OBJECTIVES: We used continuous-time Markov models based on CD4 cell counts and anti-CD3 reactivity (i.e., measure for T-cell quality) to study the progression of HIV infection in a cohort study of homosexual men in Amsterdam. We also compared the effectiveness of anti-CD3 reactivity as a marker for disease progression with that of CD4 cell counts. METHODS: We used data from 467 men (6905 visits) with visits at 3-month intervals between October 1984 and March 1993. To account for measurement error and short time-scale variability, the immunological stage at each visit was determined using a kernel smoother on log-transformed data from each individual. The Markov model had six marker-defined stages and a seventh stage for clinical AIDS. The initial stage-occupation probabilities for seroconverters were used to estimate the incubation time from infection to AIDS. Confidence intervals were calculated using the bootstrap method to account for the effect of smoothing on the variability of our estimates. RESULTS: The CD4 staging scheme estimated the median time from seroconversion to AIDS at 8.3 years [95% confidence interval (CI), 8.1-8.6], and a similar estimate was obtained with the anti-CD3 staging model. The CD4 model predicts that 10.2% (95% CI, 9.9-13.1) will remain AIDS-free 15 years after seroconversion. The mean number of stages visited before AIDS is lower with the CD4 model (7.4; 95% CI, 7.2-7.7) than with the anti-CD3 model (11.3; 95% CI, 10.8-12.0), implying that anti-CD3 predicts progression less well than CD4 cell count. CONCLUSIONS: CD4 lymphocyte counts and anti-CD3 reactivity are each associated with an increased hazard for progression to AIDS. Therefore, men in different CD4-stages (anti-CD3 stages) follow different incubation period distributions to AIDS. However, anti-CD3 predicts progression less well than CD4 cell count. Staged time-continuous Markov models are useful to study immunological markers for HIV disease progression.
OBJECTIVES: We used continuous-time Markov models based on CD4 cell counts and anti-CD3 reactivity (i.e., measure for T-cell quality) to study the progression of HIV infection in a cohort study of homosexual men in Amsterdam. We also compared the effectiveness of anti-CD3 reactivity as a marker for disease progression with that of CD4 cell counts. METHODS: We used data from 467 men (6905 visits) with visits at 3-month intervals between October 1984 and March 1993. To account for measurement error and short time-scale variability, the immunological stage at each visit was determined using a kernel smoother on log-transformed data from each individual. The Markov model had six marker-defined stages and a seventh stage for clinical AIDS. The initial stage-occupation probabilities for seroconverters were used to estimate the incubation time from infection to AIDS. Confidence intervals were calculated using the bootstrap method to account for the effect of smoothing on the variability of our estimates. RESULTS: The CD4 staging scheme estimated the median time from seroconversion to AIDS at 8.3 years [95% confidence interval (CI), 8.1-8.6], and a similar estimate was obtained with the anti-CD3 staging model. The CD4 model predicts that 10.2% (95% CI, 9.9-13.1) will remain AIDS-free 15 years after seroconversion. The mean number of stages visited before AIDS is lower with the CD4 model (7.4; 95% CI, 7.2-7.7) than with the anti-CD3 model (11.3; 95% CI, 10.8-12.0), implying that anti-CD3 predicts progression less well than CD4 cell count. CONCLUSIONS:CD4 lymphocyte counts and anti-CD3 reactivity are each associated with an increased hazard for progression to AIDS. Therefore, men in different CD4-stages (anti-CD3 stages) follow different incubation period distributions to AIDS. However, anti-CD3 predicts progression less well than CD4 cell count. Staged time-continuous Markov models are useful to study immunological markers for HIV disease progression.
Authors: H Houweling; S H Heisterkamp; L G Wiessing; R A Coutinho; J K van Wijngaarden; H J Jager Journal: Eur J Epidemiol Date: 1998-10 Impact factor: 8.082