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Real-life data on access and response to direct antiviral agents (DAA) in HIV-HCV coinfected individuals are lacking. HCV viremic, HIV-positive patients from Icona and Hepaicona cohorts naïve to DAA by January 2013 were included. Access and predictors of starting DAA were evaluated. Switches of antiretroviral drugs at starting DAA were described. We calculated sustained virological response (SVR12) in those reaching 12 weeks after end-of-treatment (EOT), and defined treatment failure (TF) as discontinuation of DAA before EOT or non-SVR12. Statistical analyses included Kaplan-Meier curves, univariable and multivariable analyses evaluating predictors of access to DAA and of treatment outcome (non-SVR and TF). 2,607 patients included. During a median follow-up of 38 (IQR:30-41) months, 920 (35.3%) patients started DAA. Eligibility for reimbursement was the strongest predictor to access to treatment: 761/1,090 (69.8%) eligible and 159/1,517 (10.5%) non-eligible to DAA reimbursement. Older age, HIV-RNA≤50 copies/mL were associated to faster DAA initiation, higher CD4 count and HCV-genotype 3 with delayed DAA initiation in those eligible to DAA reimbursement. Up to 28% of patients (36% of those on ritonavir-boosted protease inhibitors, PI/r) underwent antiretroviral (ART) modification at DAA initiation. 545/595 (91.6%) patients reaching EOT achieved SVR12. Overall, TF occurred in 61/606 patients (10.1%), with 11 discontinuing DAA before EOT. Suboptimal DAA was the only independent predictor of both non-SVR12 (AHR 2.52, 95%CI:1.24-5.12) and TF (AHR: 2.19; 95%CI:1.13-4.22). Only 35.3% had access to HCV treatment. Despite excellent rates of SVR12 rates (91.6%), only 21% (545/2,607) of our HIV-HCV co-infected patients are cured.

Introduction The aim of this study was to identify the heterogeneous classes of CD4/CD8 ratio trajectory and their impacts on prognosis in people living with HIV (PLHIV) during long-term antiretroviral therapy. Methods A retrospective cohort study of PLHIV receiving ART treatment was conducted in the Eighth Affiliated Hospital of Guangzhou Medical University, and the latent growth mixture model (LGMM) was used to identify the trajectories of longitudinal changes in the CD4/CD8 ratio between February 10, 2004, and January 31, 2019. Cox proportional hazard model and proportional subdistribution hazard model were conducted to explore the impact of CD4/CD8 ratio trajectory on prognosis. Results Three heterogeneous trajectories were identified: the baseline-low-level slow increase class (Class 1: 57.45%), the baseline-moderate-level rapid increase class (Class 2: 34.29%), and the baseline-high-level sharp increase class (Class 3: 8.26%). Cox proportional hazard model showed that CD4/CD8 ratio trajectory was associated with all-cause mortality among PLHIV, with adjusted hazard ratios (aHR) (95% confidence interval [CI]) for Class 2, and Class 3 being 0.53 (0.38–0.75), and 0.35 (0.14–0.86) respectively, compared with Class 1. The result of the proportional subdistribution hazard model showed that the CD4/CD8 ratio trajectory was associated with the risk of AIDS-related and non-AIDS-related mortality. Conclusions Our study demonstrated that there were three different trajectories of CD4/CD8 ratio during long-term ART. Personalized interventions and treatment plans can be developed based on individual changes in CD4/CD8 ratio, which is important for improving the survival of the PLHIV and reducing disease burden.

Time-to-event outcomes are common in medical research as they offer more information than simply whether or not an event occurred. To handle these outcomes, as well as censored observations where the event was not observed during follow-up, survival analysis methods should be used. Kaplan-Meier estimation can be used to create graphs of the observed survival curves, while the log-rank test can be used to compare curves from different groups. If it is desired to test continuous predictors or to test multiple covariates at once, survival regression models such as the Cox model or the accelerated failure time model (AFT) should be used. The choice of model should depend on whether or not the assumption of the model (proportional hazards for the Cox model, a parametric distribution of the event times for the AFT model) is met. The goal of this paper is to review basic concepts of survival analysis. Discussions relating the Cox model and the AFT model will be provided. The use and interpretation of the survival methods model are illustrated using an artificially simulated dataset.

In a randomized trial involving patients with previously untreated advanced renal-cell carcinoma, nivolumab plus cabozantinib had significant benefits over sunitinib with respect to progression-free and overall survival and the likelihood of response. A total of 19.7% of the patients in the combination group discontinued one or both of the trial drugs prematurely.

Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Existing methods can accommodate both heterogeneity identification and variable selection under parametric models, but for survival analysis, the commonly used Cox model is semiparametric. Although finite-mixture Cox model has been proposed to address heterogeneity in survival analysis, variable selection has not been incorporated into such semiparametric models. Using regularization regression, we propose a variable selection method for the finite-mixture Cox model and select important, subtype-specific risk factors from high-dimensional predictors. Our estimators have oracle properties with proper choices of penalty parameters under the regularization regression. An expectation-maximization algorithm is developed for numerical calculation. Simulations demonstrate that our proposed method performs well in revealing the heterogeneity and selecting important risk factors for each subtype, and its performance is compared to alternatives with other regularizers. Finally, we apply our method to analyze a gene expression dataset for ovarian cancer DNA repair pathways. Based on our selected risk factors, the prognosis model accounting for heterogeneity consistently improves the prediction for the survival probability in both training and test datasets.

AbstractObjectiveTo evaluate the association between intakes of refined grains, whole grains, and white rice with cardiovascular disease, total mortality, blood lipids, and blood pressure in the Prospective Urban and Rural Epidemiology (PURE) study.DesignProspective cohort study.SettingPURE study in 21 countries.Participants148 858 participants with median follow-up of 9.5 years.ExposuresCountry specific validated food frequency questionnaires were used to assess intakes of refined grains, whole grains, and white rice.Main outcome measureComposite of mortality or major cardiovascular events (defined as death from cardiovascular causes, non-fatal myocardial infarction, stroke, or heart failure). Hazard ratios were estimated for associations of grain intakes with mortality, major cardiovascular events, and their composite by using multivariable Cox frailty models with random intercepts to account for clustering by centre.ResultsAnalyses were based on 137 130 participants after exclusion of those with baseline cardiovascular disease. During follow-up, 9.2% (n=12 668) of these participants had a composite outcome event. The highest category of intake of refined grains (≥350 g/day or about 7 servings/day) was associated with higher risk of total mortality (hazard ratio 1.27, 95% confidence interval 1.11 to 1.46; P for trend=0.004), major cardiovascular disease events (1.33, 1.16 to 1.52; P for trend<0.001), and their composite (1.28, 1.15 to 1.42; P for trend<0.001) compared with the lowest category of intake (<50 g/day). Higher intakes of refined grains were associated with higher systolic blood pressure. No significant associations were found between intakes of whole grains or white rice and health outcomes.ConclusionHigh intake of refined grains was associated with higher risk of mortality and major cardiovascular disease events. Globally, lower consumption of refined grains should be considered.

The simultaneous estimation and variable selection for Cox model has been discussed by several authors when one observes right-censored failure time data. However, there does not seem to exist an established procedure for interval-censored data, a more general and complex type of failure time data, except two parametric procedures. To address this, we propose a broken adaptive ridge (BAR) regression procedure that combines the strengths of the quadratic regularization and the adaptive weighted bridge shrinkage. In particular, the method allows for the number of covariates to be diverging with the sample size. Under some weak regularity conditions, unlike most of the existing variable selection methods, we establish both the oracle property and the grouping effect of the proposed BAR procedure. An extensive simulation study is conducted and indicates that the proposed approach works well in practical situations and deals with the collinearity problem better than the other oracle-like methods. An application is also provided.