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.
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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