Influence of socioeconomic factors on pregnancy outcome in women with structural heart disease

OBJECTIVE: Cardiac disease is the leading cause of indirect maternal mortality. The aim of this study was to analyse to what extent socioeconomic factors influence the outcome of pregnancy in women with heart disease.  METHODS: The Registry of Pregnancy and Cardiac disease is a global prospective registry. For this analysis, countries that enrolled ≥10 patients were included. A combined cardiac endpoint included maternal cardiac death, arrhythmia requiring treatment, heart failure, thromboembolic event, aortic dissection, endocarditis, acute coronary syndrome, hospitalisation for cardiac reason or intervention. Associations between patient characteristics, country characteristics (income inequality expressed as Gini coefficient, health expenditure, schooling, gross domestic product, birth rate and hospital beds) and cardiac endpoints were checked in a three-level model (patient-centre-country).  RESULTS: A total of 30 countries enrolled 2924 patients from 89 centres. At least one endpoint occurred in 645 women (22.1%). Maternal age, New York Heart Association classification and modified WHO risk classification were associated with the combined endpoint and explained 37% of variance in outcome. Gini coefficient and country-specific birth rate explained an additional 4%. There were large differences between the individual countries, but the need for multilevel modelling to account for these differences disappeared after adjustment for patient characteristics, Gini and country-specific birth rate.  CONCLUSION: While there are definite interregional differences in pregnancy outcome in women with cardiac disease, these differences seem to be mainly driven by individual patient characteristics. Adjustment for country characteristics refined the results to a limited extent, but maternal condition seems to be the main determinant of outcome

A Note on Varying Cardinality in the Average Case Setting

AbstractWe study how much information with varying cardinality can be better than information with fixed cardinality for approximating linear operators in the average case setting with Gaussian measure. It has been known that adaptive choice of functionals forming information is not better than nonadaptive, and that the only gain may be obtained by using varying cardinality. We prove that the lower bounds from Traub (J. F. Traub, G. W. Wasilkowski, and H. Woźniakowski, "Information-Based Complexity," Academic Press, San Diego, 1988) et al. on the efficiency of varying cardinality are sharp. In particular, we show that information whose cardinality assumes at most two different values can significantly help in approximating any linear operator with infinite dimensional domain space