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Total lymphocyte count and hemoglobin combined in an algorithm to initiate the use of highly active antiretroviral therapy in resource-limited settings

Spacek, Lisa Aa; Griswold, Michaelb; Quinn, Thomas Ca,c; Moore, Richard Da

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Author Information

From the aDivision of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA; bDepartment of Biostatistics, Johns Hopkins Bloomberg School of Hygiene and Public Health, Baltimore, MD, USA; and cNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.

Correspondence and requests for reprints to: Dr Lisa A. Spacek, Johns Hopkins University, Department of Medicine, 1830 East Monument Street, Room 401, Baltimore, MD 21205, USA. Tel: +1 410 614 0921; fax: +1 410 955 7889; e-mail: lspacek@jhmi.edu

Received: 1 November 2002; revised: 8 January 2003; accepted: 22 January 2003.

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Abstract

Objective: To develop clinical algorithms that improve the sensitivity of surrogate markers to initiate the use of highly active antiretroviral therapy (HAART) in resource-limited settings.

Design: A retrospective evaluation of total lymphocyte counts (TLC) and hemoglobin to predict the CD4 lymphocyte count.

Methods: A total of 3269 members of the Johns Hopkins HIV observational cohort contributed 22 690 paired observations of CD4 lymphocyte counts and TLC. Two methods were used to evaluate the effect of combining TLC and hemoglobin to predict CD4 cell counts below 200 cells/mm3 before the initiation of HAART in 1451 participants; 55.3% of participants had CD4 cell counts below 200 cells/mm3.

Results: TLC below 1200 cells/mm3 and hemoglobin below 12 g/dl significantly predicted CD4 cell counts below 200 cells/mm3. For TLC alone sensitivity was 70.7% and specificity was 81.7%. For both men and women, we chose a TLC lower cutoff point of 1200 cells/mm3, an upper cutoff point of 2000 cells/mm3, and hemoglobin of 12 g/dl. For men, method I generated sensitivity of 78.0% and specificity of 77.5%. Method II improved specificity to 81.8%. For women, method I increased sensitivity to 85.6% and decreased specificity to 64.1%. Method II improved specificity to 81.4%.

Conclusion: TLC below 1200 cells/mm3 were associated with CD4 cell counts below 200 cells/mm3 as in the WHO guidelines, but sensitivity was low. Adding hemoglobin to TLC increased sensitivity, thereby reducing the risk of false-negative results. Our model may serve as a template for the development of algorithms to initiate the use of HAART in resource-limited settings.

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Introduction

On 22 April 2002, the World Health Organization (WHO) issued draft guidelines for the treatment of HIV-infected individuals in resource-limited settings [1]. The intent was to expand access to highly active antiretroviral therapy (HAART) to at least three million people with HIV/AIDS by 2005. In settings in which the absolute CD4 lymphocyte count is unavailable or the cost prohibitive, the guidelines indicate that the total lymphocyte count (TLC) may be used as a surrogate marker for the CD4 lymphocyte count. The WHO draft guidelines for treatment initiation recommend starting HAART in those patients with WHO stage IV disease irrespective of the CD4 lymphocyte count, and those with WHO stages I, II, or III disease with CD4 lymphocyte counts below 200 cells/mm3 in areas where the CD4 lymphocyte count is available. If the CD4 lymphocyte count is unavailable, HAART is recommended in those with WHO stages II or III disease with TLC less than 1200 cells/mm3.

Earlier studies have shown a correlation between TLC and CD4 cell counts in HIV-infected patients in South Africa [2], the United Kingdom [3] and the United States [4,5]. Longitudinal follow-up of a South African cohort showed that TLC and CD4 cell counts were equal predictors of disease progression [6]. Given the greater availability and lower cost of TLC, there is a clear argument to proceed with HIV disease treatment and opportunistic infection prophylaxis based on TLC. However, the correlation between TLC and the CD4 lymphocyte count is not perfect, and using TLC to determine the CD4 cell count may result in misclassification.

The difficulty lies in choosing a cutoff point for TLC that correlates with a CD4 cell count threshold such as 200 cells/mm3 and that reconciles the competing aims of sensitivity and specificity. By decreasing the level of TLC used to predict the CD4 cell count, we can optimize specificity and minimize the number of individuals with a high CD4 cell count who are misclassified as having a low CD4 cell count. However, the lower TLC cutoff point increases the number of individuals with falsely negative results. On the other hand, by increasing the TLC cutoff point we maximize sensitivity and more reliably detect those with a low CD4 cell count.

Anemia is associated with HIV/AIDS and is measured using basic laboratory techniques. The prevalence of severe anemia in HIV-infected patients, as defined by a hemoglobin level of less than 10 g/dl, is as high as 40% in advanced AIDS [7]. Anemia in HIV infection is independently associated with the progression of disease, mortality, and CD4 lymphocyte counts of less than 200 cells/mm3 [8]. Similar to the decline seen in CD4 lymphocyte counts, the accelerated decline in hemoglobin preceding the development of AIDS defines a point at which HAART could be initiated [9]. Furthermore, the initiation of HAART improves the anemia of HIV infection [10].

We propose that combining TLC with basic, readily available laboratory data, such as hemoglobin, in resource-limited settings may improve test sensitivity provided by TLC alone and thereby decrease the risk of false-negative results. In this study, we develop a clinical algorithm using TLC and hemoglobin to predict CD4 lymphocyte count of less than 200 cells/mm3. This model, or similar models, may be used to initiate the use of HAART in resource-limited settings with laboratory tests that are widely available and cost effective.

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Methods

Study population

The Johns Hopkins Clinical Cohort has been described previously [11]. A total of 3269 participants of this observational cohort, followed from January 1995 to June 2001, contributed 22 690 paired observations of CD4 cell counts and TLC.

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Laboratory studies

Complete blood counts were performed on the Sysmex SE 9500. CD4 lymphocyte counts were measured by commercially available flow-cytometric techniques (Becton Dickinson, San Jose, CA, USA).

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Data collection

All laboratory data were obtained in the context of routine clinical practice and were processed at Johns Hopkins Hospital. We evaluated four timepoints over the course of follow-up. These included the baseline value obtained before the initiation of HAART, and those obtained between 1–6 months, 12–24 months, and 24–48 months after the initiation of HAART. If there were multiple measurements within a time interval, we used the measurement closest to the end of the interval.

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Statistical analysis

We first evaluated the correlation between CD4 cell count with TLC for all pairs of data, and then separated the data according to the initiation and duration of HAART. Scatter plots and Spearman correlation coefficients characterized the correlation between TLC and CD4 cell counts. We calculated the predicted CD4 lymphocyte count based on both TLC and hemoglobin. In addition to TLC and hemoglobin, we evaluated sex, race, albumin level, and platelet count as predictors of CD4 cell count. Race, albumin level and platelet count did not significantly contribute.

Second, we dichotomized TLC and hemoglobin on the basis of various cutoff point values, and evaluated how well different combinations predicted a CD4 cell count of less than 200 cell/mm3 before the initiation of HAART. Because hemoglobin levels vary according to sex, we evaluated the cutoff points for hemoglobin for men and women separately. Multiple logistic regression was used to assess statistically significant predictors, adjust for known confounders, and test for effect modifiers.

Third, we employed receiver operating characteristics (ROC) curves to evaluate the sensitivity and specificity of algorithms that combined TLC and hemoglobin levels before the initiation of HAART [12]. We generated separate ROC curves stratified by sex to determine the optimal cutoff points for men and women.

We used four algorithms to evaluate the effect of combining TLC and hemoglobin and to reconcile the competing aims of sensitivity and specificity. These four algorithms included two techniques to categorize TLC and two methods to test for CD4 cell counts less than 200 cells/mm3 (Fig. 1).

Fig. 1
Fig. 1
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The first technique used two TLC cutoff points consisting of an upper and a lower TLC cutoff point. Those with TLC of less than 1200 cells/mm3 were considered positive tests. If TLC was greater than 2000 cells/mm3, the test was considered negative. Those TLC values between the upper and lower limits were then categorized according to the hemoglobin level. We defined a hemoglobin level less than 12 g/dl as a positive test and a hemoglobin level of 12 g/dl or more as a negative test. The second technique included only one TLC cutoff point. If TLC was less than 1200 cells/mm3, the test was considered positive. For TLC values of 1200 cells/mm3 or more, we used a hemoglobin level less than 12 g/dl to define a positive test and a hemoglobin level of 12 g/dl or more to define a negative test.

Method I used TLC followed by hemoglobin levels to predict CD4 cell counts less than 200 cells/mm3. This approach would be applicable in a resource-limited setting, where flow cytometry is not accessible. Method II combined TLC and hemoglobin levels to predict CD4 cell counts less than 200 cells/mm3, and then retested those with a hemoglobin level less than 12 g/dl with flow cytometry. For the purpose of this exercise, the CD4 cell count was considered a perfect test with 100% sensitivity and 100% specificity. Method II would be applicable in settings where flow cytometry is available but expensive.

The fourth element of the analysis examined the change in CD4 cell counts and TLC from the time before the initiation of HAART with each of the three follow-up timepoints: 1–6 months, 6–12 months, and 12–48 months after the initiation of HAART. All analyses were conducted using the SAS 8.02 statistical software package (SAS Institute, Inc., Cary, NC, USA).

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Results

A total of 3269 participants were included in this analysis, contributing 5967 person-years of follow-up. The mean age was 37.5 years with a standard deviation of 8.6 years; 1070 (33%) of participants were women and 2199 (67%) were men; 2553 (78.1%) were African-American, 667 (20.4%) were white (non-Hispanic), and 49 (1.5%) were of other racial origins. For the purpose of race analysis, individuals of other racial origins were categorized as white. Of the 3269 study participants, 1451 (44%) were evaluated before the initiation of HAART. In this subset the mean age was 37.4 with a standard deviation of 8.6 years, 1005 (69%) were men, and 1109 (76%) were African-American. Of these 1451 participants, 55.3% had CD4 cell counts less than 200 cells/mm3, 22.4% had CD4 cell counts between 200 and 350 cells/mm3, 12.8% had CD4 cell counts between 350 and 500 cells/mm3, and 9.6% had CD4 cell counts of 500 cells/mm3 or greater.

Fig. 2 illustrates the correlation between TLC and CD4 cell count in 1451 patients before the initiation of HAART. The graph depicts true positive (39.1%), true negative (36.5%), false positive (8.2%), and false negative (16.2%) results based on WHO draft guidelines of TLC less than 1200 cells/mm3 as a cutoff point to determine a positive test of CD4 cell count less than 200 cells/mm3. The Spearman correlation coefficient calculated for the 1451 observations before HAART was 0.72 (P < 0.0001). The correlation decreased to 0.61 (P < 0.0001) after 1–6 months on HAART, was 0.65 (P < 0.0001) after 6–12 months, and returned to 0.71 (P < 0.0001) after 12–24 months.

Fig. 2
Fig. 2
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Using a single linear regression for observations before the initiation of HAART, the predicted value of CD4 cell count given a TLC of 1200 cells/mm3 was approximately 200 cells/mm3, as in the WHO guidelines (Fig. 3a). The results of the linear regression analysis provided the basis for choosing a TLC of 1200 cells/mm3. In order to construct ROC curves for the further evaluation of alternative TLC cutoff points, we chose one point below and above 1200 cells/mm3 (1000 and 1400 cells/mm3) and one point to complete the ROC curve (1800 cells/mm3). The TLC of 2000 cells/mm3 was chosen as an upper limit because above 2000 cells/mm3 there were proportionately fewer observations that correlated with a CD4 cell count of less than 200 cells/mm3 compared with 200 cells/mm3 or greater.

Fig. 3
Fig. 3
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The mean hemoglobin level before the initiation of HAART was 12.9 g/dl in men and 11.8 g/dl in women. For a predicted CD4 cell count of 200 cells/mm3, the hemoglobin level was approximately 13 g/dl for men and 11 g/dl for women. Using a single linear regression, a hemoglobin level of 12 g/dl was associated with a predicted CD4 cell count of 200 cells/mm3 for men and women combined (Fig. 3b). According to ROC curve analysis, a hemoglobin level of 12 g/dl optimized sensitivity and specificity for both men and women. Further analyses of hemoglobin cutoff points ranging from 10 to 14 g/dl did not improve the results.

Multiple logistic regression analysis showed that TLC less than 1200 cells/mm3, a hemoglobin level less than 12 g/dl, sex, and race were statistically significant predictors of a CD4 cell count of less than 200 cells/mm3. After stratification by sex, TLC less than 1200 cells/mm3 and hemoglobin levels less than 12 g/dl remained statistically significant for both men and women. Race remained significant for men, but was not significant for women.

Table 1 summarizes the sensitivity, specificity, positive and negative predictive value of each of the described algorithms. For both men and women, we chose the TLC lower cutoff point of 1200 cells/mm3, the upper cutoff point of 2000 cells/mm3, and a hemoglobin level of 12 g/dl. Fig. 4 illustrates the ROC curves for men and women. Each curve includes TLC cutoff points of 1000, 1200, 1400, and 1800 cells/mm3. For the TLC cutoff point recommended by WHO of 1200 cells/mm3 to predict a CD4 cell count less than 200 cells/mm3 in men, sensitivity was 70.4% and specificity was 81.8%. Method I, which categorized intermediate TLC results according to hemoglobin levels, generated a sensitivity of 78.0% and a specificity of 77.5%. Method II, which retested results of hemoglobin levels less than 12 g/dl with flow cytometry, improved specificity to 81.8%. The percentage of results retested was 8.6% for one TLC cutoff point and 6.3% for two TLC cutoff points. For women, the WHO cutoff point of 1200 cells/mm3 generated a sensitivity of 71.6% and a specificity of 81.4%. Method I increased sensitivity to 85.6% and decreased specificity to 64.1%. Method II improved specificity to 81.4%. The percentage of results retested was 22.0% for one TLC cutoff point and 15.7% for two TLC cutoff points.

Fig. 4
Fig. 4
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Table 1
Table 1
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Race categories were combined for this final analysis. Although African-American race significantly predicted a CD4 cell count less than 200 cells/mm3, after stratification by sex and adjustment for TLC and hemoglobin levels, the effect of race was limited to men. When methods I and II were conducted separately for African-American men versus white men, differences in sensitivity, specificity, positive and negative predictive values were minor. Because of these findings, our algorithm does not use different cutoff points based on race.

Finally, changes in CD4 cell count were compared with changes in TLC with respect to the duration of HAART. An increase in TLC from baseline ranging from 0 to 500 cells/mm3 was associated with an increase in CD4 cell count for each of three time periods. Median increases were 59, 55 and 58 cells/mm3 for 1–6 months, 6–12 months and 12–24 months, respectively. A decrease in TLC from baseline ranging from 0 to 500 cells/mm3 was associated with a minimal change in CD4 cell count. A TLC decline of 500 cells/mm3 or more was seen with a decrease in CD4 cell count medians of 36, 27, and 43 cells/mm3 for each of the sequential time periods. An increase in TLC thus predicted an increase in the CD4 cell count, a small decrease in TLC predicted minimal change, and only a large decrease in TLC predicted a decrease in the CD4 cell count.

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Discussion

By applying the WHO recommended TLC cutoff point of 1200 cells/mm3 to participants in the Johns Hopkins HIV Cohort before the initiation of HAART, the sensitivity of TLC less than 1200 cells/mm3 to predict a CD4 cell count less than 200 cells/mm3 was 70.4% for men and 71.6% for women. Limited sensitivity results in an underestimation of disease prevalence. The risk of false negative results makes TLC of less than 1200 cell/mm3 a relatively insensitive predictor of the CD4 cell count.

We evaluated basic laboratory tests such as those included in the complete blood count and found that hemoglobin levels in addition to TLC significantly predicted CD4 cell counts of less than 200 cells/mm3. We developed clinical algorithms for the initiation of HAART combining TLC and hemoglobin levels stratified by sex. The algorithms improved the sensitivity of TLC as a surrogate marker of CD4 cell counts. We compared the WHO draft guidelines criteria of TLC less than 1200 cells/mm3 with the combined criteria of TLC less than 1200 cells/mm3 and a hemoglobin level less than 12 g/dl for men and women. According to our data, we recommend evaluating intermediate values of TLC between 1200 and 2000 cells/mm3 with a hemoglobin level of 12 g/dl for both men and women.

Method I with one TLC cutoff point showed that categorizing intermediate values of TLC with hemoglobin levels less than 12 g/dl to predict CD4 cell counts less than 200 cells/mm3 increased sensitivity but decreased specificity; with two TLC cutoff points the reduction in specificity was less. In contrast, method II improved sensitivity without compromising specificity. In method II, intermediate values of TLC were categorized by hemoglobin level then retested using flow cytometry when the hemoglobin level was less than 12 g/dl. The calculation of ROC curves with method II demonstrated that from 6 to 22% of samples required repeat testing using flow cytometry. Whereas method I could be used in resource-limited settings where flow cytometry is inaccessible, method II could be used in settings where access to flow cytometry was available but limited by cost.

In this population, multiple logistic regression demonstrated that after adjustment for TLC and hemoglobin levels, sex remained significantly associated with CD4 cell counts less than 200 cells/mm3. Also, a comparison of ROC curves for men versus women illustrated differences in sensitivity and specificity. Male sex is associated with lower levels of CD4 lymphocytes compared with female sex when evaluated in HIV-seronegative populations [13,14]. These findings support the inclusion of sex in our clinical algorithm to predict CD4 cell counts less than 200 cells/mm3.

We further evaluated the change in CD4 cell count as predicted by a change in TLC over a follow-up period of 24 months of HAART in a subset of the cohort. Response to therapy as indicated by a statistically significant increase in both TLC and CD4 cell count has been documented in HIV infected individuals attending an HIV specialty clinic in Chennai, India [15]. The correlation coefficient (r = 0.74), sensitivity, and specificity were similar to the results of our study.

Because our study was conducted in patients cared for at an urban clinic in the United States, we cannot generalize the conclusions to populations studied in other parts of the world. However, these same techniques could be applied to improve the sensitivity of tests used as alternatives to CD4 lymphocyte measurement. The presence of viral, bacterial and parasitic co-infections will undoubtedly affect both TLC and hemoglobin levels. In Uganda, multiple studies have shown that HIV infection is associated with an increased frequency and severity of clinical episodes of malaria parasitemia [16–18]. The interaction of malaria and HIV infection may profoundly alter the hemoglobin level associated with CD4 cell counts of less than 200 cells/mm3. Similarly, gastrointestinal parasitic infections and iron deficiency as a result of multiparity increase the prevalence of anemia.

Another caveat is that the calculated sensitivity and specificity may improve with the addition of WHO clinical staging. For example, a study conducted in Brazil [19] evaluated the combination of the WHO staging system, TLC, and hematocrit, and found a sensitivity of 95.7% and a specificity of 83.3% to detect CD4 cell counts of 200 cells/mm3. However, as the presence of clinical symptoms may vary from setting to setting, the inclusion of clinical staging may not considerably change sensitivity and specificity. For instance, symptoms alone have been shown to be poor predictors of disease progression and death, with 40.5% of HIV-positive subjects reporting no illness in the 10 months before death in a study conducted in rural Uganda [20].

Further studies must be conducted to evaluate how well inexpensive, surrogate markers predict CD4 lymphocyte counts in other geographical areas. HIV providers in resource-limited settings need inexpensive and reliable tests to provide HIV care to those for whom laboratory tests are prohibitively expensive. This study develops algorithms based on the analysis of laboratory tests from an urban cohort in the United States. Our analysis is meant to serve as a template for the development of algorithms in resource-limited settings. We have shown that TLC can be improved as a surrogate marker of the CD4 cell count with the addition of hemoglobin levels and stratification by sex. Our algorithm, or similar algorithms, may be useful in resource-limited settings for the prediction of CD4 cell counts less than 200 cells/mm3 and the initiation of HAART.

Sponsorship: This study was supported by the National Institute of Allergy and Infectious Diseases T32-AI07451, and the National Institute on Drug Abuse, RO1-DA11602 and K24-DA00432.

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Keywords:

Algorithms; antiretroviral therapy; CD4 cell count; HIV-1; total lymphocyte count

© 2003 Lippincott Williams & Wilkins, Inc.

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