our knowledge, the present study is the first controlled trial to evaluate algorithm-based treatment of depression in a public sector population treated by psychiatrists. One open trial51 of the impact of algorithm-driven treatment on symptomatic outcomes in a psychiatric (inpatient) population showed effectiveness for an algorithm in the inpatient setting but lacked a control condition.
A series of studies,51-65 including those conducted by Katon et al,52, 64 have evaluated clinical outcomes following the use of Agency for Health Care Policy and Research–based, guideline-driven treatments (see the "Comment" section). Katon et al52 conducted a randomized controlled trial of a guideline-driven intervention vs usual care in the treatment of patients with major (n = 91) or minor depression (n = 126) in a primary care setting. For major but not for minor depression, the intervention was associated with greater adherence to adequate medication doses and more favorable ratings of antidepressant medications benefit, as well as higher ratings of the quality of care and better symptomatic outcomes. Most other trials conducted among primary care settings evaluated broadly defined guideline-driven principles (eg, Did the patient complete the acute-phase trial or not? Was the recommended visit frequency achieved?).
The Texas Medication Algorithm Project (TMAP) aimed to compare the clinical and economic outcomes achieved with the use of prespecified medication algorithms combined with clinical support and a prespecified patient and family educational package for algorithm-guided treatment (ALGO) with treatment as usual (TAU). To increase the probability of appropriate algorithm implementation, an extensive provider support system with additional personnel funded by research moneys was used.
METHODS
STUDY DESIGN
This study is an effectiveness, intent-to-treat, prospective trial that compares patient outcomes in clinics offering ALGO with matched clinics offering TAU. Clinics were prematched based on mental health and mental retardation authority 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-Report scale [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 4 clinics compared with 6 clinics that offered TAU (TAUnonALGO) and an additional 4 clinics that offered TAU to patients with MDD but also provided ALGO for either schizophrenia or bipolar disorder (TAUinALGO). Physicians from all 14 clinics had access to the same medications. The TAUinALGO clinics were intended to assess the effect of an "algorithm culture" associated with the implementation of any of these algorithms on treatment practices. If no differences between TAU groups were found, they could be combined for comparison with ALGO. Randomizing patients among physicians and clinics would require health care providers to ignore their algorithm training and consultation interventions when treating control patients. To randomize by health care providers within the same clinic risked the "water cooler" effects (ie, algorithm physicians would talk to and affect the practice of TAU physicians). Furthermore, physicians in a clinic typically cross-covered for each other, further limiting feasibility.
The primary aim was to assess whether ALGO produced better clinical outcomes in terms of either an earlier onset and/or a greater overall effect during 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 guidelines for good clinical practice and the Declaration of Helsinki and approved by the institutional review boards at The University of Texas Southwestern Medical Center at Dallas and The University of Texas at Austin. On study entry, symptoms, function, quality of life, side effect severity and burden, and health care service utilization and treatment costs were evaluated at baseline and quarterly for at least a 12-month period for all available participants.
ALGORITHM INTERVENTION
ALGO included 2 consensus-driven, medication management algorithms (one each for psychotic and nonpsychotic forms of MDD)48 and expert consultation (offered on biweekly teleconference) and on-site clinical support from clinical coordinators and a patient and family education program provided by the clinical coordinators.67 This intervention package was intended to optimize pharmacotherapy, thereby enhancing clinical outcomes. Each physician implemented ALGO in close collaboration with a clinical coordinator. A 7-step medication algorithm for nonpsychotic MDD 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 earlier steps including those treatment options with the most evidence and the best risk-benefit ratios.
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Figure 1. Strategies for the treatment of nonpsychotic major depressive disorder. Asterisk indicates consider TCA/VLF if not tried; dagger, lithium, thyroid, buspirone; double dagger, skip if lithium 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 permission from the Texas Department of Mental Health and Mental Retardation and is part of a state-funded project.
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Figure 2. Strategies for the treatment of psychotic 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 Texas Department of Mental Health and Mental Retardation and is part of a state-funded project.
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Multiple tools were used to enhance adherence to the algorithm. A detailed treatment manual was used for initial didactic training and ongoing consultations with clinicians (available at: http://www.mhmr.state.tx.us/centraloffice/medicaldirector/timamddman.pdf).68 The manual identified critical decision points (eg, weeks 4, 6, 8, 10, and 12) for each medication when revisions in treatment strategies or tactics were to be undertaken based on degree of symptom change and side effect burden (Figure 1 and Figure 2).
Symptom severity and side effect burden were routinely monitored at each treatment visit to guide treatment implementation, with the aim of ensuring an adequate duration and dose of medication. Clinical assessments at each visit included a global assessment of symptoms and associated symptoms, IDS-C30 and IDS-SR30, and side effect burden by a 10-point global scale. A standard clinical record form was completed at each clinic visit by those implementing the ALGO intervention. The symptom severity assessments were conducted by clinical coordinators before the physician visits.
Each ALGO patient also received a stepwise education package that provided information about the disease, prognosis, treatment options, and medication side effects. This package encouraged patients to participate in treatment decisions and adhere to the treatment.67
PATIENT SELECTION
Male and female outpatients 18 years or older with a clinical diagnosis of MDD (psychotic or nonpsychotic) were eligible for the study. Patients entered ALGO if their treating physician judged that they required an antidepressant medication change or were starting antidepressant therapy. Entrance into TAU initially used the same criteria. However, because medication changes were made less frequently in TAU, patients were also recruited if their quarterly, routinely administered 24-item Brief Psychiatric Rating Scale (BPRS-24)69-71 total score was higher than the median for that clinic's routine quarterly evaluation of each patient. Once approached, another BPRS-24 interview was conducted. Patients with BPRS-24 total scores no more than 1 SD below enrolled ALGO patient average scores were asked to participate. This procedure ensured a minimal level of symptom severity 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-cued methods was used.
Exclusion criteria were minimal. Patients were excluded if they had schizophrenic, bipolar, or schizoaffective disorder or a primary diagnosis of an obsessive-compulsive or eating disorder (anorexia nervosa or bulimia nervosa). Also excluded were patients who required inpatient hospitalization for detoxification at the time of study entry, received mental retardation services, or participated in an Assertive Community Treatment program.72 Table 1 gives the ethnic composition and characteristics of the participating sites and clinics.
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Table 1. Ethnic Composition and Characteristics of Participating Sites and Clinics
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STUDY PROCEDURES
Study participants provided demographic and medical history at baseline and during outcome assessments every 3 months for at least 12 months. Enrollment for the study occurred throughout 13 months. Research coordinators not blind to treatment assignment but not involved in providing any treatment conducted the research outcome assessments.
RESEARCH ASSESSMENTS
The clinical rating of depressive symptoms by the research coordinators with the IDS-C3073 was the primary outcome. Confirmatory symptom measures included the IDS-SR3073 and the BPRS-24.69-71 Health-related quality of life was assessed using the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12).74 Participants were asked about the burden of side effects from medication during the past month that "bothered or interfered with daily functioning." Respondents were considered to have no significant side effects if they reported "no side effects" or "only mild side effects, not really significant" and to have significant side effects whenever side effects " . . . bothered me, but could tolerate them" or " . . . really bothered me, I either need to change my medication or take something for the side effects" or " . . . was so severe I had to be hospitalized."
Demographic information was obtained from a patient questionnaire administered during the face-to-face baseline interview. Alcohol and other drug use was assessed quarterly using the Drug Abuse Screening Test75 (scores >5 indicate drug abuse) and the Michigan Alcoholism Screening Test76 (scores  5 indicate alcohol abuse). The Patient Perception of Benefits (T.M.K., unpublished data, 2000) is a 10-item, self-report instrument developed for this study, with scores ranging from 0 (belief) to 40 (disbelief) that indicate whether the patient will see improved functioning if he/she gets needed care.
STATISTICAL ANALYSES
Hierarchical linear models77 were adapted to assess the impact of ALGO on clinical outcomes based on declining effects analyses developed for this study by Kashner et al.78-79 Declining effects models are growth curves with dependent outcome variables represented as change scores. Independent variables include dichotomous treatment, time since follow-up began, time x treatment interaction terms, and a constant term. This approach takes into account repeated observations nested within patients; missing observations; varying intervals between follow-up observations; effect sizes that vary with time; heteroscedastic, autocorrelated, and other complex level 1 covariance structures; and continuous, bivariate, or ordinal valued outcome variables. Parameter estimates were computed using HLM version 5 software.80
Estimates were computed separately to assess the impact of treatment on ALGO and TAU patients. These estimates included an initial change in outcomes between baseline and the first 3 months and a growth rate in outcomes during the subsequent 9-month follow-up period. Growth rates were measured in terms of change in outcomes per quarter. To assess differences in the impact of ALGO vs TAU on outcomes, we measured differences in both initial changes and growth rates between ALGO and TAU patients. All estimates were adjusted to reflect baseline differences in starting values (change scores) and baseline characteristics (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 other factors (African American and Hispanic status). Program effects were computed by taking differences between ALGO and TAU with respect to initial changes (initial effect) and growth rates (growth rate effect). The growth rate effect was used to determine if any initial ALGO advantage (initial effect) realized during the first quarter increased, remained constant, or declined during the 9-month follow-up. Declining effects were expected if, following an initial ALGO advantage, TAU patients began to catch up to their ALGO counterparts (see Rush et al66). To adjust for regression to the mean due to baseline differences in reported side effect burden, patients were divided for analyses between those reporting and not reporting significant side effects at baseline.
RESULTS
DEVELOPMENT OF THE ANALYTIC SAMPLE
A total of 634 patients met study entry criteria and signed informed consent. Of these, 21 did not complete the baseline assessment, 62 failed to report for any postbaseline visit, and 4 had such a visit but did not complete at least 1 postbaseline primary outcome (IDS-C30). The remaining 547 evaluable patients completed the primary symptom measure for at least 1 postbaseline visit, including ALGO (n = 181), TAUinALGO (n = 212), and TAUnonALGO (n = 154).
Preliminary analysis revealed that patients attending TAUinALGO clinics were achieving numerically but not statistically larger initial reductions in symptoms following intake than their TAUnonALGO counterparts (IDS-C30 adjusted D = –1.89, SE = 1.62, t1080 = 1.17, P<.24). The larger reductions were expected if clinics that participated in ALGO programs targeting other disorders also improved care for MDD patients. On the other hand, TAUinALGO patients were found to have only slightly lower severity of baseline symptoms (mean IDS-C30 score = 36.0 ± 13.8) than their TAUnonALGO counterparts (mean IDS-C30 score = 37.9 ± 13.2), although the difference was not statistically significant (D = –1.9, t338 = 1.4, P = .18, with equal variances not assumed).
Although change scores prevent factor loading baseline differences onto estimates of effect sizes, issues of regression to the mean remain, leading to upward biases of ALGO effect estimates. Because regression to the mean posed a more serious problem, we constructed a final analytic sample by matching each ALGO patient with the best match (without replacement) from either the TAUinALGO or TAUnonALGO groups with respect to baseline IDS-C30 score ( 2), IDS-SR30 score (10), and whenever possible, length of illness (20 years) independent of and blind to any outcomes. The approach is conservative, because including TAUinALGO patients would likely bias against finding 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 estimates compare with –4.42 ± 1.36 for IDS-C30 (t1320 = 3.24, P = .002) when calculations are based on comparing ALGO (n = 175) with its matched TAU (n = 175) samples. The underlying hierarchical linear model is stable, because the adjusted estimated initial 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 patients from 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 comparable baseline IDS-C30 scores (42.0 ± 13.1 vs 41.7 ± 12.7,  = 0.22, t348 = 0.16, P<.87) and were comparable on most other demographic and health variables (Table 2).
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Table 2. Baseline Characteristics of the Analytic Sample by Treatment Group*
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Although resolving regression to the mean issues was important, the impact 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 = 41.8 vs 33.2; = 8.7; t545 = 7.4; P<.001), poorer mental functioning (SF-12 Mental Health Summary [MHS] score = 27.8 vs 32.5; = –4.7; t527 = 5.3; P<.001), and shorter length of illness (13.2 vs 19.8 years; = –6.6; t533 = 6.0; P<.001) and were slightly younger (41.4 vs 43.7 years; = –2.3; t543 = 2.3; P<.02). No other baseline features distinguished these 2 groups.
Baseline covariates were not statistically significant predictors of change scores. Thus, potential biases introduced by these factors are likely to be small. These analyses were limited to primary outcome measures over all analytic patients and not broken down by baseline symptom scores. This underscores our finding of a difference between ALGO vs TAU on both initial and growth rate effects. Neither family size nor disposable income predicted outcome. Medicaid and other public assistance variables, when included into the existing model, did not improve the exploratory power of the included covariates.
CLINICAL EFFICACY
The percentage of patients available for analyses at 3, 6, 9, and 12 months were 100%, 99.5%, 83.2%, and 75.9%, respectively. As such, retention for the analyzable sample was considered excellent. The efficacy analyses were conducted on the analytic sample of 350 patients (n = 175 each from ALGO and TAU). For the primary outcome measure, both TAU and ALGO groups had significant decreases in IDS-C30 scores during the first 3 months, with continuing reductions during the subsequent 9 months. The initial decline was significantly greater for ALGO than TAU during the first 3 months. This advantage for ALGO over TAU persisted throughout the ensuing 3 quarters (ie, there was no catch-up by TAU) (Figure 3). Table 3 gives changes in IDS-C30 scores in subgroups determined by baseline level of symptom severity.
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Figure 3. Adjusted mean symptoms for all patients according to the 30-item Inventory of Depressive Symptomatology–Clinician-Rated scale (IDS-C30) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU) (N = 350).
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Table 3. Baseline IDS-C30 Score–Adjusted Estimates of Initial Effect, Growth Rate Effect, and Initial Effect x Growth Rate Effect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
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To explore whether ALGO effects varied depending on baseline symptom severity, we further subdivided the sample into (1) very severe, (2) severe, and (3) mild/moderate baseline symptoms by IDS-C30 score defined a priori. These analyses revealed that the effects obtained with ALGO were largely accounted for by patients with severe and very severe baseline IDS-C30 symptoms. The study was powered only to address the comparison between ALGO 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 significantly greater symptom reduction during the first 3 months than was TAU. Both groups continued to improve during the subsequent 9 months, although TAU patients showed 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 symptom severity using the IDS-SR30. The differences between ALGO and TAU were statistically significant for the very severely ill (IDS-SR30 score, 58) and for the severely ill (IDS-SR30 score, 30-57) groups.
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Table 4. Baseline IDS-SR30 Score–Adjusted Estimates of Initial Effect, Growth Rate Effect, and Initial Effect x Growth Rate Effect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
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Figure 4. Adjusted mean symptoms for all patients according to the 30-item Inventory of Depressive Symptomatology–Self-Report scale (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 significant symptomatic improvements after the first quarter, with continued improvement during the subsequent 9 months (data not shown). Mental functioning, as measured by the SF-12 MHS score, improved initially and over time for both the ALGO and TAU groups, although the ALGO group experienced a significantly greater initial improvement, with no discernible catch up for their TAU counterparts (Table 5 and Figure 5). This effect was most profound for those with the lowest baseline SF-12 MHS score. No significant between-group differences were observed for the SF-12 MHS score for either group (data not shown).
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Table 5. Baseline SF-12 MHS Score–Adjusted Estimates of Initial Effect, Growth Rate Effect, and Initial Effect x Growth Rate Effect Differences Between ALGO and TAU Patients With Major Depressive Disorder*
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Figure 5. Adjusted mean function for all patients according to the Mental Health Summary (MHS) score of the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU) (N = 340).
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ALGO patients with significant side effects at baseline (n = 77) tended to 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 differ from 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, 1.02 per quarter; 95% CI, 0.82-1.26; t629 = 0.16; P<.87).
COMMENT
This is the first study, to our knowledge, to assess the short- and longer-term effects of treatment algorithms in the care of psychiatric patients with MDD in the public mental health sector. At baseline, this patient population was characterized by substantial symptom severity, poor daily functioning, significant concurrent general medical conditions, and frequent alcohol and other substance abuse.
The ALGO intervention was associated with statistically and clinically significantly better clinical outcomes than TAU in the primary (and most secondary) efficacy assessments, including IDS-C30, IDS-SR30,and SF-12 MHS scores. The magnitude of the difference between ALGO and TAU was robust (mean IDS-C30 difference = 4.5 points; mean IDS-SR30 difference = 7.5 points). The significant advantage for ALGO was seen in the first quarter, with no evidence that TAU patients caught up with their ALGO counterparts during the ensuing 9-month period. Exploratory analyses suggested that ALGO was superior to TAU in those with greater symptom severity or worse function (SF-12) at baseline. The magnitude of the difference between TAU and ALGO is clinically substantial. By the clinician rating, twice as much (and by the self-report, 3 times as much) symptom reduction occurred in ALGO than in TAU. A 4.4-point difference in IDS-C30 is roughly equivalent to a 3-point difference on the Hamilton Rating Scale for Depression, which is the difference typically found in drug-placebo comparisons, yet here we are comparing 2 active treatments (TAU and ALGO).
Results are generalizable to public sector patients with psychotic or nonpsychotic MDD. This population is characterized by substantial socioeconomic disadvantages, long-standing depressive illness, and likely varying degrees of 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 functional impairment persisted points to the severity, comorbidity, chronicity, or possible treatment resistance in this population. Results also raise the question of whether the outcomes would be different (more robust) if ALGO was used in less severely and chronically ill populations or in different treatment environments. On the other hand, those patients with particularly high health service utilization might accrue even greater benefits than patients with less complicated illnesses.
Furthermore, the study intervention was directed only toward optimizing pharmacotherapy and patient adherence. These results suggest the need to study the effects of a broader-based intervention that would integrate evidence-based psychotherapy with evidence-based pharmacotherapy, as well as changes in the health service provision systems, to enhance physician adherence to evidence-based treatments.
Also, ALGO physicians likely demonstrated varying levels of algorithm adherence. It is possible that the clinical impact would be greater if physician algorithm adherence was monitored more closely and facilitated with real-time feedback provided to clinicians to enhance decision making and algorithm adherence. In future analyses, we will examine whether physician adherence to the algorithms is related to patient outcomes. Physicians' average patient load was not altered during the study and thus could have negatively affected their adherence to the algorithms.
Limitations to the present study include the fact that although the study clinics were matched, the clinics, patients, and physicians were not randomly assigned to the study groups (ie, ALGO or TAU), which may have introduced a bias. The outcome assessors were not blinded to treatment assignment and could have biased the results in favor of the ALGO group. However, self-reports (IDS-SR30, SF-12) corroborated clinician ratings of the benefits of ALGO. In addition, varying degrees of algorithm adherence were accepted.
Despite its limitations, the results of this study have significant implications for the provision of mental health care. Our findings, together with reports of enhanced outcomes reported in primary care66, 81-86 settings with the use of enriched disease management programs, suggest ways to enhance clinical practice and care systems that might improve clinical outcomes and positively affect health care utilization. Evidence to date indicates that care systems and practice procedures that attempt to apply practice guidelines, improve the consistency of care provided, and improve patient adherence appear to provide improved patient outcomes (both depressive symptoms and function).
Future studies need to evaluate how we can ensure more consistent implementation of disease management programs. To accomplish behavioral change, these issues need to be examined at both the practitioner and organizational levels. At the practitioner level, we need to explore mechanisms to increase algorithm adherence, including academic detailing, continuous quality improvement, and computerized decision support systems.87-89 At the organizational level, we need to explore modes that more efficiently implement change and more effectively allow practitioners to provide care. As noted in the Institute of Medicine's report, "Crossing the Quality Chasm: A New Health System for the 21st Century,"90 improving the quality of health care in the United States requires not only changing health care professionals and organizations but also better methods of disseminating information, application of technology, communication systems, and the creation of payment systems that reward positive performance. This is obviously an evolutionary process. We hope that this study provides a step toward additional research to improve practice procedures and to provide care to enhance the outcomes for individuals with depressive disorders.
AUTHOR INFORMATION
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Correspondence: Madhukar H. Trivedi, MD, Department of Psychiatry, University of Texas Southwestern Medical Center, 6363 Forest Park Rd, Suite 1300, Dallas, TX 75235 (madhukar.trivedi{at}utsouthwestern.edu).
Submitted for publication June 11, 2002; final revision received December 31, 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 Wood Johnson Foundation (Princeton, NJ); the Meadows Foundation (Dallas); the Lightner-Sams Foundation (Dallas); the Nanny Hogan Boyd Charitable Trust (Dallas); the Texas Department of Mental Health and Mental Retardation (Austin); the Center for Mental Health Services (Washington, DC); the Department of Veterans Affairs (Washington, DC); Health Services Research and Development Research Career Scientist Award (RCS92-403); the Betty Jo Hay Distinguished Chair in Mental Health and the Rosewood Corporation Chair in Biomedical Science (Dr Rush); the United States Pharmacopoeia Convention, Inc (Rockville, Md); Mental Health Connections, a partnership between Dallas County Mental Health and Mental Retardation and the Department of Psychiatry of the University of Texas Southwestern Medical Center, which receives funding from the Texas State Legislature and the Dallas County Hospital District and the University of Texas at Austin College of Pharmacy; and the Southwestern Drug Corporation Centennial Fellowship in Pharmacy (Dr Crismon). The following pharmaceutical companies provided unrestricted educational grants: Abbott Laboratories (Abbott Park, Ill), AstraZeneca PLC (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), Novartis International 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 of the American College of Neuropsychopharmacology; December 12, 2000; San Juan, Puerto Rico; the annual meeting of the American Psychiatric Association; May 7, 2001; New Orleans, La; the annual meeting of the American 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 this study could not have been accomplished. We also thank the National and Texas Alliance for the Mentally Ill and the National and Texas Depressive and Manic Depressive Association for providing patient and family educational materials. We appreciate greatly the assistance of Gus Sicard, PhD, for his translation of the educational, clinical, and research evaluation materials into Spanish. We thank Karla Starkweather, BJ (TMAP communications director, Texas Department of Mental Health and Mental Retardation, Austin). Most important, we wish to express our gratitude to the personnel at each community mental health center 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 Mental Retardation Center; Mental Health and Mental Retardation Authority of Harris County, 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 support of Fast Word Inc (Dallas) and David Savage and thank Andrew Sedillo, MD, for establishing 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 management team coordinators, Judith Chiles, BSN, Ellen Dennehy, PhD, Ellen Habamacher, BS, and Tracie Key, BSN, RN.
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; College of Pharmacy, The University of Texas at Austin (Dr Crismon); Health Services Research and Development Service Research Career Scientist Program, Department of Veterans Affairs, Dallas (Dr Kashner); Texas Department of Mental Health and Mental Retardation, Austin (Drs Toprac and Shon); and Department of Psychiatry, University of Texas Health Science Center at San Antonio (Dr Miller). Dr Trivedi is a grantee and/or speaker for Abbott Laboratories, Organon Inc (Akzo), Bayer, Bristol-Meyers Squibb Company, Eli Lilly and Company, GlaxoSmithKline, Janssen Pharmaceutica Products, Johnson & Johnson, National Institutes of Mental Health, Mead Johnson and Company, Parke-Davis, Pfizer Inc, Solvay, Wyeth, NARSAD, and Forest Laboratories Inc. Dr Rush is a grantee, consultant, and/or speaker for the Robert Wood Johnson Foundation, National Institutes of Mental Health, 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 and Company, Forest Laboratories Inc, and Janssen Pharmaceutica Products, Pharmacia Pharmaceuticals, McNeil Specialty and Consumer Products, and Pfizer Inc. Dr Suppes is a grantee, consultant, and/or speaker or advisor for Abbott Laboraotires, AstraZeneca, Bristol-Meyers Squibb Company, GlaxoSmithKline, Janssen Pharmaceutica Products, National Institutes of Mental Health, Novartis, Robert Wood Johnson Pharmaceutical Research Institute, Stanley Medical Research Institute, Johnson & Johnson Pharmaceutical Research & Development, Pfizer Inc, Pharmaceutical Research Institute, Ortho McNeil Pharmaceutical Inc, UCB Pharma, and Novartis. Dr Miller is a grantee, consultant, and/or speaker for AstraZeneca, Abbott Laboratories, Bristol-Myers Squibb Company, Eli Lilly and Company, Janssen Pharmaceutica Products, and Pfizer Inc.
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