Publications

OBJECTIVE

Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost.

DESIGN

Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement.

METHODS

Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count.

RESULTS

Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05).

CONCLUSIONS

Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.

BACKGROUND

Nonadherence to physician treatment recommendations is an increasingly recognized cause of adverse outcomes and increased health care costs, particularly among patients with cardiovascular disease. Whether patient self-report can provide an accurate assessment of medication adherence in outpatients with stable coronary heart disease is unknown.

METHODS

We prospectively evaluated the risk of cardiovascular events associated with self-reported medication nonadherence in 1015 outpatients with established coronary heart disease from the Heart and Soul Study. We asked participants a single question: "In the past month, how often did you take your medications as the doctor prescribed?" Nonadherence was defined as taking medications as prescribed 75% of the time or less. Cardiovascular events (coronary heart disease death, myocardial infarction, or stroke) were identified by review of medical records during 3.9 years of follow-up. We used Cox proportional hazards analysis to determine the risk of adverse cardiovascular events associated with self-reported medication nonadherence.

RESULTS

Of the 1015 participants, 83 (8.2%) reported nonadherence to their medications, and 146 (14.4%) developed cardiovascular events. Nonadherent participants were more likely than adherent participants to develop cardiovascular events during 3.9 years of follow-up (22.9% vs 13.8%, P = .03). Self-reported nonadherence remained independently predictive of adverse cardiovascular events after adjusting for baseline cardiac disease severity, traditional risk factors, and depressive symptoms (hazards ratio, 2.3; 95% confidence interval, 1.3-4.3; P = .006).

CONCLUSIONS

In outpatients with stable coronary heart disease, self-reported medication nonadherence is associated with a greater than 2-fold increased rate of subsequent cardiovascular events. A single question about medication adherence may be a simple and effective method to identify patients at higher risk for adverse cardiovascular events.