Prophylaxis for human immunodeficiency virus-related Pneumocystis carinii pneumonia: using simulation modeling to inform clinical guidelines.
BACKGROUND: Human immunodeficiency virus (HIV)-infected patients receiving highly active antiretroviral therapy (HAART) have experienced a dramatic decrease in Pneumocystis carinii pneumonia (PCP), necessitating reassessment of clinical guidelines for prophylaxis. METHODS: A simulation model of HIV infection was used to estimate the lifetime costs and quality-adjusted life expectancy (QALE) for alternative CD4 cell count criteria for stopping primary PCP prophylaxis in patients with CD4 cell count increases receiving HAART and alternative agents for second-line PCP prophylaxis in those intolerant of trimethoprim-sulfamethoxazole (TMP/SMX). The target population was a cohort of HIV-infected patients in the United States with initial CD4 cell counts of 350/microL who began PCP prophylaxis after their first measured CD4 lymphocyte count less than 200/microL. Data were from randomized controlled trials and other published literature. RESULTS: For patients with CD4 cell count increases during HAART, waiting to stop prophylaxis until the first observed CD4 cell count was greater than 300/microL prevented 9 additional cases per 1000 patients and cost $9400 per quality-adjusted life year (QALY) gained compared with stopping prophylaxis at 200/microL. For patients intolerant of TMP/SMX, using dapsone increased QALE by 2.7 months and cost $4500 per QALY compared with no prophylaxis. Using atovaquone rather than dapsone provided only 3 days of additional QALE and cost more than $1.5 million per QALY. CONCLUSIONS: Delaying discontinuation of PCP prophylaxis until the first observed CD4 cell count greater than 300/microL is cost-effective and provides an explicit "PCP prophylaxis stopping criterion." In TMP/SMX-intolerant patients, dapsone is more cost-effective than atovaquone.[1]References
- Prophylaxis for human immunodeficiency virus-related Pneumocystis carinii pneumonia: using simulation modeling to inform clinical guidelines. Goldie, S.J., Kaplan, J.E., Losina, E., Weinstein, M.C., Paltiel, A.D., Seage, G.R., Craven, D.E., Kimmel, A.D., Zhang, H., Cohen, C.J., Freedberg, K.A. Arch. Intern. Med. (2002) [Pubmed]
Annotations and hyperlinks in this abstract are from individual authors of WikiGenes or automatically generated by the WikiGenes Data Mining Engine. The abstract is from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.About WikiGenesOpen Access LicencePrivacy PolicyTerms of Useapsburg
