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.

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.

Risk charts to guide targeted HIV-1 viral load monitoring of ART: development and validation in patients from resource-limited settings.

BACKGROUND HIV-1 RNA viral load (VL) testing is recommended to monitor antiretroviral therapy (ART) but not available in many resource-limited settings. We developed and validated CD4-based risk charts to guide targeted VL testing. METHODS We modeled the probability of virologic failure up to 5 years of ART based on current and baseline CD4 counts, developed decision rules for targeted VL testing of 10%, 20% or 40% of patients in seven cohorts of patients starting ART in South Africa, and plotted cut-offs for VL testing on colour-coded risk charts. We assessed the accuracy of risk chart-guided VL testing to detect virologic failure in validation cohorts from South Africa, Zambia and the Asia-Pacific. FINDINGS 31,450 adult patients were included in the derivation and 25,294 patients in the validation cohorts. Positive predictive values increased with the percentage of patients tested: from 79% (10% tested) to 98% (40% tested) in the South African, from 64% to 93% in the Zambian and from 73% to 96% in the Asia-Pacific cohorts. Corresponding increases in sensitivity were from 35% to 68% in South Africa, from 55% to 82% in Zambia and from 37% to 71% in Asia-Pacific. The area under the receiver-operating curve increased from 0.75 to 0.91 in South Africa, from 0.76 to 0.91 in Zambia and from 0.77 to 0.92 in Asia Pacific. INTERPRETATION CD4-based risk charts with optimal cut-offs for targeted VL testing may be useful to monitor ART in settings where VL capacity is limited.

Implementation and Operational Research: Risk Charts to Guide Targeted HIV-1 Viral Load Monitoring of ART: Development and Validation in Patients From Resource-Limited Settings.

BACKGROUND HIV-1 RNA viral load (VL) testing is recommended to monitor antiretroviral therapy (ART) but not available in many resource-limited settings. We developed and validated CD4-based risk charts to guide targeted VL testing. METHODS We modeled the probability of virologic failure up to 5 years of ART based on current and baseline CD4 counts, developed decision rules for targeted VL testing of 10%, 20%, or 40% of patients in 7 cohorts of patients starting ART in South Africa, and plotted cutoffs for VL testing on colour-coded risk charts. We assessed the accuracy of risk chart-guided VL testing to detect virologic failure in validation cohorts from South Africa, Zambia, and the Asia-Pacific. RESULTS In total, 31,450 adult patients were included in the derivation and 25,294 patients in the validation cohorts. Positive predictive values increased with the percentage of patients tested: from 79% (10% tested) to 98% (40% tested) in the South African cohort, from 64% to 93% in the Zambian cohort, and from 73% to 96% in the Asia-Pacific cohort. Corresponding increases in sensitivity were from 35% to 68% in South Africa, from 55% to 82% in Zambia, and from 37% to 71% in Asia-Pacific. The area under the receiver operating curve increased from 0.75 to 0.91 in South Africa, from 0.76 to 0.91 in Zambia, and from 0.77 to 0.92 in Asia-Pacific. CONCLUSIONS CD4-based risk charts with optimal cutoffs for targeted VL testing maybe useful to monitor ART in settings where VL capacity is limited.