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Influence of Alternative Thresholds for Initiating HIV Treatment on Quality-Adjusted Life Expectancy: A Decision Model

R. Scott Braithwaite, MD, MSc; Mark S. Roberts, MD, MPP; Chung Chou H. Chang, PhD; Matthew Bidwell Goetz, MD; Cynthia L. Gibert, MD, MSc; Maria C. Rodriguez-Barradas, MD; Steven Shechter, PhD; Andrew Schaefer, PhD; Kimberly Nucifora, MS; Robert Koppenhaver, MS; and Amy C. Justice, MD, PhD
[+] Article and Author Information

From Yale University and Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut; University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California; George Washington University and Veterans Affairs Medical Center, Washington, D.C.; Baylor College of Medicine and Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas; and University of British Columbia, Vancouver, British Columbia, Canada.


Grant Support: By the National Institute of Alcohol Abuse and Alcoholism (grants K23 AA14483-01, 2U10 AA13566).

Potential Financial Conflicts of Interest:Consultancies: M.S. Roberts (Archimedes). Grants received: M.S. Roberts (National Institutes of Health).

Requests for Single Reprints: R. Scott Braithwaite, MD, MSc, Yale University, 950 Campbell Avenue, West Haven, CT 06516; e-mail, Ronald.Braithwaite@va.gov.

Current Author Addresses: Drs. Braithwaite and Justice and Ms. Nucifora: Yale University, 950 Campbell Avenue, West Haven, CT 06516.

Drs. Roberts and Chang: University of Pittsburgh, 200 Meyran Avenue, Pittsburgh, PA 15213.

Dr. Goetz: Veterans Affairs Greater Los Angeles Healthcare System, 11301 Wilshire Boulevard, Los Angeles, CA 90073.

Dr. Gilbert: Veterans Affairs Medical Center, 50 Irving Street NW, Washington, DC 20422.

Dr. Rodriguez-Barradas: Veterans Affairs Medical Center, 2002 Holcombe Boulevard, Houston, TX 77030.

Dr. Shechter: University of British Columbia, 2053 Main Mall, Vancouver, British Columbia V6T172, Canada.

Dr. Schaefer: University of Pittsburgh, 3800 Ottawa Street, Pittsburgh, PA 15261.

Mr. Koppenhaver: University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261.

Author Contributions: Conception and design: R.S. Braithwaite, M.S. Roberts, C.L. Gibert, M.C. Rodriguez-Barradas, A. Schaefer, A.C. Justice.

Analysis and interpretation of the data: R.S. Braithwaite, M.S. Roberts, C.C. Chang, M.B. Goetz, C.L. Gibert, M.C. Rodriguez-Barradas, S. Shechter, R. Koppenhaver, A.C. Justice.

Drafting of the article: R.S. Braithwaite, C.C. Chang, C.L. Gibert, A.C. Justice.

Critical revision of the article for important intellectual content: R.S. Braithwaite, M.S. Roberts, M.B. Goetz, C.L. Gibert, M.C. Rodriguez-Barradas, A.C. Justice.

Final approval of the article: R.S. Braithwaite, M.S. Roberts, C.C. Chang, M.B. Goetz, C.L. Gibert, M.C. Rodriguez-Barradas, S. Shechter, A.C. Justice.

Provision of study materials or patients: A.C. Justice.

Statistical expertise: C.C. Chang, R. Koppenhaver, A.C. Justice.

Obtaining of funding: R.S. Braithwaite.

Administrative, technical, or logistic support: K. Nucifora, A.C. Justice.

Collection and assembly of data: K. Nucifora, R. Koppenhaver.


Ann Intern Med. 2008;148(3):178-185. doi:10.7326/0003-4819-148-3-200802050-00004
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We first describe how we quantified the potentially harmful consequences of combination antiretroviral therapy and then discuss how we incorporated these estimates into our HIV simulation.

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Figure 1.
Selected constructs in computer simulation.

Constructs fall into 2 broad categories: genetic characteristics of the HIV strain and clinical characteristics of the patient. Genetic characteristics reflect the acquisition of mutations and affect clinical characteristics by altering the effectiveness of combination antiretroviral therapy (CART). Clinical characteristics affect the probability of dying of HIV-related or non–HIV-related causes. With time, combination antiretroviral therapy may give rise to HIV mutations through selection pressures on viral replication. As resistance accrues, the viral replication rate increases, and this in turn increases the probability that subsequent mutations will develop. Adherence, viral resistance, and other patient characteristics together determine the level of effectiveness of combination therapies, as manifested by changes in CD4 count and viral load. The simulation does not merely extrapolate long-term mortality risks based on short-term mortality data but rather predicts long-term mortality risks on the basis of the duration of the effectiveness of CART and other factors, including toxicity. Although determinants of the effectiveness of combination antiretroviral therapy may be unobserved or rarely observed, they may have a profound effect on survival and quality of life.

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Figure 2.
Cumulative incidence of resistance mutations (top) and combination antiretroviral therapy (CART) regimens (bottom) after 5 years, by treatment initiation threshold and viral load.

Earlier therapy leads to more resistance mutations and to more regimens being used. Resistance mutations are defined as any mutation that may give rise to antiretroviral resistance in any drug class. A change in regimen is defined as a change in 2 or more antiretroviral drugs. We show results for age 40 years; results for other age groups did not differ greatly.

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Appendix Figure 1.
Quantity of therapy-related toxicity required to favor earlier rather than later initiation of combination antiretroviral therapy (CART).

In each bar, as the hazard ratio of therapy-related toxicities increases, the different fills indicate the preferred treatment strategy, stratified by age and viral load. Therapy-related toxicity is manifested by greater non–HIV-related mortality. The upper bound used in our base-case analyses (3.8 × non–HIV-related mortality) favors starting therapy at a CD4 count of 500 cells/mm3 at age 30 years and at age 40 years if the viral load is greater than 30 000 copies/mL. If toxicity were below 2.4 × non–HIV-related mortality, starting therapy at a CD4 count of 500 cells/mm3 would be preferred for all ages and viral loads examined.

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Appendix Figure 2.
Sensitivity analysis of how preferred treatment strategies vary with different parameter assumptions.

The 3 graphs correspond to distinct assumptions about toxicity related to combination antiretroviral treatment (CART): high toxicity, as in our upper-bound base-case analysis (top); moderate toxicity (middle); and low toxicity (bottom). Within each graph, other model parameters are varied across plausible ranges. We analyzed only scenarios in which base-case results favored earlier treatment (age 30 years, all viral loads; age 40 years, viral loads of 100 000 and 300 000 copies/mL) because results favoring later treatment were unlikely to be robust. These analyses suggest that findings in favor of earlier treatment were generally stable, varying little with parameter assumptions. Numbers in parentheses are reference citations. *Reference refers to base-case estimate.

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