The use of readily available biomarkers to predict CD4 cell counts in HIV-infected individuals

Moolla, Yusuf; Moolla, Zaheer; Reddy, Tarylee; Magula, Nombulelo

doi:10.1080/20786190.2015.1073895

Cited by 3 publications

(4 citation statements)

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“…Several CD4 + count covariates from different clinical platforms have been investigated in HIV-positive patients. The quest for understanding the behavioural patterns of the CD4 + count covariates has been due to different reasons ranging from their potential use as either cost-effective CD4 + count surrogates [ 2 – 4 ] or predictors [ 5 – 7 ] to pre-treatment assessment and monitoring of therapy in HIV-positive patients [ 8 ]. Such endeavours to keep abreast of the health status of HIV-positive patients in the absence of the CD4 + count were triggered by high costs of the CD4 + count diagnostic devices in the past [ 9 , 10 ], making them not easily accessible to resource-limited settings in the developing world [ 11 ] where the health facilities are usually overburdened [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among other CD4 + count clinical covariates, sodium and calcium levels are affected by dietary conditions [ 35 , 36 ], which can become a potentially important integral part of the HIV treatment process during disease progression. Other blood chemistry components have also been suggested [ 5 , 7 , 33 , 37 – 52 ] including CD4 + count covariates from other clinical platforms such as the full blood count [ 3 , 5 , 53 – 57 ], lipids [ 58 – 60 ], sugar [ 61 – 63 ] and clinical examination measurements [ 2 , 6 , 64 – 71 ]. It then stands to reason that endeavours to deal with the obstacle of expensive CD4 + count diagnostic devices in the past left a long trail of suggested continuous CD4 + count clinical covariates that have potential to be an important integral part of the treatment process during HIV disease progression.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

et al. 2019

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Introduction Past endeavours to deal with the obstacle of expensive Cluster of Difference 4 (CD4 + ) count diagnostics in resource-limited settings have left a long trail of suggested continuous CD4 + count clinical covariates that turned out to be a potentially important integral part of the human immunodeficiency virus (HIV) treatment process during disease progression. However, an evaluation to determine the strongest candidates among these CD4 + count covariates has not been well documented. Methods The Centre for the AIDS Programme of Research in South Africa (CAPRISA) initially enrolled HIV-negative (phase 1) patients into different study cohorts. The patients who seroconverted (237) during follow-up care were enrolled again into a post-HIV infection cohort where they were further followed up with weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3–12 months (phase 3: early infection) and quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5). The CD4 + count and 46 covariates were repeatedly measured at each phase of the HIV disease progression. A multilevel partial least squares approach was applied as a variable reduction technique to determine the strongest CD4 + count covariates. Results Only 18 of the 46 investigated clinical attributes were the strongest CD4 + count covariates and the top 8 were positively and independently associated with the CD4 + count. Besides the confirmatory lymphocytes , these were basophils , albumin , haematocrit , alkaline phosphatase (ALP) , mean corpuscular volume (MCV) , platelets , potassium and monocytes . Overall, electrolytes, proteins and red blood cells were the dominant categories for the strongest covariates. Conclusion Only a few of the many previously suggested continuous CD4 + count clinical covariates showed the potential to become an important integral part of the treatment process. Prolonging the pre-treatment period of the HIV disease progression by effectively incorporating and managing the covariates for long-term influence on the CD4 + cell response has the potential to delay challenges associated with ART side effects. Electronic supplementary material The online version of this article (10.1007/s40121-019-0235-4) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

et al. 2019

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“…Previously, the associations of these covariates with the CD4 count have been analyzed with statistical methods that ranged from Pearson or Spearmen correlation analysis,50,51 sensitivity, specificity and positive prediction,52,53 linear regression54,55 multivariate regression,18 logistic regression,26,45 Chi-Square tests,28,29,56 non-parametric tests,34,39 independent student t -tests,57,58 confidence intervals,40 the analysis of variance59,60 to generalized estimating equations 27. Their limitations include the inability to give the covariates an opportunity to compete in a single multidimensional model to identify the most influential ones and consequently assessing their effects on the CD4 count variation.…”

Section: Introductionmentioning

confidence: 99%

<p>Covariate random effects on the CD4 count variation during HIV disease progression in women</p>

Tinarwo¹,

Zewotir²,

North³

2019

HIV

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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.

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“…Previously suggested covariates of the CD4 + count from different clinical categories include the full blood count [20][21][22][23][24][25][26], lipids [27][28][29], sugar [30][31][32], blood chemistry [21,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], and clinical examination measurements [51][52][53][54][55][56][57][58][59][60].…”

Section: Introductionmentioning

confidence: 99%

Trends and Adaptive Optimal Set Points of CD4⁺ Count Clinical Covariates at Each Phase of the HIV Disease Progression

Tinarwo

Zewotir

North

2020

AIDS Research and Treatment

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In response to invasion by the human immunodeficiency virus (HIV), the self-regulatory immune system attempts to restore the CD4+ count fluctuations. Consequently, many clinical covariates are bound to adapt too, but little is known about their corresponding new optimal set points. It has been reported that there exist few strongest clinical covariates of the CD4+ count. The objective of this study is to harness them for a streamlined application of multidimensional viewing lens (statistical models) to zoom into the behavioural patterns of the adaptive optimal set points. We further postulated that the optimal set points of some of the strongest covariates are possibly controlled by dietary conditions or otherwise to enhance the CD4+ count. This study investigated post-HIV infection (acute to therapy phases) records of 237 patients involving repeated measurements of 17 CD4+ count clinical covariates that were found to be the strongest. The overall trends showed either downwards, upwards, or irregular behaviour. Phase-specific trends were mostly different and unimaginable, with LDH and red blood cells producing the most complex CD4+ count behaviour. The approximate optimal set points for dietary-related covariates were total protein 60–100 g/L (acute phase), <85 g/L (early phase), <75 g/L (established phase), and >85 g/L (ART phase), whilst albumin approx. 30–50 g/L (acute), >45 g/L (early and established), and <37 g/L (ART). Sodium was desirable at approx. <45 mEq/L (acute and early), <132 mEq/L (established), and >134 mEq/L (ART). Overall, desirable approximates were albumin >42 g/L, total protein <75 g/L, and sodium <137 mEq/L. We conclude that the optimal set points of the strongest CD4+ count clinical covariates tended to drift and adapt to either new ranges or overlapped with the known reference ranges to positively influence the CD4+ cell counts. Recommendation for phase-specific CD4+ cell count influence in adaptation to HIV invasion includes monitoring of the strongest covariates related to dietary conditions (sodium, albumin, and total protein), tissue oxygenation (red blood cells and its haematocrit), and hormonal control (LDH and ALP).

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The use of readily available biomarkers to predict CD4 cell counts in HIV-infected individuals

Cited by 3 publications

References 21 publications

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

<p>Covariate random effects on the CD4 count variation during HIV disease progression in women</p>

Trends and Adaptive Optimal Set Points of CD4⁺ Count Clinical Covariates at Each Phase of the HIV Disease Progression

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The use of readily available biomarkers to predict CD4 cell counts in HIV-infected individuals

Cited by 3 publications

References 21 publications

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

<p>Covariate random effects on the CD4 count variation during HIV disease progression in women</p>

Trends and Adaptive Optimal Set Points of CD4+ Count Clinical Covariates at Each Phase of the HIV Disease Progression

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Trends and Adaptive Optimal Set Points of CD4⁺ Count Clinical Covariates at Each Phase of the HIV Disease Progression