Background and aims Current ESC guidelines on the management of patients after acute myocardial infarction only include the evaluation of left ventricular (LV) function by assessment of the ejection fraction in addition to clinical risk scores to estimate the patient’s prognosis. We aimed to determine, whether comprehensive evaluation of cardiac function using LV and right ventricular (RV) global longitudinal strain (GLS) and left atrial (LA) reservoir strain improves the prediction of survival in patients with acute myocardial infarction. Methods In patients with non-ST segment elevation or ST segment elevation myocardial infarction receiving echocardiography within 1 year after revascularisation, LV-GLS, RV-GLS and LA reservoir strain were quantified. In multivariable Cox regression analysis, HRs and 95% CIs were calculated per 1 SD increase in strain measure, adjusting for age, sex, systolic blood pressure, low-density lipoprotein cholesterol, smoking, diabetes and family history of premature coronary artery disease. Results During a median follow-up of 1.5 (0.5–4.2) years, 157 (11.1%) out of 1409 patients (64.4±13.5 years, 24.7% female) died. LV-GLS (1.68 (1.37–2.06), p<0.001), RV-GLS (1.39 (1.16–1.67), p<0.001) and LA reservoir strain (0.57 (0.47–0.69), p<0.001) were associated with mortality. Adding LV ejection fraction, tricuspid annular plane systolic excursion (TAPSE) or LA volume index to these models did not alter the association of strain measures of the LV (1.41 (1.06–1.89), p=0.02), RV (1.48 (1.03–2.13), p=0.04) or LA (0.61 (0.49–0.76), p<0.001). In receiver operating characteristics, combining the three strain measures improved the prediction of mortality above risk factors (AUC: 0.67 (0.63–0.71) to 0.75 (0.70–0.80)), while further addition of LV ejection fraction, TAPSE and LA volume index did not (0.75 (0.70–0.81)). Conclusion The comprehensive evaluation of contractility of various cardiac chambers via transthoracic echocardiography using myocardial strain analysis, when routinely performed after acute myocardial infarction, may help to detect patients at increased mortality risk.

Background: Many datasets in medicine and other branches of science are incomplete. In this article we compare various imputation algorithms for missing data. Objectives: We take the point of view that it has already been decided that the imputation should be carried out using multiple imputation by chained equation and the only decision left is that of a subroutine for the one-dimensional imputations. The subroutines to be compared are predictive mean matching, weighted predictive mean matching, sampling, classification or regression trees and random forests. Methods: We compare these subroutines on real data and on simulated data. We consider the estimation of expected values, variances and coefficients of linear regression models, logistic regression models and Cox regression models. As real data we use data of the survival times after the diagnosis of an obstructive coronary artery disease with systolic blood pressure, LDL, diabetes, smoking behavior and family history of premature heart diseases as variables for which values have to be imputed. While we are mainly interested in statistical properties like biases, mean squared errors or coverage probabilities of confidence intervals, we also have an eye on the computation time. Results: Weighted predictive mean matching had to be excluded from the statistical comparison due to its enormous computation time. Among the remaining algorithms, in most situations we tested, predictive mean matching performed best. Novelty: This is by far the largest comparison study for subroutines of multiple imputation by chained equations that has been performed up to now.

The area under the receiver operating characteristic curve (AUC) is a suitable measure for the quality of classification algorithms. Here we use the theory of U-statistics in order to derive new confidence intervals for it. The new confidence intervals take into account that only the total sample size used to calculate the AUC can be controlled, while the number of members of the case group and the number of members of the control group are random. We show that the new confidence intervals can not only be used in order to evaluate the quality of the fitted model, but also to judge the quality of the classification algorithm itself. We would like to take this opportunity to show that two popular confidence intervals for the AUC, namely DeLong’s interval and the Mann–Whitney intervals due to Sen, coincide.

The surface area of a set which is only observed as a binary pixel image is often estimated by a weighted sum of pixel configurations counts. In this paper we examine these estimators in a design based setting—we assume that the observed set is shifted uniformly randomly. Bounds for the difference between the essential supremum and the essential infimum of such an estimator are derived, which imply that the variance is in $$O(t^2)$$O ( t 2 ) as the lattice distance t tends to zero. In particular, it is asymptotically neglectable compared to the bias. A simulation study shows that the theoretically derived convergence order is optimal in general, but further improvements are possible in special cases.

Publisher: VTeX - Solutions for Science Publishing, Journal: Modern Stochastics - Theory and Applications, Title: Corrigendum to “A new confidence interval based on the theory of U-statistics for the area under the curve”, Authors: Jürgen Kampf, Lukas Hermann Vogel, Iryna Dykun, Tienush Rassaf, Amir Abbas Mahabadi