Biomedical subjects

Steven P Shon

Publications and source records attributed to Steven P Shon.

7 recordsLinked to original sources

PubMed2004

Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project.

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-C(30)) 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-SR(30)). RESULTS: All patients improved during the study (P<.001), but ALGO patients had significantly greater symptom reduction on both the IDS-C(30) and IDS-SR(30) 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.

Adult↗

PubMed2004

Clinicians' adherence to an algorithm for pharmacotherapy of depression in the Texas public mental health sector.

This study evaluated clinicians' adherence to the major depressive disorder algorithm of the Texas Implementation of Medication Algorithms (TIMA) as a component of usual care in the Texas public mental health system. Data were collected from two Texas Department of Mental Health and Mental Retardation centers between April and December 2000. Clinician adherence measures included documentation of outcome measures, prescribing patterns (correct medications, therapeutic dosing, dosage increases, and appropriate medication changes), and visit frequency. Clinicians had consistently high adherence to appropriate drug regimens, at appropriate dosages. Variability in attempts to increase dosages when warranted, visit frequency, and documentation of patient outcome measures between clinicians were seen. The results suggest that implementation of medication algorithms is possible in the public mental health sector.

Adult↗

PubMed2004

The Texas Medication Algorithm Project antipsychotic algorithm for schizophrenia: 2003 update.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) has been a public-academic collaboration in which guidelines for medication treatment of schizophrenia, bipolar disorder, and major depressive disorder were used in selected public outpatient clinics in Texas. Subsequently, these algorithms were implemented throughout Texas and are being used in other states. Guidelines require updating when significant new evidence emerges; the antipsychotic algorithm for schizophrenia was last updated in 1999. This article reports the recommendations developed in 2002 and 2003 by a group of experts, clinicians, and administrators. METHOD: A conference in January 2002 began the update process. Before the conference, experts in the pharmacologic treatment of schizophrenia, clinicians, and administrators reviewed literature topics and prepared presentations. Topics included ziprasidone's inclusion in the algorithm, the number of antipsychotics tried before clozapine, and the role of first generation antipsychotics. Data were rated according to Agency for Healthcare Research and Quality criteria. After discussing the presentations, conference attendees arrived at consensus recommendations. Consideration of aripiprazole's inclusion was subsequently handled by electronic communications. RESULTS: The antipsychotic algorithm for schizophrenia was updated to include ziprasidone and aripiprazole among the first-line agents. Relative to the prior algorithm, the number of stages before clozapine was reduced. First generation antipsychotics were included but not as first-line choices. For patients refusing or not responding to clozapine and clozapine augmentation, preference was given to trying monotherapy with another antipsychotic before resorting to antipsychotic combinations. CONCLUSION: Consensus on algorithm revisions was achieved, but only further well-controlled research will answer many key questions about sequence and type of medication treatments of schizophrenia.

Algorithms↗

PubMed2003

A quality improvement process for implementing the Texas algorithm for schizophrenia in Ohio.

Medication algorithms developed in Texas are being implemented in a number of states in the United States and internationally. This report describes a quality improvement process adapted from the Texas Medication Algorithm Project that was used to implement the Texas algorithm for schizophrenia in Ohio. A total of 38 physicians were surveyed about their perceptions of barriers to implementation of the guidelines. The physicians generally thought that the schizophrenia algorithm was good, current, and applicable. Although they did not perceive barriers to its implementation, they did not seem to alter their practices to a great extent in response to the algorithm. The results of the study may guide other states in their implementation of algorithms.

Algorithms↗

PubMed2003

Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale and study design.

BACKGROUND: Medication treatment algorithms may improve clinical outcomes, uniformity of treatment, quality of care, and efficiency. However, such benefits have never been evaluated for patients with severe, persistent mental illnesses. This study compared clinical and economic outcomes of an algorithm-driven disease management program (ALGO) with treatment-as-usual (TAU) for adults with DSM-IV schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) treated in public mental health outpatient clinics in Texas. DISCUSSION: The disorder-specific intervention ALGO included a consensually derived and feasibility-tested medication algorithm, a patient/family educational program, ongoing physician training and consultation, a uniform medical documentation system with routine assessment of symptoms and side effects at each clinic visit to guide ALGO implementation, and prompting by on-site clinical coordinators. A total of 19 clinics from 7 local authorities were matched by authority and urban status, such that 4 clinics each offered ALGO for only 1 disorder (SCZ, BD, or MDD). The remaining 7 TAU clinics offered no ALGO and thus served as controls (TAUnonALGO). To determine if ALGO for one disorder impacted care for another disorder within the same clinic ("culture effect"), additional TAU subjects were selected from 4 of the ALGO clinics offering ALGO for another disorder (TAUinALGO). Patient entry occurred over 13 months, beginning March 1998 and concluding with the final active patient visit in April 2000. Research outcomes assessed at baseline and periodically for at least 1 year included (1) symptoms, (2) functioning, (3) cognitive functioning (for SCZ), (4) medication side effects, (5) patient satisfaction, (6) physician satisfaction, (7) quality of life, (8) frequency of contacts with criminal justice and state welfare system, (9) mental health and medical service utilization and cost, and (10) alcohol and substance abuse and supplemental substance use information. Analyses were based on hierarchical linear models designed to test for initial changes and growth in differences between ALGO and TAU patients over time in this matched clinic design.

Adolescent↗

PubMed2003

Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. METHOD: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. RESULTS: ALGO and TAU patients showed significant initial decreases in symptoms (p =.03 and p <.001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. CONCLUSION: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

Adolescent↗

PubMed2002

Report of the Texas Consensus Conference Panel on medication treatment of bipolar disorder 2000.

BACKGROUND: The process and outcome of a consensus conference to develop revised algorithms for treatment of bipolar disorder to be implemented in the public mental health system of Texas are described. These medication algorithms for bipolar disorder are an update of those developed for the Texas Medication Algorithm Project, a research study that tested the clinical and economic impact of treatment guidelines for major psychiatric illnesses treated in the Texas public mental health system (Texas Department of Mental Health and Mental Retardation [TDMHMR]). METHOD: Academic clinicians and researchers, practicing clinicians in the TDMHMR system, administrators, advocates, and consumers participated in a consensus conference in August 2000. Participants attended presentations reviewing new evidence in the pharmacologic treatment of bipolar disorder and discussed the needs of consumers in the TDMHMR system. Principles were enumerated, including balancing of evidence for efficacy, tolerability, and safety in medication choices. A set of 7 distinct algorithms was drafted. In the following months, a subcommittee condensed this product into 2 primary algorithms. RESULTS: The panel agreed to 2 primary algorithms: treatment of mania/hypomania, including 3 pathways for treatment of euphoric symptoms, mixed or dysphoric symptoms, and psychotic symptoms; and treatment of depressive symptoms. General principles to guide algorithm implementation were discussed and drafted. CONCLUSION: The revised algorithms are currently being disseminated and implemented within the Texas public mental health system. The goals of the Texas initiative include increasing the consistency of appropriate treatment of bipolar disorder, encouraging systematic and optimal use of available pharmacotherapies, and improving the outcomes of patients with bipolar disorder.

Algorithms↗