What are the components that contribute to variability in echocardiographic measurements in aortic stenosis?

INTRODUCTION Echocardiography is the standard clinical approach for quantification of the severity of aortic stenosis (AS). A comprehensive examination of its overall reproducibility and the simultaneous estimation of its variance components by multiple operators, readers, probe applications, and beats have not been undertaken. METHOD AND RESULTS Twenty-seven subjects with AS were scanned over 7 months in the echo-department by a median of 3 different operators. From each patient and each operator multiple runs of beats from multiple probe positions were stored for later analysis by multiple readers. The coefficient of variation was 13.3%, 15.9%, 17.6%, and 20.2% for the aortic peak velocity (Vmax), and velocity time integral (VTI), and left ventricular outflow tract (LVOT) Vmax and VTI respectively. The largest individual contributors to the overall variability were the beat-to-beat variability (9.0%, 9.3%, 9.5%, 9.4% respectively) and that of inability of an individual operator to precisely apply the probe to the same position twice (8.3%, 9.4%, 12.9%, 10.7% respectively). The tracing (inter-reader) and reader (inter-reader), and operator (inter-operator) contribution were less important. CONCLUSIONS Reproducibility of measurements in AS is poorer than often reported in the literature. The source of this variability does not appear, as traditionally believed, to result from a lack of training or operator and reader specific factors. Rather the unavoidable beat-to-beat biological variability, and the inherent impossibility of applying the ultrasound probe in exactly the same position each time are the largest contributors. Consequently, guidelines suggesting greater standardisation of procedures and further training for sonographers are unlikely to result in an improvement in precision. Clinicians themselves should be wary of relying on even three-beat averages as their expected coefficient of variance is 10.3% for the peak velocity at the aortic valve.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.