Tenofovir or zidovudine in second-line antiretroviral therapy after stavudine failure in southern Africa

BACKGROUND There is debate over using tenofovir or zidovudine alongside lamivudine in second-line antiretroviral therapy (ART) following stavudine failure. We analyzed outcomes in cohorts from South Africa, Zambia and Zimbabwe METHODS: Patients aged ≥16 years who switched from a first-line regimen including stavudine to a ritonavir-boosted lopinavir-based second-line regimen with lamivudine or emtricitabine and zidovudine or tenofovir in seven ART programs in southern Africa were included. We estimated the causal effect of receiving tenofovir or zidovudine on mortality and virological failure using Cox proportional hazards marginal structural models. Its parameters were estimated using inverse probability of treatment weights. Baseline characteristics were age, sex, calendar year and country. CD4 cell count, creatinine and hemoglobin levels were included as time-dependent confounders. RESULTS 1,256 patients on second-line ART, including 958 on tenofovir, were analyzed. Patients on tenofovir were more likely to have switched to second-line ART in recent years, spent more time on first-line ART (33 vs. 24 months) and had lower CD4 cell counts (172 vs. 341 cells/μl) at initiation of second-line ART. The adjusted hazard ratio comparing tenofovir with zidovudine was 1.00 (95% confidence interval 0.59-1.68) for virologic failure and 1.40 (0.57-3.41) for death. CONCLUSIONS We did not find any difference in treatment outcomes between patients on tenofovir or zidovudine; however, the precision of our estimates was limited. There is an urgent need for randomized trials to inform second-line ART strategies in resource-limited settings.

Ecological study of the predictors of successful management of dyslipidemia in HIV-infected patients on ART: the Swiss HIV Cohort Study

PURPOSE: Antiretroviral therapy (ART) may induce metabolic changes and increase the risk of coronary heart disease (CHD). Based on a health care system approach, we investigated predictors for normalization of dyslipidemia in HIV-infected individuals receiving ART. METHOD: Individuals included in the study were registered in the Swiss HIV Cohort Study (SHCS), had dyslipidemia but were not on lipid-lowering medication, were on potent ART for >or= 3 months, and had >or= 2 follow-up visits. Dyslipidemia was defined as two consecutive total cholesterol (TC) values above recommended levels. Predictors of achieving treatment goals for TC were assessed using Cox models. RESULTS: Analysis included 958 individuals with median followup of 2.3 years (IQR 1.2-4.0). 454 patients (47.4%) achieved TC treatment goals. In adjusted analyses, variables significantly associated with a lower hazard of reaching TC treatment goals were as follows: older age (compared to 18-37 year olds: hazard ratio [HR] 0.62 for 45-52 year olds, 95% CI 0.47-0.82; HR 0.40 for 53-85, 95% CI 0.29-0.54), diabetes (HR 0.39, 95% CI 0.26-0.59), history of coronary heart disease (HR 0.27, 95% CI 0.10-0.71), higher baseline TC (HR 0.78, 95% CI 0.71-0.85), baseline triple nucleoside regimen (HR 0.12 compared to PI-only regimen, 95% CI 0.07-0.21), longer time on PI-only regimen (HR 0.39, 95% CI 0.33-0.46), longer time on NNRTI only regimen (HR 0.35, 95% CI 0.29-0.43), and longer time on PI/NNRTI regimen (HR 0.34, 95% CI 0.26-0.43). Switching ART regimen when viral load was undetectable was associated with a higher hazard of reaching TC treatment goals (HR 1.48, 95% CI 1.14-1.91). CONCLUSION: In SHCS participants on ART, several ART-related and not ART-related epidemiological factors were associated with insufficient control of dyslipidemia. Control of dyslipidemia in ART recipients must be further improved.

Tracing of patients lost to follow-up and HIV transmission: Mathematical modelling study based on two large ART programmes in Malawi

OBJECTIVES: Treatment as prevention depends on retaining HIV-infected patients in care. We investigated the effect on HIV transmission of bringing patients lost to follow up (LTFU) back into care. DESIGN: Mathematical model. METHODS: Stochastic mathematical model of cohorts of 1000 HIV-infected patients on antiretroviral therapy (ART), based on data from two clinics in Lilongwe, Malawi. We calculated cohort viral load (CVL; sum of individual mean viral loads each year) and used a mathematical relationship between viral load and transmission probability to estimate the number of new HIV infections. We simulated four scenarios: 'no LTFU' (all patients stay in care); 'no tracing' (patients LTFU are not traced); 'immediate tracing' (after missed clinic appointment); and, 'delayed tracing' (after six months). RESULTS: About 440 of 1000 patients were LTFU over five years. CVL (million copies/ml per 1000 patients) were 3.7 (95% prediction interval [PrI] 2.9-4.9) for no LTFU, 8.6 (95% PrI 7.3-10.0) for no tracing, 7.7 (95% PrI 6.2-9.1) for immediate, and 8.0 (95% PrI 6.7-9.5) for delayed tracing. Comparing no LTFU with no tracing the number of new infections increased from 33 (95% PrI 29-38) to 54 (95% PrI 47-60) per 1000 patients. Immediate tracing prevented 3.6 (95% PrI -3.3-12.8) and delayed tracing 2.5 (95% PrI -5.8-11.1) new infections per 1000. Immediate tracing was more efficient than delayed tracing: 116 and to 142 tracing efforts, respectively, were needed to prevent one new infection. CONCLUSION: Tracing of patients LTFU enhances the preventive effect of ART, but the number of transmissions prevented is small.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.