The Texas Children's Medication Algorithm Project: Revision of the Algorithm for Pharmacotherapy of Attention-Deficit/Hyperactivity Disorder
In 1998, the Texas Department of Mental Health and Mental Retardation developed algorithms for medication treatment of attention-deficit/hyperactivity disorder (ADHD). Advances in the psychopharmacology of ADHD and results of a feasibility study of algorithm use in community mental health centers ca...
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Veröffentlicht in: | Journal of the American Academy of Child and Adolescent Psychiatry 2006-06, Vol.45 (6), p.642-657 |
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Zusammenfassung: | In 1998, the Texas Department of Mental Health and Mental Retardation developed algorithms for medication treatment of attention-deficit/hyperactivity disorder (ADHD). Advances in the psychopharmacology of ADHD and results of a feasibility study of algorithm use in community mental health centers caused the algorithm to be modified and updated.
We convened a consensus conference of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families to revise the algorithms for the pharmacotherapy of ADHD itself as well as ADHD with specific comorbid disorders. New research was reviewed by national experts, and rationales were provided for proposed changes and additions to the algorithms. The changes to the algorithms were discussed and approved both by the national experts and experienced clinicians from the Texas public mental health system.
The panel developed consensually agreed-upon algorithms for ADHD with and without comorbid disorders. The major changes included elimination of pemoline as a treatment option, adding atomoxetine to the algorithm, and refining guidelines for treating ADHD with comorbid depression, aggressive behaviors, and tic disorders.
Medication algorithms for ADHD can be modified to keep abreast of developments in the field. Although these evidence- and consensus-based treatment recommendations may be a useful approach to guide the treatment of ADHD in children, additional research is needed to determine how these algorithms can be used to maximally benefit child outcomes. |
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ISSN: | 0890-8567 1527-5418 |
DOI: | 10.1097/01.chi.0000215326.51175.eb |