Comparative Cardiac Magnetic Resonance-Based Feature Tracking and Deep-Learning Strain Assessment in Patients Hospitalized for Acute Myocarditis.

This study sought to examine the correlation between left ventricular (LV) myocardial feature tracking (FT) and deep learning-based strain (DLS) analysis in the diagnostic (CMRd) and follow-up (CMRf) cardiac magnetic resonance imaging of patients with acute myocarditis. The retrospective study included 17 patients with acute myocarditis and 20 healthy controls. The CMRd took place within 14 days of symptom onset, while the CMRf took place at least 2 months after the event. The global-circumferential FT (FTc) and global-circumferential DLS (DLSc) were analyzed. The continuous variables were compared using paired t-tests or the Wilcoxon test, whereas Pearson's test or Spearman's test was used to evaluate the correlation between the continuous variables. The time between the CMRd and CMRf was 5 months [3-11]. The LV ejection fraction (LVEF) was 55 ± 6 and 59 ± 4%, p = 0.008, respectively, and 94.1% of the patients showed late gadolinium enhancement (LGE) and myocardial edema on the CMRd. Significantly lower FTc (-16.1 ± 2.2% vs. -18.9 ± 1.9%, p = 0.001) and DLSc (-38.1 ± 5.2% vs. -41.3 ± 4.5%, p = 0.015) were observed with respect to the controls. Significant increases in the FTc (-16.1 ± 2.2 vs. -17.5 ± 1.9%, p = 0.016) and DLSc (-38.1 ± 5.2 vs. -39.8 ± 3.9%, p = 0.049) were found between the CMRd and CMRf, which were unrelated to the LGE. The LVEF correlated well with the FTc (r = 0.840) and DLSc (r = 0.760). Both techniques had excellent reproducibility, with high intra- (FTc = 0.980, DLSc = 1.000) and inter-observer (FTc = 0.970, DLSc = 0.980) correlation. There was correlation between the LV DLSc/FTc and LVEF in the patients with acute myocarditis according to the CMRd and CMRf.

Public healthcare expenditure in Spain: measuring the impact of driving factors.

OBJECTIVES: To assess the impact of demography, health status, death related costs and some macroeconomic variables on the evolution of health expenditure. METHODS: We follow the methodology used by the Ageing Working Group (AWG) of the European Union to simulate expenditure projections on the basis of healthcare expenditure profiles for age-sex population groups. We estimate the profiles using data from Hospital Discharges Statistics and the Spanish National Health Survey. RESULTS: The differences between the compression of morbidity scenario and the expansion of morbidity scenario range from 1.35 to 1.57 points of GDP in 2060. The overestimation of healthcare expenditure when death related costs are ignored ranges from 0.04 to 0.11 percentage points, depending on the health status hypothesis. Moreover, the effect of death related cost diminishes as health status improves. CONCLUSIONS: Our results support the fact that intensity of healthcare use, instead of ageing, is the main driver of health expenditure. Thus, the concern of keeping expenditure under control should be focused on factors such as the population's health status, economic growth and development, new technologies and medical progress, and the organization and management of the healthcare system.

[Real per capita health spending by age and sex in Spain (1998-2008): changes and effects on public healthcare expenditure projections]. / Evolución de la prestación real media en España por edad y sexo (1998-2008) y su repercusión en las proyecciones de gasto sanitario público.

OBJECTIVES: To analyze changes in real per capita spending by age and sex from 1998 to 2008 in Spain, and to assess their effects on public healthcare expenditure projections. METHODS: Age- and sex-related expenditure profiles in constant terms were estimated for the Spanish population for 3 distinct years (1998, 2003 and 2008) by using data from hospital records and several National Health Surveys. These profiles were used to compare actual healthcare expenditure for 2003 and 2008 with the projections obtained by considering 1998 as the base year and by applying the methodology used by the Working Group on Aging of the European Union. RESULTS: The average annual growth rate of real per capita spending per person from 1998 to 2008 was 2.79%, which was higher than the GDP per capita growth rate (1.90%), basically due to its high rate of increase in the second half of the decade. From 1998 to 2008, per capita healthcare expenditure increased in most age groups, particularly in the groups aged 45-49 years, 60-64 years and 75 years and older. Projections of per capita expenditure in constant terms covered the real value observed for 2003, but were below the real value for 2008. CONCLUSIONS: Changes in the quantity and quality of healthcare services consumed by each person are an important factor in changes in healthcare expenditure and must be included in spending projections.