Methods of Assessment and Clinical Relevance of QT Dynamics

The dependence on heart rate of the QT interval has been investigated for many years and several mathematical formulae have been proposed to describe the QT interval/heart rate (or QT interval/RR interval) relationship. While the most popular is Bazett’s formula, it overcorrects the QT interval at high heart rates and under-corrects it at slow heart rates. This formulae and many others similar ones, do not accurately describe the natural behaviour of the QT interval. The QT interval/RR interval relationship is generally described as QT dynamics. In recent years, several methods of its assessment have been proposed, the most popular of which is linear regression. An increased steepness of the linear QT/RR slope correlates with the risk of arrhythmic death following myocardial infarction. It has also been demonstrated that the QT interval adapts to heart rate changes with a delay (QT hysteresis) and that QT dynamics parameters vary over time. New methods of QT dynamics assessment that take into account these phenomena have been proposed. Using these methods, changes in QT dynamics have been observed in patients with advanced heart failure, and during morning hours in patients with ischemic heart disease and history of cardiac arrest. The assessment of QT dynamics is a new and promising tool for identifying patients at increased risk of arrhythmic events and for studying the effect of drugs on ventricular repolarisation

A New Operator and Method for Solving Interval Linear Equations

We deal with interval linear systems of equations. We present a new operator, which generalizes the interval Gauss-Seidel method. Also, based on the new operator and properties of the well-known methods, we propose a new algorithm, called the magnitude method. We illustrate by numerical examples that our approach overcomes some classical methods with respect to both time and sharpness of enclosures

Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods

Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.forecasting, forecasting time series, interval forecasts, Lee-Carter method, life expectancy, mortality forecasting, principal components analysis