Giuliana Cortese

New Insight Into Epirubicin Cardiac Toxicity: Competing Risks Analysis of 1097 Breast Cancer Patients

BACKGROUND Current recommendations that cancer patients receive a maximum cumulative dose of 900 mg/m(2) epirubicin are based on the risk of epirubicin-mediated cardiotoxicity and do not take into account the competing risk of death from cancer. Here, we identify risk factors for cardiotoxicity and overall mortality and determine the cumulative dose of epirubicin that yields a 5% risk for cardiotoxicity for cancer patients from different risk backgrounds. METHODS Data were collected from 1097 consecutive anthracycline-naive patients treated for metastatic breast cancer with epirubicin. Patients who developed congestive heart failure classified as New York Heart Association class 2 or higher were recorded as having cardiotoxicity. Independent Cox multiple regression analyses for cardiotoxicity and for overall mortality were followed by competing risks analysis, with cardiotoxicity as the primary event and death from all other causes as the competing event. All statistical tests were two-sided. RESULTS A total of 11.4% of patients developed cardiotoxicity. Risk factors for cardiotoxicity included increased cumulative dose of epirubicin (hazard ratio per every 100 mg/m(2) administered = 1.40, 95% confidence interval = 1.21 to 1.61), patient age, predisposition to cardiac disease, history of mediastinal irradiation, or antihormonal treatment for metastatic disease. Risk factors for death from all other causes (including breast cancer) included lesser dosages of epirubicin, increased tumor burden, prior use of adjuvant chemotherapy, and patient age. The cumulative dosage of epirubicin that carries a 5% risk of cardiotoxicity was lower than previously assumed and was dependent on risks of both cardiotoxicity and overall mortality. CONCLUSION Maximum cumulative dosages of epirubicin are presented that confer a 5% risk of cardiotoxicity for patients with different sets of risk factors.

Competing risks and time-dependent covariates.

Time-dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time-fixed covariates. This study briefly recalls the different types of time-dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time-dependent covariates are to be included in the modeling process, then it is still possible to estimate cause-specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi-state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34, 70-85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause-specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods.

Long-Term outcome of drug-eluting stents compared with bare metal stents in ST-segment elevation myocardial infarction: results of the paclitaxel- or sirolimus-eluting stent versus bare metal stent in Primary Angioplasty (PASEO) Randomized Trial.

BACKGROUND Drug-eluting stents may offer benefits in terms of repeat revascularization that may be counterbalanced by a potential higher risk of stent thrombosis, especially among ST-segment elevation myocardial infarction (STEMI) patients. No data have been reported so far on the long-term benefits and safety of drug-eluting stents in STEMI. Thus, the aim of the present study was to evaluate the short- and long-term benefits of sirolimus-eluting stents (SES) and paclitaxel-eluting stents (PES) compared with bare metal stents (BMS) in patients undergoing primary angioplasty. METHODS AND RESULTS Consecutive STEMI patients admitted within 12 hours of symptom onset and undergoing primary angioplasty and stent implantation at a tertiary center with 24-hour primary percutaneous coronary intervention capability were randomly assigned to BMS, PES, or SES. All patients received upstream glycoprotein IIb/IIIa inhibitors. The primary end point was target lesion revascularization at the 1-year follow-up. Secondary end points were death and/or reinfarction, in-stent thrombosis, and major adverse cardiac events (combined death and/or reinfarction and/or target lesion revascularization) at long-term follow-up (up to 4 to 6 years). Cumulative incidence of end points was investigated. No patient was lost to follow-up. From October 1, 2003, to December 31, 2005, 270 patients with STEMI were randomized to BMS (n=90), PES (n=90), or SES (n=90). Procedural success was obtained in 93% to 95% of patients. Follow-up data were available for all patients. Compared with BMS (14.4%), both PES (4.4%; hazard ratio, 0.29; 95% confidence interval, 0.095 to 0.89; P=0.023) and SES (3.3%; hazard ratio, 0.21; 95% confidence interval, 0.06 to 0.75; P=0.016) were associated with a significant reduction in target lesion revascularization at the 1-year follow-up (primary study end point). At the long-term follow-up (4.3 years; 25th to 75th percentile, 3.7 to 5 years), no difference was observed in terms of death, reinfarction, and combined death and/or reinfarction, but compared with BMS (22.2%), both PES (6.7%; hazard ratio, 0.27; 95% confidence interval, 0.11 to 0.68; P=0.005) and SES (5.6%; hazard ratio, 0.22; 95% confidence interval, 0.083 to 0.59; P=0.003) were associated with a significant reduction in target lesion revascularization. CONCLUSIONS This study shows that among STEMI patients undergoing primary angioplasty, both SES and PES are associated with significant benefits in terms of target lesion revascularization at the long-term follow-up compared with BMS with no excess risk of thrombotic complications. Thus, until the results of further large randomized trials with long-term follow-up become available, drug-eluting stents may be considered among STEMI patients undergoing primary angioplasty.

Flexible survival regression modelling

Regression analysis of survival data, and more generally event history data, is typically based on Cox’s regression model. We here review some recent methodology, focusing on the limitations of Cox’s regression model. The key limitation is that the model is not well suited to represent time-varying effects. We start by considering classical and also more recent goodness-of-fit procedures for the Cox model that will reveal when the Cox model does not capture important aspects of the data, such as time-varying effects. We present recent regression models that are able to deal with and describe such time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox’s regression model and the need for other supplementary analyses with models such as those we present here.

Dynamic regression hazards models for relative survival.

A natural way of modelling relative survival through regression analysis is to assume an additive form between the expected population hazard and the excess hazard due to the presence of an additional cause of mortality. Within this context, the existing approaches in the parametric, semiparametric and non-parametric setting are compared and discussed. We study the additive excess hazards models, where the excess hazard is on additive form. This makes it possible to assess the importance of time-varying effects for regression models in the relative survival framework. We show how recent developments can be used to make inferential statements about the non-parametric version of the model. This makes it possible to test the key hypothesis that an excess risk effect is time varying in contrast to being constant over time. In case some covariate effects are constant, we show how the semiparametric additive risk model can be considered in the excess risk setting, providing a better and more useful summary of the data. Estimators have explicit form and inference based on a resampling scheme is presented for both the non-parametric and semiparametric models. We also describe a new suggestion for goodness of fit of relative survival models, which consists on statistical and graphical tests based on cumulative martingale residuals. This is illustrated on the semiparametric model with proportional excess hazards. We analyze data from the TRACE study using different approaches and show the need for more flexible models in relative survival.

Comparing predictions among competing risks models with time‐dependent covariates

Prediction of cumulative incidences is often a primary goal in clinical studies with several endpoints. We compare predictions among competing risks models with time-dependent covariates. For a series of landmark time points, we study the predictive accuracy of a multi-state regression model, where the time-dependent covariate represents an intermediate state, and two alternative landmark approaches. At each landmark time point, the prediction performance is measured as the t-year expected Brier score where pseudovalues are constructed in order to deal with right-censored event times. We apply the methods to data from a bone marrow transplant study where graft versus host disease is considered a time-dependent covariate for predicting relapse and death in remission.

Ca2+ channel clustering with insulin-containing granules is disturbed in type 2 diabetes

Loss of first-phase insulin secretion is an early sign of developing type 2 diabetes (T2D). Ca2+ entry through voltage-gated L-type Ca2+ channels triggers exocytosis of insulin-containing granules in pancreatic &bgr; cells and is required for the postprandial spike in insulin secretion. Using high-resolution microscopy, we have identified a subset of docked insulin granules in human &bgr; cells and rat-derived clonal insulin 1 (INS1) cells for which localized Ca2+ influx triggers exocytosis with high probability and minimal latency. This immediately releasable pool (IRP) of granules, identified both structurally and functionally, was absent in &bgr; cells from human T2D donors and in INS1 cells cultured in fatty acids that mimic the diabetic state. Upon arrival at the plasma membrane, IRP granules slowly associated with 15 to 20 L-type channels. We determined that recruitment depended on a direct interaction with the synaptic protein Munc13, because expression of the II–III loop of the channel, the C2 domain of Munc13-1, or of Munc13-1 with a mutated C2 domain all disrupted L-type channel clustering at granules and ablated fast exocytosis. Thus, rapid insulin secretion requires Munc13-mediated recruitment of L-type Ca2+ channels in close proximity to insulin granules. Loss of this organization underlies disturbed insulin secretion kinetics in T2D.

Interleukin‐22 in the diagnosis of active chronic graft‐versus‐host disease in paediatric patients

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A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size

The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann–Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.

Regression models for the restricted residual mean life for right-censored and left-truncated data

The hazard ratios resulting from a Coxs regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier to interpret than the hazard ratio; the residual mean time is an important example of those measures. However, because of the presence of right censoring, the tail of the survival distribution is often difficult to estimate correctly. Therefore, we consider the restricted residual mean time, which represents a partial area under the survival function, given any time horizon τ, and is interpreted as the residual life expectancy up to τ of a subject surviving up to time t. We present a class of regression models for this measure, based on weighted estimating equations and inverse probability of censoring weighted estimators to model potential right censoring. Furthermore, we show how to extend the models and the estimators to deal with delayed entries. We demonstrate that the restricted residual mean life estimator is equivalent to integrals of Kaplan-Meier estimates in the case of simple factor variables. Estimation performance is investigated by simulation studies. Using real data from Danish Monitoring Cardiovascular Risk Factor Surveys, we illustrate an application to additive regression models and discuss the general assumption of right censoring and left truncation being dependent on covariates. Copyright