Search Results (4)

Pulmonary hypertension (PH) is a common complication of aortic stenosis (AS). Despite the established association between PH and poor outcomes in patients with AS, the prognostic implication of a change in PH after transcatheter aortic valve implantation (TAVI) has been rarely evaluated. This study analyzed a prospective multi-center TAVI registry database involving six Japanese centers and used the transtricuspid pressure gradient (TRPG) obtained by echocardiography to estimate pulmonary artery systolic pressure. The participants (n = 2056) were first divided into two groups by TRPG before TAVI, a PH (−) group (TRPG < 30 mmHg) (n = 1407, 61.9%) and a PH (+) group (TRPG ≥ 30 mmHg) (n = 649, 28.6%). Next, by TRPG after (4.1 ± 5.3 days) TAVI, the PH (+) group was further subdivided into two groups, Recovered PH (TRPG < 30 mmHg, n = 253) and Persistent PH (TRPG after TAVI ≥ 30 mmHg, n = 396). The median follow-up duration was 1.8 years. The primary and secondary endpoints were the composite and each of cardiovascular (CV) death and heart failure hospitalization, respectively. Unadjusted Kaplan-Meier estimates with log-rank comparisons showed significantly higher cumulative incidences of primary and secondary endpoints in the Persistent PH group compared to other groups. Moreover, adjusted multivariate Cox-proportional hazard analyses showed that a decreased (−10 mmHg) TRPG after TAVI was linearly associated with a reduced risk of the primary endpoint (hazard ratio (HR): 0.76, 95% confidence interval (CI): 0.64–0.90, p = 0.0020). The findings in the present study indicate that the recovery of PH may partly contributes to the prognostic benefit of TAVI procedure in patients with AS and elevated pulmonary artery systolic pressure. Full article

This paper proposes a method for deriving interpretable common factors based on canonical correlation analysis applied to the vectors of common factors and manifest variables in the factor analysis model. First, an entropy-based method for measuring factor contributions is reviewed. Second, the entropy-based contribution measure of the common-factor vector is decomposed into those of canonical common factors, and it is also shown that the importance order of factors is that of their canonical correlation coefficients. Third, the method is applied to derive interpretable common factors. Numerical examples are provided to demonstrate the usefulness of the present approach. Full article

In factor analysis, factor contributions of latent variables are assessed conventionally by the sums of the squared factor loadings related to the variables. First, the present paper considers issues in the conventional method. Second, an alternative entropy-based approach for measuring factor contributions is proposed. The method measures the contribution of the common factor vector to the manifest variable vector and decomposes it into contributions of factors. A numerical example is also provided to demonstrate the present approach. Full article

A path analysis method for causal systems based on generalized linear models is proposed by using entropy. A practical example is introduced, and a brief explanation of the entropy coefficient of determination is given. Direct and indirect effects of explanatory variables are discussed as log odds ratios, i.e., relative information, and a method for summarizing the effects is proposed. The example dataset is re-analyzed by using the method. Full article