Neurocognitive screening tools in HIV/AIDS: comparative performance among patients exposed to antiretroviral therapy.

OBJECTIVE: The aim of the study was to compare the performance of several bedside neuropsychological tools for detection of HIV-associated neurocognitive disorder (HAND) in antiretroviral drug-exposed persons. METHODS: We analysed the relative performance of the HIV Dementia Scale (HDS), International HIV Dementia Scale (IHDS) and the Mini-Mental Status Exam (MMSE) together with neuropsychological tests (Symbol-Digit, Grooved Pegboard and Trail Making) in HIV-1-seronegative subjects (HIV-; n=13) and in HIV-1-seropositive subjects with HAND (HIV+HAND; n=13) and other neurological disorders (HIV+OND; n=20). RESULTS: Established neuropsychological tests consistently showed significantly poorer performance by HIV+HAND subjects compared with the other two groups. Similarly, the mean HDS and IHDS scores were lower in the HIV+HAND group compared with the other two groups (P<0.005) while the mean MMSE score did not show significant differences between the HIV+HAND and HIV+OND groups. Receiver operator characteristics curves generated from these data using predefined cut-off scores revealed that the HDS, IHDS and MMSE displayed corresponding area under the curve values of 0.82, 0.74 and 0.48, respectively (P<0.006). CONCLUSIONS: The present findings indicate that the MMSE is a weak tool for diagnosing HAND in this group of patients but the HDS and IHDS demonstrate better efficiencies, although cut-off values for the HDS require reassessment in the era of effective antiretroviral therapy.

Pathway analysis of microarray data via regression.

Pathway analysis of microarray data evaluates gene expression profiles of a priori defined biological pathways in association with a phenotype of interest. We propose a unified pathway-analysis method that can be used for diverse phenotypes including binary, multiclass, continuous, count, rate, and censored survival phenotypes. The proposed method also allows covariate adjustments and correlation in the phenotype variable that is encountered in longitudinal, cluster-sampled, and paired designs. These are accomplished by combining the regression-based test statistic for each individual gene in a pathway of interest into a pathway-level test statistic. Applications of the proposed method are illustrated with two real pathway-analysis examples: one evaluating relapse-associated gene expression involving a matched-pair binary phenotype in children with acute lymphoblastic leukemia; and the other investigating gene expression in breast cancer tissues in relation to patients' survival (a censored survival phenotype). Implementations for various phenotypes are available in R. Additionally, an Excel Add-in for a user-friendly interface is currently being developed.