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.
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.
Authors: Konstantinos N Fountoulakis; Lakshmi Yatham; Heinz Grunze; Eduard Vieta; Allan Young; Pierre Blier; Siegfried Kasper; Hans Jurgen Moeller Journal: Int J Neuropsychopharmacol Date: 2017-02-01 Impact factor: 5.176
Authors: Joseph M Cerimele; Simon B Goldberg; Christopher J Miller; Stephen W Gabrielson; John C Fortney Journal: Psychiatr Serv Date: 2019-02-05 Impact factor: 3.084
Authors: Ira H Bernstein; A John Rush; Trisha Suppes; Madhukar H Trivedi; Ada Woo; Yasushi Kyutoku; M Lynn Crismon; Ellen Dennehy; Thomas J Carmody Journal: Int J Methods Psychiatr Res Date: 2009-06 Impact factor: 4.035