Covariate random effects on the CD4 count variation during HIV disease progression in women

Abstract

Purpose: To investigate the variation in CD4 count between HIV positive patients due to clinical covariates at each phase of the HIV disease progression. Patients and methods: The Centre for the AIDS Programme of Research in South Africa (CAPRISA) conducted different studies in which female patients were initially enrolled in HIV negative cohorts (phase 1). Seroconverts were further followed-up weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3 to 12 months (phase 3: early infection), quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5). Results: Eighteen out of the 46 CD4 count covariates investigated were significant. Low average CD4 counts at acute and early phase entry improved at a faster rate than entries at higher average CD4 count. During therapy, all the 18 covariates induced significantly different patients' average CD4 counts. The rate of change of CD4 count greatly varied in response to lactate dehydrogenase during the acute phase. Red blood cells increase resulted in the patients' CD4 counts approaching a common higher level during the early phase. During therapy, the already high CD4 counts improved faster than lower ones in response to the red blood cells increase. As the monocytes increased, patients with lower average CD4 counts became worse than those with higher average CD4 counts. Conclusion: Changes in the covariates measurements either induced no variation effects in certain phases or improved the CD4 count at a faster rate for those patients whose average CD4 was already high or worsen the CD4 level which was already low or caused the patients' CD4 counts to approach the same level - higher or lower than the general cohort. The studied covariates induced wide variations in the CD4 count between HIV positive patients during the ART phase.

Keywords: between variation; mixOmics; mixed models; parallel plot; partial least squares; redundant features.

Figure 1

The recruitment of the 237 study participants. The HIV negative cohort screening involved 775 voluntary potential candidates of which 462 were already HIV positive and 313 initially eligible. Of the 313 HIV negative, only 245 were enrolled and the rest excluded for various reasons according to the eligibility criteria. Eventually, 27 out of the 245 seroconverted and enrolled into follow-up care. Seroconverts from other CAPRISA studies (210) were also included in the follow-up care that resulted in a total of 237 patients for this study.

Figure 2

The coefficients of variation (CV). The CVs give information about the spread of the repeated measurements around the mean. The colour codes represent Phase II (blue), Phase III (pink), Phase IV (green) and Phase V (red). Abbreviations: BMI, body mass indixex; bp, blood pressure; ALT_GPT, Alamine Aminotransferase_Glutamate Pyruvate Transaminase; AST_GOT, Aspartate Aminotransferase_Glutamate Oxaloactate Transaminase; LDL, Low density lipoprotein; RDW, red blood cell distribution width; MCHC, mean corpuscular haemoglobin concentration; MCH, mean corpuscular haemoglobin; MCV, mean corpscular volume; Hb, haemoglobin.

Figure 3

Proportion of variation in intercepts and slopes. The fixed effects parameters are identical, and each covariate at a time was allowed to have a random effect. Different variance parameter estimates were obtained for each phase (group) and these were expressed as a percentage of the total variation including the ARMA (1,1) and residuals.

Figure 4

Proportion of variation in intercept and slope covariations. The fixed effects parameters are identical, and each covariate at a time was allowed to have a random effect. Different covariance parameter estimates were obtained for each phase (group) and these were expressed as a percentage of the total variation including the autoregressive of order 1 and moving average of order 1 (ARMA (1,1)) and residuals.