JAIDS Journal of Acquired Immune Deficiency Syndromes:
Complete Blood Cell Count as a Surrogate CD4 Cell Marker for HIV Monitoring in Resource-Limited Settings
Chen, Ray Y MD*†; Westfall, Andrew O MS‡; Hardin, J Michael PhD‡; Miller-Hardwick, Cassandra BSN†; Stringer, Jeffrey S. A MD§; Raper, James L DSN, CRNP, JD*; Vermund, Sten H MD, PhD*∥; Gotuzzo, Eduardo MD¶; Allison, Jeroan MD*; Saag, Michael S MD*
Background: A total lymphocyte count (TLC) of 1200 cells/mL has been used as a surrogate for a CD4 count of 200 cells/μL in resource-limited settings with varying results. We developed a more effective method based on a decision tree algorithm to classify subjects.
Methods: A decision tree was used to develop models with the variables TLC, hemoglobin, platelet count, gender, body mass index, and antiretroviral treatment status of subjects from the University of Alabama at Birmingham (UAB) observational database. Models were validated on data from the Birmingham Veterans Affairs Medical Center (BVAMC) and Zambia, with primary decision trees also generated from these data.
Results: A total of 1189 patients from the UAB observational database were included. The UAB decision tree classified a CD4 count ≤200 cells/μL as better than a TLC cut-point of 1200 cells/mL, based on the area under the curve of the receiver-operator characteristic curve (P < 0.0001). When applied to data from the BVAMC and Zambia, the UAB-based decision tree performed better than the TLC cut-point of 1200 cells/mL (BVAMC: P < 0.0001; Zambia: P = 0.0009) but worse than a decision tree based on local data (BVAMC: P ≤ 0.0001; Zambia: P ≤ 0.0001).
Conclusion: A decision tree algorithm based on local data identifies low CD4 cell counts better than one developed from a different population or a TLC cut-point of 1200 cells/mL.
As highly active antiretroviral therapy becomes more accessible and affordable in developing countries through pharmaceutic company price reductions, generic drugs, and programs such as the Global Fund to Fight AIDS, Tuberculosis, and Malaria and the President's Emergency Plan for AIDS Relief, the cost of monitoring HIV therapy may become more prohibitive than the cost of the medications themselves.1,2 The World Health Organization (WHO) recommends using a total lymphocyte count (TLC) of 1200 cells/mL as a surrogate marker for a CD4+ count of 200 cells/μL for treatment initiation when CD4+ cell counts are unavailable.3 Many studies have evaluated the use of TLC as a surrogate marker for CD4+ cell count with mixed results.4,5 Some studies have found a good correlation,6-17 but others have not.18-22 In addition to low lymphocyte count, anemia,23 thrombocytopenia,24 and body mass index (BMI)25,26 have been associated with advanced HIV infection. In an effort to improve the TLC model, other studies have added such parameters.27-31
In this study, we hypothesized that a decision tree algorithm based on multiple components of a complete blood cell count (CBC) and other easily obtainable variables would be more effective in identifying patients with a CD4+ count ≤200 cells/μL than a decision rule based solely on a TLC cut-point of 1200 cells/mL. Furthermore, we examined the transportability of decision tree models across populations.
The University of Alabama at Birmingham (UAB) Outpatient HIV Clinic began collecting information on all patients in an ongoing observational database in January 1994. Trained medical records personnel use standardized procedures to collect clinical and treatment data from medical records daily. Laboratory data are downloaded from the hospital laboratory system directly into the database. Outside laboratory values are entered into the database manually. The UAB Institutional Review Board (IRB) has approved the database protocol. Patients were included in this study if they had a complete set of laboratory data (CD4+ cell count, TLC, hemoglobin, and platelet count) drawn on the same day. For patients with more than 1 set of complete laboratory data, 1 set was randomly selected for this analysis.
SAS Enterprise Miner (version 5.2; SAS Institute, Cary, NC) software was used to develop a decision tree model to discriminate a CD4+ cell count ≤200/μL. The variables used to develop the decision tree were limited to a minimal number of easily obtainable variables to make the model specific yet still broadly applicable. The variables included in this analysis were TLC, hemoglobin, platelet count, gender, BMI, and any antiretroviral therapy in the previous 30 days (yes/no). In accordance with standard practices, the data were randomly split into training and validation data sets. The random samples were stratified on the outcome variable to ensure comparability with respect to classification error rates. Seventy percent of the data were allocated to the training sample and the remaining 30% to the validation sample. The decision tree algorithms used were the default Enterprise Miner algorithm, the classification and regression tree (CART) algorithm, and the χ2 automatic interaction detector (CHAID). The CART32 algorithm uses a recursive partitioning Gini reduction strategy, whereas the CHAID33 uses χ2 tests to determine the best cut-points to use in developing the decision tree.
To determine the best algorithm, receiver-operator characteristic (ROC) curves were generated from the decision trees by varying the cutoffs, 1-by-1, for predicting a CD4 count ≤200 cell/mL. The first point on the ROC curve occurs when none of the nodes predicts a CD4 count ≤200 cells/mL [point (0,0); sensitivity 0%, 1-specificity 0%]. Each subsequent point on the ROC curve is determined by sequentially moving 1 terminal node to the predict ≤200-cells/mL group, gradually increasing sensitivity and 1-specificity until all nodes predict a CD4 count ≤200 cells/mL [point (1,1); sensitivity 100%, 1-specificity 100%]. The best algorithm, based on the largest area under the curve (AUC) of the ROC curve, was used to develop the decision tree model for this study.
The decision tree developed with UAB data was then used to discriminate a CD4+ count ≤200 cells/μL on data from the Birmingham Veterans Affairs Medical Center (BVAMC) and Lusaka, Zambia, after receiving appropriate IRB approvals. Separate decision trees were also developed using the BVAMC and Zambian data directly to determine if a “custom-designed” decision tree classified better than a tree developed using data from a different (UAB) population. Because of the smaller sample size of the BVAMC and Zambian data sets, each data set was used in its entirety to develop the decision tree model rather than splitting into training and validation samples.
When 2 or more ROC curves are constructed based on the same individuals, statistical analysis on differences between curves must account for the correlated nature of the data. A nonparametric approach was used for the analysis of areas under correlated ROC curves using the theory of generalized U-statistics to generate an estimated covariance matrix.34 For each site, a nonparametric comparison of the correlated AUCs was performed using the ROC macro available on the SAS Institute Web site.35 The ROC macro performs statistical tests of the equality of all areas and pairwise comparisons among the curves, along with point and confidence interval estimates.
A total of 1189 patients from the UAB database had a CD4+ cell count and CBC drawn on the same day. The baseline characteristics of these patients are listed in Table 1. The decision tree developed using the CART algorithm is shown in Figure 1. Using data from a CBC, one can follow the decision tree down step-by-step to a terminal node and classification as a CD4+ count <200 cells/μL or >200 cells/μL. A TLC of 1326 cells/mL is the first discriminating point with those above or below, going down separate sides of the tree. Each of the subsequent decision points is likewise based on one of the CBC laboratory values until a terminal node is reached. Each terminal node can be classified as greater than or less than a CD4 count of 200 cells/μL depending on how well that node identifies subjects and on unique local conditions, which determine whether test sensitivity or specificity is more important to emphasize. For this study, nodes are classified based on the best ROC curve, as defined by the curve with the largest AUC. Subjects ending up in a gray node (nodes 1-4, 6, and 7) are therefore classified as having a CD4 count ≤200 cells/μL. Although some of the terminal nodes with fewer subjects (eg, nodes 4 and 7) do not classify subjects extremely accurately, the UAB CART ROC curve as a whole (AUC = 0.888) correctly classified CD4+ cell count significantly better than the TLC cut-point of 1200 cells/mL (AUC = 0.806; P < 0.0001; Table 2; Fig. 2A). Of note, the variables gender, BMI, and any antiretroviral therapy in the last 30 days (yes/no) were not discriminative of CD4+ cell count.
The decision tree was then validated with data from 2 different populations. From the BVAMC, 512 sets of laboratory data (TLC, hemoglobin, platelet count, and CD4+ cell count) were obtained from 204 HIV-infected patients, with a median CD4+ count of 297 cells/μL (Table 3). Based on the UAB CART decision tree, the ROC curve generated had a significantly better AUC than the TLC cut-point of 1200 cells/mL (0.802 vs. 0.723, respectively; P < 0.0001). A CART decision tree developed directly from BVAMC data classified significantly better than the UAB CART decision tree, however, with an AUC of 0.886 (P < 0.0001; see Table 2; see Fig. 2B).
From Lusaka, Zambia, laboratory data were obtained from 596 HIV-infected women participating in a contraceptive clinical trial. The median CD4+ count for these women was 471 cells/μL (see Table 3). Based on the UAB CART decision tree, the ROC curve generated had a significantly better AUC than the TLC cut-point of 1200 cells/mL (0.714 vs. 0.623, respectively; P = 0.0009). As with the BVAMC data, however, a CHAID decision tree developed directly from Zambian data classified significantly better than the UAB CART decision tree, with an AUC of 0.841 (P < 0.0001; see Table 2; see Fig. 2C).
In this study, we used a decision tree analysis to model whether the variables TLC, hemoglobin, platelet count, gender, BMI, and any antiretroviral therapy within the previous 30 days (yes/no) identified CD4 counts ≤200/μL better than the TLC cut-point of 1200 cells/mL. Our model emphasizes the use of inexpensive, easily obtained variables that are relevant whether or not the patient is receiving antiretroviral medications. The variables TLC, hemoglobin, and platelet count were significant, and the UAB decision tree generated demonstrated an AUC significantly better than that of the TLC cut-point of 1200 cells/mL. When applied to data from other populations, although the UAB decision tree continued to perform significantly better than the TLC cut-point of 1200 cells/mL, a decision tree developed specifically from data from that population was clearly superior to both.
Decision trees have been used successfully to identify outcomes in medicine36-38 but have not been used previously to classify CD4+ cell counts. The primary benefit of using decision trees in rural settings is the ease with which they can be applied. Using the data from a patient's CBC to determine how to follow the tree down to the terminal node, any health care worker from a developing country can use the tree to classify a patient's CD4 status, and thus to make decisions about treatment with minimal training. In our study, using a decision tree algorithm incorporating TLC, hemoglobin, and platelet count to identify low CD4+ cell counts was more effective than using the TLC cut-point of 1200 cells/mL in any single population.
When applied to a different population, however, both algorithms were inferior, based on the AUC, to a decision tree developed specifically from local data (see Table 2; see Fig. 2). The reasons why local data provide the best results may be related to the differences in baseline laboratory parameters. People in developing countries may have different “normal” baseline laboratory values because of local genetic, environmental, infectious, or nutritional factors that affect immunologic and hematologic parameters.39-43 If, for this reason, locally developed decision trees determine low CD4+ cell counts better for local populations, the same may apply to the WHO-recommended TLC cut-point of 1200 cells/mL. Our data support this with fairly different test characteristics between Birmingham and Lusaka for the TLC cut-point of 1200 cells/mL (see Table 2). This may also help to explain the differing results reported in the literature as to how well TLC classifies CD4+ cell count.4,5 Additional studies need to be done to confirm whether or not local models should be developed for different populations.
If a developing country site has additional resources, the local decision tree developed can be just a single step in a treatment algorithm. The Zambian decision tree, for example, has a 97% negative predictive value (NPV) but only a 22% positive predictive value (PPV; see Table 2). With such a high NPV, practitioners can be confident that patients who test “negative” (ie, have a CD4+ count >200 cells/μL) actually have a CD4+ count >200 cells/μL. With a low PPV, however, most patients who test “positive” (ie, have a CD4+ count ≤200 cells/μL) actually have a CD4+ count >200 cells/μL. Thus, patients who end up in a positive terminal node with a low PPV might be selected to receive further testing with a CD4+ cell count if resources for only a limited number of CD4+ cell count tests are available. For the Zambian data, 223 (37.4%) of 596 subjects tested positive, reducing the need for CD4+ cell counts by more than 60%. This proportion would obviously be different for different populations, and treatment algorithms would need to be tailored to local situations.
Our study demonstrates that the discriminative ability of a decision tree model based on TLC, hemoglobin, and platelet count is significantly better than the WHO-recommended TLC cut-point of 1200 cells/mL. Furthermore, because the discriminative ability of both methods varies by population, a locally developed decision tree best identifies low CD4+ cell counts. One limitation of our study is that we only examined 2 algorithms with 3 data sets. Whether or not a different algorithm based on 1 data set can be successfully applied to another population remains to be determined. At least 1 other study, however, found that a given model is not always applicable in another population.44 If other studies confirm our result that the best model to identify a low CD4+ cell count is one based on local data, an emphasis should be placed on encouraging local data analysis based on local treatment factors and priorities rather than on applying a single universal algorithm. In addition, continued progress must be made in identifying CD4+ cell count assays that are affordable in resource-limited settings.5,45
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AIDS; CD4+ cell count; complete blood cell count; decision tree; HIV; total lymphocyte count
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