Prognostic value of congestive heart failure history in patients undergoing percutaneous coronary interventions

AbstractObjectives. We sought to determine the prognostic significance of a history of congestive heart failure above that provided by baseline ejection fraction in patients undergoing percutaneous coronary interventions.Background. Left ventricular function is a known predictor of survival in patients with coronary artery disease, as is a history of congestive heart failure. The contribution of heart failure history independent of left ventricular function is unknown.Methods. Data were pooled from four interventional trials and the Duke University database. The combined dataset included 5,260 patients undergoing percutaneous interventions, 334 with and 4,926 without a history of heart failure. Patients were defined by the treating physician as having a clinical history of heart failure at the time of enrollment.Results. The 30-day and 6-month mortality were higher in patients with a clinical history of congestive heart failure than in those without such a history (2% vs. <1%, p = 0.002 at 30 days, 5% vs. 1%, p = 0.001 at 6 months). Heart failure history did not influence the incidence of myocardial infarction, use of angioplasty or the use of bypass surgery during follow-up. Multivariable analysis revealed that heart failure history added significantly to ejection fraction in predicting intermediate-term (6-month) mortality (p = 0.01). Stepwise logistic regression also revealed heart failure history to be an independent predictor of 6-month mortality (odds risk 1.9, 95% confidence interval 1.1 to 3.5).Conclusions. A clinical history of congestive heart failure is associated with increased early and intermediate-term mortality in patients undergoing percutaneous revascularization. Congestive heart failure history appears to provide prognostic information independent of that available from a patient’s left ventricular function. These findings suggest that patients with a clinical history of congestive heart failure who undergo a percutaneous intervention should be closely monitored, especially those with the lowest ejection fractions

Optimising sample sizes for animal distribution analysis using tracking data

Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. Here we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the r package SDLfilter. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking dataset of flatback turtles Natator depressus tagged with accurate Fastloc‐GPS tags (n = 69). Our approach has applicability for the post hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life‐history stages of animals