Computerizing medication algorithms and decision support systems for major psychiatric disorders

Midline and right frontal brain function as a physiologic biomarker of remission in major depression

Prior investigations have reported that changes in the prefrontal electroencephalogram (EEG) precede symptom improvement from antidepressant medications, and could serve as a biomarker of treatment outcome in major depressive disorder (MDD). A new physiologically defined region of interest (ROI), overlying the midline and right frontal (MRF) cortical area, was examined here for a relationship between early decreases in theta-band cordance and remission. Subjects were 72 adults with unipolar MDD who had completed placebo-controlled antidepressant treatment trials, with 37 randomized to medication and 35 to placebo. We assessed changes in cordance and absolute and relative power in the MRF region at 48 h, 1 week, and 2 weeks after start of drug, as potential predictors of remission (final score on the 17-item Hamilton Depression Rating Scale of 5 or below. Out of 37 medication-treated subjects, 11 (30%) remitted versus 6 of 35 placebo subjects (17%). Change in MRF cordance 1 and 2 weeks after the beginning of treatment was significantly associated with remission in medication-treated subjects at 1 week, with receiver operating characteristic (ROC) analysis yielding 0.76 area under the curve. Decreases in MRF cordance at 1 week predicted remission with medication with 69% overall accuracy (90% sensitivity; 60% specificity). MRF cordance changes were not associated with remission with placebo. Absolute and relative power did not differentiate groups. These results suggest that remission may be predictable from physiologic measurements after 1 week of treatment, and that this region merits further investigation in the neurobiology of treatment response.

Telephone-based assessments to minimize missing data in longitudinal depression trials: a project IMPACTS study report

PURPOSE

Missing data in clinical efficacy and effectiveness trials continue to be a major threat to the validity of study findings. The purpose of this report is to describe methods developed to ensure completion of outcome assessments with public mental health sector subjects participating in a longitudinal, repeated measures study for the treatment of major depressive disorder. We developed longitudinal assessment procedures that included telephone-based clinician interviews in order to minimize missing data commonly encountered with face-to-face assessment procedures.

METHODS

A pre-planned, multi-step strategy was developed to ensure completeness of data collection. The procedure included obtaining multiple pieces of patient contact information at baseline, careful education of both staff and patients concerning the purpose of assessments, establishing good patient rapport, and finally being flexible and persistent with phone appointments to ensure the completion of telephone-based follow-up assessments. A well-developed administrative and organizational structure was also put in place prior to study implementation.

RESULTS

The assessment completion rate for the primary outcome for 310 of 504 subjects who enrolled and completed 52 weeks (at the time of manuscript) of telephone-based follow-up assessments was 96.8%.

CONCLUSION

By utilizing telephone-based follow-up procedures and adapting our easy-to-use pre-defined multi-step approach, researchers can maximize patient data retention in longitudinal studies.

Maximizing the adequacy of medication treatment in controlled trials and clinical practice: STAR(*)D measurement-based care

The success of well-developed protocols has been limited in real-world practice, where even effective strategies have not been sufficient to meet patient needs in routine clinical care owing to Axis I and III comorbidities. The Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) trial required that antidepressant medication treatment be optimal regarding dose and duration, yet accommodate flexibility to ensure safety given the wide range of comorbid general medical and psychiatric disorders allowed in the trial. The objective of this study was to develop a measurement-based care (MBC) approach and an automated feedback system to ensure adequate and safe antidepressant treatment delivery suitable for both clinical research and routine practice. Ratings of depressive symptom severity and side-effect frequency, intensity, and burden were obtained at each treatment visit using the MBC system that (1) guided medication dose adjustments and treatment duration, (2) documented clinician adherence to treatment recommendations, and (3) provided prompt feedback to clinicians to enhance appropriate treatment decisions. Physician adherence to protocol-specific treatment was monitored based on measured symptoms and side-effect burden, and dose and duration of antidepressant at each critical decision point during the acute phase treatment of major depression. Feedback was provided at the point of care by the clinical coordinators, assisted by Web-based reports following each treatment visit. On the basis of the first treatment step with citalopram, over 85% of treatment encounters had appropriate fidelity to recommendations. Most deviations from treatment recommendations occurred late in treatment and were often justifiable. MBC proved to be feasible and effective in busy primary and psychiatric settings. This approach signals a paradigm shift toward the use of measurement-based clinical decisions, both at the point of care and following each visit, to deliver optimal pharmacotherapy for depression.

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

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

Assessing physicians' use of treatment algorithms: Project IMPACTS study design and rationale

Effective treatments for major depressive disorder have been available for 35 years, yet inadequate pharmacotherapy continues to be widespread leading to suboptimal outcomes. Evidence-based medication algorithms have the potential to bring much-needed improvement in effectiveness of antidepressant treatment in "real-world" clinical settings. Project IMPACTS (Implementation of Algorithms using Computerized Treatment Systems) addresses the critical question of how best to facilitate integration of depression treatment algorithms into routine care. It tests an algorithm implemented through a computerized decision support system using a measurement-based care approach for depression against a paper-and-pencil version of the same algorithm and non-algorithm-based, specialist-delivered usual care. This paper reviews issues related to the Project IMPACTS study rationale, design, and procedures. Patient outcomes include symptom severity, social and work function, and quality of life. The economic impact of treatment is assessed in terms of health care utilization and cost. Data collected on physician behavior include degree of adherence to guidelines and physician attitudes about the perceived utility, ease of use, and self-reported effect of the use of algorithms on workload. Novel features of the design include a two-tiered study enrollment procedure, which initially enroll physicians as subjects, and then following recruitment of physicians, enrollment of subjects takes place based initially on an independent assessment by study staff to determine study eligibility. The study utilizes brief, easy-to-use symptom severity measures that facilitate physician decision making, and it employs a validated, phone-based, follow-up assessment protocol in order to minimize missing data, a problem common in public sector and longitudinal mental health studies. IMPACTS will assess the success of algorithm implementation and subsequent physician adherence using study-developed criteria and related statistical approaches. These new procedures and data points will also allow a more refined assessment of algorithm-driven treatment in the future.