Impact of lipoatrophy on quality of life in HIV patients receiving anti-retroviral therapy1

Metabolic and morphological side-effects occur in HIV-infected individuals receiving anti-retroviral treatment (ART). Peripheral fat loss that occurs particularly in the face, limbs and/or buttocks is referred to as lipoatrophy and has been found to be highly stigmatizing and to adversely impact the health-related quality of life (HRQL). Consumer Health Sciences Survey data collected between November 2003 and January 2006 were utilized to evaluate the impact of lipoatrophy on the HRQL in HIV-infected individuals receiving ART. This was evaluated using analysis of variance with item scores and mental component summary (MCS) and physical component summary (PCS) scores from the Medical Outcomes Trust questionnaire, SF-8 as dependent variables and lipoatrophy as the independent variable controlling for baseline age, sex and ethnicity. Clinical meaningfulness (mean difference divided by population standard deviation, δ/σ) of differences between the groups with and without lipoatrophy was also evaluated. A cohort of 1124 subjects with at least six months of ART was selected based on the availability of data on whether or not lipoatrophy was present. Subjects were primarily male (80%), between the ages of 30 and 60 years (90%), Hispanic (37%) and about 25% each of African American and White. Overall, prevalence of lipoatrophy in this cohort of HIV patients was 18.9%. Statistically significant (p < 0.001) differences in quality of life (as measured by SF-8 individual item scores and MCS and PCS scores) were observed between the two groups. The differences between the groups in item and summary scores were clinically meaningful in the small to near medium range (0.28–0.43). HIV-infected patients already experience a considerable deficiency in HRQL compared to general population; this study demonstrates that lipoatrophy further enhances that negative impact on HRQL

Economic modeling of the combined effects of HIV-disease, cholesterol and lipoatrophy based on ACTG 5142 trial data

<p>Abstract</p> <p>Background</p> <p>This study examines the cost and consequences of initiating an ARV regimen including Lopinavir/ritonavir (LPV/r) or Efavirenz (EFV), using data from a recent clinical trial in a previously published model of HIV-disease.</p> <p>Methods</p> <p>We populated the Markov model of HIV-disease with data from ACTG 5142 study to estimate the economic outcomes of starting ARV therapy with a PI-containing regimen as compared to an NNRTI-containing regimen, given their virologic and immunologic efficacy and effects on cholesterol and lipoatrophy. CNS toxicities and GI tolerability were not included in the model because of their transient nature or low cost remedies, and therefore lack of economic impact. CD4+ T-cell counts and the HIV-1 RNA (viral load) values from the study were used to assign a specific health state (HS) to each patient for each quarter year. The resulting frequencies used as "raw" data directly into the model obviate the reliance on statistical tests, and allow the model to reflect actual patient behavior in the clinical trial. An HS just below the last observed HS was used to replace a missing value.</p> <p>Results</p> <p>The modeled estimates (undiscounted) for the LPV/r-based regimen resulted in 1.41 quality-adjusted life months (QALMs) gained over a lifetime compared to the EFV-based regimen. The LPV/r-based regimen incurred $7,458 (1.8$88,829/QALY. Most of the higher costs accrue before the 7th year of treatment and were offset by subsequent savings. The estimates are highly sensitive to the effect of lipoatrophy on Health-related Quality of Life (HRQOL), but not to the effect of cholesterol levels.</p> <p>Conclusions</p> <p>The cost effectiveness of ARV regimens may be strongly affected by enduring AEs, such as lipoatrophy. It is important to consider specific AE effects from all drugs in a regimen when ARVs are compared.</p> <p>Trial registration</p> <p>(ClinicalTrials.gov number, <a href="http://www.clinicaltrials.gov/ct2/show/NCT00050895">NCT00050895</a><url>http://[ClinicalTrials.gov]</url>).</p

Economics of Switching to Second-Line Antiretroviral Therapy with Lopinavir/Ritonavir in Africa: Estimates Based on DART Trial Results and Costs for Uganda and Kenya

AbstractBackgroundSubstantial immunological improvement has been reported for HIV-infected patients who switch from a failing regimen to a protease inhibitor regimen with Lopinavir/ritonavir (LPV/r). We use decision analysis modeling to estimate health and economic consequences expected from this switch.MethodsA Markov model combined best evidence for CD4+ T-cell response, infectious disease events, death rates, and quality of life for African populations with Kenyan and Ugandan data on drug and medical care costs. We estimate the incremental cost-effectiveness ratio of switching to an LPV/r-based regimen versus remaining on a failed first antiretroviral (ARV) regimen or discontinuing all ARV drugs. The model assumes concurrent use of cotrimoxazole, and 4% annual loss to follow-up. Local effects due to prevalence of malaria and tuberculosis are included in the model. Sensitivity analysis examines the effects of varying disease, ARV therapy and CD4+ T-cell cost, and ART discontinuation assumptions.ResultsThe base model estimates an improvement of 20 months in average survival for the LPV/r group. The respective LPV/r ICER for Kenya is $1483 per quality-adjusted life year (QALY) compared to$1673/QALY for Uganda. The ICERs increase to $1517 and$1707, respectively, if CD4+ T-cell tests cost $25. The model comparing switching to LPV/r to discontinuing all ARV drugs decreases both costs and benefits proportionally for the treatment groups.ConclusionThe estimates are clearly below the most stringent World Health Organization benchmark for cost-effectiveness for Kenya and within the acceptable range of cost-effectiveness for Uganda. Thus, the switch to second-line therapy with LPV/r in these countries appears to be a cost-effective use of resources

Morphology and chemical composition of dentin in permanent first molars with the diagnose MIH

The purpose of this investigation was to study the morphology and distribution of some inorganic elements in dentin in first permanent molars from children with Molar-Incisor Hypomineralization (MIH). Sixty four tooth sections from thirty two children were examined in polarized light. Fifteen representative sections were selected for SEM/XRMA analysis; 5 were used for SEM analysis and 10 for XRMA analysis. No morphological changes in the dentin were revealed in polarized light microscopy (PLM). However, in all but two sections interglobular dentin was found. The SEM analyzes confirmed the findings of the PLM with no structural changes to be found in the dentin. The XRMA results showed a difference in the concentration of elements between dentin below normal and dentin below carious or hypomineralized enamel. Elements related to organic matter appeared with higher values in dentin below hypomineralized and carious enamel. The morphological and chemical findings in dentin below hypomineralized enamel imply that the odontoblasts are not affected in cases of MIH, but may be affected by hypocalcemia, reflected by the presence of interglobular dentin

Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV

BackgroundBackground Markov models have been the standard framework for predicting long-term clinical and economic outcomes using the surrogate marker endpoints from clinical trials. However, they are complex, have intensive data requirements and are often difficult for decision makers to understand. Recent developments in modelling software have made it possible to use discrete-event simulation (DES) to model outcomes in HIV. Using published results from 48-week trial data as model inputs, Markov model and DES modelling approaches were compared in terms of clinical outcomes at 5 years and lifetime cost-effectiveness estimates. Abstract: MethodsMethods A randomly selected cohort of 100 antiretroviral-naive patients with a mean baseline CD4+ T-cell count of 175 cells/mm treated with lopinavir/ritonavir was selected from Abbott study M97-720. Parameter estimates from this cohort were used to populate both a Markov and a DES model, and the long-term estimates for these cohorts were compared. The models were then modified using the relative risk of undetectable viral load as reported for atazanavir and lopinavir/ritonavir in the published BMS 008 study. This allowed us to compare the mean cost effectiveness of the models. The clinical outcomes included mean change in CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA (viral load &lsqb;VL&rsqb;) <50 copies/mL, VL 50-400 copies/mL and VL >400 copies/mL. US wholesale acquisition costs (year 2007 values) were used in the mean cost-effectiveness analysis, and the cost and QALY data were discounted at 3%. Abstract: ResultsResults The results show a slight predictive advantage of the DES model for clinical outcomes. The DES model could capture direct input of CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA VL <50 copies/mL, VL 50-400 copies/mL and VL >400 copies/mL over a 48-week period, which the Markov model could not. The DES and Markov model estimates were similar to the actual clinical trial estimates for 1-year clinical results; however, the DES model predicted more detailed outcomes and had slightly better long-term (5-year) predictive validity than the Markov model. Similar cost estimates were derived from the Markov model and the DES. Both models predict cost savings at 5 and 10 years, and over a lifetime for the lopinavir/ritonavir treatment regimen as compared with an atazanavir regimen. Abstract: ConclusionConclusion The DES model predicts the course of a disease naturally, with few restrictions. This may give the model superior face validity with decision makers. Furthermore, this model automatically provides a probabilistic sensitivity analysis, which is cumbersome to perform with a Markov model. DES models allow inclusion of more variables without aggregation, which may improve model precision. The capacity of DES for additional data capture helps explain why this model consistently predicts better survival and thus greater savings than the Markov model. The DES model is better than the Markov model in isolating long-term implications of small but important differences in crucial input data.