Background: The changes in the period of ventricular repolarization, i.e., QT interval, QTp (Q-Tpeak) and TpTe interval (Tpeak–Tend), make it possible to assess the electrical instability of the heart muscle, which may lead to the development of life-threatening ventricular arrhythmia. The aim of the study was to determine and evaluate the use of differences in T-wave morphology and durations of repolarization period parameters (QT, TpTe) in resting ECGs for children with ventricular arrhythmias. Methods: The retrospective analysis was made of the disease histories of 80 examined children with resting ECGs, which were admitted to the Children’s Cardiology Department. The study group consisted of 46 children aged 4 to 18 with ventricular arrhythmias and the control group consisted of 34 healthy children between 4 and 18 years of age, with no arrhythmias. Results: The duration of the TpTe interval was significantly (p < 0.001) longer in the group of children with ventricular arrhythmia with abnormal T-wave (bactrian/bifid, humid/biphasic) compared to the TpTe interval in children with ventricular arrhythmia with the normal repolarization period. The duration of the TpTe (p < 0.001), QTcB (p < 0.001) and QTcF (p < 0.001) intervals were significantly longer in the group of children with ventricular arrhythmias and with abnormal T-wave compared to the values of the TpTe, QTcB, and QTcF intervals of the control group with normal morphology of the repolarization period. Only the duration of the TpTe interval was significantly (p = 0.020) longer in the group of children with ventricular arrhythmia without clinical symptoms. Conclusions: Children with benign ventricular arrhythmias recorded on a standard ECG with prolonged TpTe and QT intervals and abnormal T-wave morphology require systematic and frequent cardiac check up with long term ECG recordings due to the possibility of future more severe ventricular arrhythmias. Further follow-up studies in even larger groups of patients are necessary to confirm the values of these repolarization parameters in clinical practice.
Robust estimation presented in the following paper is based on Fisher consistent and Fréchet differentiable statistical functionals. The method has been used in the multivariate normal model with variance components [5]. To transfer the method to estimate vector of expectations and positive definite covariance matrix of the multivariate normal model it is required to express the covariance matrix as a linear combination of basic elements of the vector space of real, square and symmetric matrices. The theoretical results have been completed with computer simulation studies. The robust estimator has been investigated both for model and contaminated data. Comparison with the maximum likelihood estimator has also been included.
Spatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.
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