Closed loop stimulation in patients with chronic heart failure and severe chronotropic incompetence: Responders versus non-responders.

BACKGROUND: Clinical effects of rate-adaptive pacing (RAP) are unpredictable and highly variable among cardiac resynchronization therapy (CRT) patients with chronotropic incompetence. Physiologic sensors such as Closed Loop Stimulation (CLS), measuring intracardiac impedance changes (surrogate for ventricular contractility), may add clinical benefit and help identify predictors of response to RAP. The objective of the present BIOlCREATE study subanalysis was to identify criteria for selection of CRT patients who are likely to respond positively to CLS-based RAP. METHODS: In the randomized, crossover BIO|CREATE study, CRT patients with severe chronotropic incompetence and NYHA class II/III were randomized to CLS with conventional upper sensor rate programming or to no RAP for 1 month, followed by crossover for another month. At 1-month and 2-month follow-ups, patients underwent treadmill-based cardiopulmonary exercise test. Positive CLS response was defined as a ≥ 5% reduction in ventilatory efficiency slope. Eight of 17 patients (47%) were CLS responders. In this subanalysis, we compared responders and non-responders to explore outcomes, mechanisms, and predictors. RESULTS: All cardiopulmonary variables, health-related quality of life, patient activity status, and NT-proBNP concentration showed favorable trend in CLS responders and unfavorable trend in non-responders, underlining the need to find predictors. Following all analyses, we recommend CLS in heart failure patients with improved left ventricular ejection fraction (LVEF >40%, after a ≥ 10-point increase from a CRT-pre-implant value of ≤40%), corresponding to 'HFimpEF' in the universal classification system. CONCLUSION: HFimpEF patients are likely to benefit from CLS-based RAP, in contrast to 'HFrEF' (heart failure with reduced LVEF [≤40%]).

Impact of closed loop stimulation on prognostic cardiopulmonary variables in patients with chronic heart failure and severe chronotropic incompetence: a pilot, randomized, crossover study.

AIMS: Clinical effects of rate-adaptive pacing in heart failure patients with chronotropic incompetence (CI) undergoing cardiac resynchronization therapy (CRT) remain unclear. Closed loop stimulation (CLS) is a new rate-adaptive sensor in CRT devices. We evaluated the effectiveness of CLS in CRT patients with severe CI, focusing primarily on key prognostic variables assessed by cardiopulmonary exercise (CPX) testing. METHODS AND RESULTS: In the randomized, crossover, multicentre BIO|CREATE study, 20 CRT patients with severe CI and NYHA Class II/III (60%/40%) were randomized 1:1 to the sequence DDD-40 mode to DDD-CLS mode, or the sequence DDD-CLS mode to DDD-40 mode (1 month in each mode). Patients underwent symptom-limited treadmill-based CPX test in each mode. An improvement (decrease) of the ventilatory efficiency (VE) slope of ≥5% during CLS was regarded as positive response to CLS. Seventeen patients with full data sets had a mean intra-individual VE slope change of -1.8 ± 3.0 (-4.1%) with CLS (P = 0.23). Eight patients (47%) were CLS responders, with a -6.1 ± 2.7 (-16.4%) slope change (P = 0.029). Compared to non-responders, CLS responders had a higher left ventricular (LV) ejection fraction (46 ± 3 vs. 36 ± 9%; P = 0.0070), smaller end-diastolic LV volume (121 ± 34 vs. 181 ± 41 mL; P = 0.0085), smaller end-systolic LV volume (65 ± 23 vs. 114 ± 39 mL; P = 0.0076), and were predominantly in NYHA Class II (P = 0.0498). CONCLUSION: The data of the present pilot study are compatible with the notion that CLS activation may improve VE slope in CRT patients with severe CI and less advanced heart failure. Further research is needed to determine the long-term clinical outcomes of CLS.

A DAG-based comparison of interventional effect underestimation between composite endpoint and multi-state analysis in cardiovascular trials.

BACKGROUND: Composite endpoints comprising hospital admissions and death are the primary outcome in many cardiovascular clinical trials. For statistical analysis, a Cox proportional hazards model for the time to first event is commonly applied. There is an ongoing debate on whether multiple episodes per individual should be incorporated into the primary analysis. While the advantages in terms of power are readily apparent, potential biases have been mostly overlooked so far. METHODS: Motivated by a randomized controlled clinical trial in heart failure patients, we use directed acyclic graphs (DAG) to investigate potential sources of bias in treatment effect estimates, depending on whether only the first or multiple episodes are considered. The biases first are explained in simplified examples and then more thoroughly investigated in simulation studies that mimic realistic patterns. RESULTS: Particularly the Cox model is prone to potentially severe selection bias and direct effect bias, resulting in underestimation when restricting the analysis to first events. We find that both kinds of bias can simultaneously be reduced by adequately incorporating recurrent events into the analysis model. Correspondingly, we point out appropriate proportional hazards-based multi-state models for decreasing bias and increasing power when analyzing multiple-episode composite endpoints in randomized clinical trials. CONCLUSIONS: Incorporating multiple episodes per individual into the primary analysis can reduce the bias of a treatment's total effect estimate. Our findings will help to move beyond the paradigm of considering first events only for approaches that use more information from the trial and augment interpretability, as has been called for in cardiovascular research.

Simulating recurrent event data with hazard functions defined on a total time scale.

BACKGROUND: In medical studies with recurrent event data a total time scale perspective is often needed to adequately reflect disease mechanisms. This means that the hazard process is defined on the time since some starting point, e.g. the beginning of some disease, in contrast to a gap time scale where the hazard process restarts after each event. While techniques such as the Andersen-Gill model have been developed for analyzing data from a total time perspective, techniques for the simulation of such data, e.g. for sample size planning, have not been investigated so far. METHODS: We have derived a simulation algorithm covering the Andersen-Gill model that can be used for sample size planning in clinical trials as well as the investigation of modeling techniques. Specifically, we allow for fixed and/or random covariates and an arbitrary hazard function defined on a total time scale. Furthermore we take into account that individuals may be temporarily insusceptible to a recurrent incidence of the event. The methods are based on conditional distributions of the inter-event times conditional on the total time of the preceeding event or study start. Closed form solutions are provided for common distributions. The derived methods have been implemented in a readily accessible R script. RESULTS: The proposed techniques are illustrated by planning the sample size for a clinical trial with complex recurrent event data. The required sample size is shown to be affected not only by censoring and intra-patient correlation, but also by the presence of risk-free intervals. This demonstrates the need for a simulation algorithm that particularly allows for complex study designs where no analytical sample size formulas might exist. CONCLUSIONS: The derived simulation algorithm is seen to be useful for the simulation of recurrent event data that follow an Andersen-Gill model. Next to the use of a total time scale, it allows for intra-patient correlation and risk-free intervals as are often observed in clinical trial data. Its application therefore allows the simulation of data that closely resemble real settings and thus can improve the use of simulation studies for designing and analysing studies.

Sample-size calculation and reestimation for a semiparametric analysis of recurrent event data taking robust standard errors into account.

In some clinical trials, the repeated occurrence of the same type of event is of primary interest and the Andersen-Gill model has been proposed to analyze recurrent event data. Existing methods to determine the required sample size for an Andersen-Gill analysis rely on the strong assumption that all heterogeneity in the individuals' risk to experience events can be explained by known covariates. In practice, however, this assumption might be violated due to unknown or unmeasured covariates affecting the time to events. In these situations, the use of a robust variance estimate in calculating the test statistic is highly recommended to assure the type I error rate, but this will in turn decrease the actual power of the trial. In this article, we derive a new sample-size formula to reach the desired power even in the presence of unexplained heterogeneity. The formula is based on an inflation factor that considers the degree of heterogeneity and characteristics of the robust variance estimate. Nevertheless, in the planning phase of a trial there will usually be some uncertainty about the size of the inflation factor. Therefore, we propose an internal pilot study design to reestimate the inflation factor during the study and adjust the sample size accordingly. In a simulation study, the performance and validity of this design with respect to type I error rate and power are proven. Our method is applied to the HepaTel trial evaluating a new intervention for patients with cirrhosis of the liver.

Sample size in cluster-randomized trials with time to event as the primary endpoint.

In cluster-randomized trials, groups of individuals (clusters) are randomized to the treatments or interventions to be compared. In many of those trials, the primary objective is to compare the time for an event to occur between randomized groups, and the shared frailty model well fits clustered time-to-event data. Members of the same cluster tend to be more similar than members of different clusters, causing correlations. As correlations affect the power of a trial to detect intervention effects, the clustered design has to be considered in planning the sample size. In this publication, we derive a sample size formula for clustered time-to-event data with constant marginal baseline hazards and correlation within clusters induced by a shared frailty term. The sample size formula is easy to apply and can be interpreted as an extension of the widely used Schoenfeld's formula, accounting for the clustered design of the trial. Simulations confirm the validity of the formula and its use also for non-constant marginal baseline hazards. Findings are illustrated on a cluster-randomized trial investigating methods of disseminating quality improvement to addiction treatment centers in the USA.

Effectiveness of postoperative radiotherapy in patients with small oral and oropharyngeal squamous cell carcinoma and concomitant ipsilateral singular cervical lymph node metastasis (pN1) : A meta-analysis.

BACKGROUND: The positive effect of radiation therapy for patients with advanced oropharyngeal squamous cell carcinoma (OSCC) has been substantially verified. The present work investigated whether a meta-analysis of current data is able to evaluate the effectiveness of postoperative radiotherapy (PORT) in patients with small OSCC (pT1, pT2) and a single ipsilateral lymph node metastasis (pN1). METHODS: The meta-analysis comprises randomized and non-randomized studies. High-risk tumors were excluded and defined by size ≥ pT3/pT4, lymph node involvement ≥ pN2, or presence of additional histological risk factors, e.g., involved positive resection margins, extra nodal spread of the disease, or lymphangiosis carcinomatosa. The primary outcome analyzed mortality between the different treatment arms. RESULTS: Only one prospective randomized clinical trial and six retrospective observational studies were adequate for evaluation. Descriptive analysis revealed a marginally higher mortality in the irradiation group (44% vs. 34%). In contrast, a forest plot presentation of two of seven studies with and without events in the control and therapy arms presented an advantage for the irradiation group with the limitation of large heterogeneity and a lack of statistical significance. CONCLUSION: Present data are poor and exhibit limited internal and external validity; thus, direct comparison was not possible with the eligible studies. Therefore, a meta-analysis of present data may not serve as the basis for a general treatment recommendation but underlines the need of prospective, randomized, controlled clinical trials.

Adaptive trial design: a general methodology for censored time to event data.

Adaptive designs allow a clinical trial design to be changed according to interim findings without inflating type I error. The Inverse Normal method can be considered as an adaptive generalization of classical group sequential designs. The use of the Inverse Normal method for censored survival data was demonstrated only for the logrank statistic. However, the logrank statistic is inefficient in the presence of nuisance covariates affecting survival. We demonstrate, how the Inverse Normal method can be applied to Cox regression analysis. The required independence between test statistics of the different stages of the trial can be obtained by two different approaches. One is using the independent increment structure of the score process. The other uses right censoring and left truncating to divide individuals follow-up into per-stage data. Simulation studies show, that performance of the adaptive design does not depend on the method used for obtaining independence. Either way, an adaptive Cox regression analyis is more efficient than an adaptive logrank analysis if nuisance covariates affect survival.