Joachim Möcks

N-terminal pro brain natriuretic peptide in the management of patients in the medical emergency department (PROMPT): correlation with disease severity, utilization of hospital resources, and prognosis in a large, prospective, randomized multicentre trial

N‐terminal pro brain natriuretic peptide (NT‐proBNP) is a potent marker of heart failure and other cardiac diseases. The value of NT‐proBNP testing in the medical emergency department (ED) was assessed in patients >65 years old.

Trial-to-Trial Variability of Single Potentials: Methodological Concepts and Results

Variability of single visual evoked potentials was investigated by means of three statistical tests sensitive to amplitude variations, gradual changes, and latency jitter, respectively. In a sample of (n = 78) normal children, a considerable number of inhomogeneous responders was found, and most prominent were gradual potential changes and latency jitter. Removal of latency jitter demonstrated that the gradual changes are not of latency type and only partly of the amplitude type. As found from empirical densities, there is strong indication that there were subpopulations differing in their response style. On the whole, however, it was concluded that there was no clear, interindividually stable type of response variation in these data.

Performance and Measures of Performance for Estimators of Brain Potentials Using Real Data

A number of filtering and smoothing procedures have been proposed for estimating evoked brain potentials. Their common goal is to reduce noise further than averaging. A statistical method is proposed for comparing these estimators for real data, thereby avoiding the use of simulated data, which usually are not representative of the shapes encountered for signal and noise and might also rely on some artificial assumptions. This approach is applied to visual evoked potential (both flash and pattern reversal). Filtering methods offer substantial gains in mean square error, but most of the gain is obtained trivially by attenuating the average. This points out the need for also using other loss functions. In terms of these, adaptive filtering brings at best gains equivalent to smoothing.