Article

Time to discontinuation and self-discontinuation of olanzapine and risperidone in patients with schizophrenia in a naturalistic outpatient setting.

Mental Illness Research, Education, and Clinical Center, Veterans Integrated Service Network 20, VA Puget Sound Health Care System, American Lake Division, Mental Health Service, Tacoma, WA 98493, USA.
Journal of Clinical Psychopharmacology (Impact Factor: 3.76). 03/2008; 28(1):74-7. DOI: 10.1097/jcp.0b013e3181602cf3
Source: PubMed

ABSTRACT Although efficacy of antipsychotic medications is well documented, their effectiveness in real-world practice is less robust. We examined the effectiveness of olanzapine and risperidone in schizophrenia in a naturalistic setting.
We used an electronic medical records database at a Veterans Affairs Medical Center to conduct a retrospective study of all new outpatient medication trials of olanzapine (n = 221) and risperidone (n = 274) over a 2-year period beginning January 1999 in patients diagnosed with schizophrenia or schizoaffective disorder. We defined medication discontinuation as a switch between the 2 agents (most switches) or self-discontinuation when a patient is without medication supply for longer than 1 month.
Sample mean age (+/-SD) was 48.4 (+/-11.6) years; 91% were men. Discontinuation rates were high (73%), trending lower in olanzapine (70%) than risperidone (76%) (P = 0.12). Median time to discontinuation was 120 days (95% confidence interval [CI], 105-135), longer for olanzapine (150 days; 95% CI, 120-180) than risperidone (90 days; 95% CI, 71-109) (P = 0.04). Self-discontinuation was high (48%), with no significant difference between olanzapine (50%) and risperidone (46%). Switching rate was 25% and more likely to occur in risperidone (30%) than olanzapine (20%) (odds ratio, 1.72; 95% CI, 1.13-2.61).
Effectiveness of antipsychotic medications in schizophrenia may be hampered by high rates of medication self-discontinuation in outpatient practice settings. Time to discontinuation suggests that olanzapine may be more effective than risperidone. Strategies to address causes of poor adherence should be incorporated in medication algorithms to optimize their effectiveness.

0 Followers
 · 
48 Views
  • European Neuropsychopharmacology 08/2008; 18. DOI:10.1016/S0924-977X(08)70921-0 · 5.40 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Non-adherence to medication is highly prevalent in patients with schizophrenia. Adherence therapy aims to improve medication adherence of these patients by applying techniques of cognitive behavioural therapy, psycho-education, and motivational interviewing. Even though adherence therapy is frequently discussed and researched, its effectiveness is still uncertain. This paper aims to review the effectiveness of adherence therapy on the medication adherence of patients with schizophrenia. To this end, six electronic databases were systematically searched for randomized, controlled trials on adherence therapy from January 2002 to March 2013. Four trials met the inclusion criteria and were incorporated into the review. The findings suggest that adherence therapy does not improve patients' medication adherence in comparison to treatment as usual or a control intervention. However, all the studies reviewed showed high-adherence ratings at baseline. Thus, further well-designed studies that target adherence therapy to patients who are non-adherent to their medication are needed for a more profound understanding of its effectiveness. In addition, if adherence therapy is aimed not only at improving medication adherence, but also to reach an agreement whereby the patient's decision not to take his medication is accepted, the shared decision-making process needs to be assessed as well.
    International journal of mental health nursing 10/2014; 23(6):490-497. DOI:10.1111/inm.12089 · 2.01 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A color classification method that partitions color image data into a set of uniform color regions is described. The ability to classify spatial regions of the measured image into a small number of uniform regions can be useful for several problems, including image segmentation and image representation. The input image data are first mapped from device coordinates into all approximately uniform perceptual color space. Colors are classified by means of cluster detection in the uniform color space. The classification process is composed of two stages of basic classification and reclassification. The basic classification is based on histogram analysis to detect color clusters sequentially. The principal components of the color data are extracted for effective discrimination of clusters. At the reclassification stage, the extracted representative colors are reclassified on a color distance. Experimental results show that a fundamental set of colors composing an image with shades and shadows is extracted at the basic classification stage and that the objects in the original image are extracted at the reclassification stage
    Pattern Recognition, 1990. Proceedings., 10th International Conference on; 07/1990