The Role Of Family History In Predicting Dissection Outcomes

Background: Studies have shown a significant association between family history and aortic aneurysm but this relationship has yet to be explored in depth with dissections. This study examined the role of three family histories (dissection, aortic aneurysm, and sudden death) with the objective to find the best indicator of dissection development. Methods: A total of N=278 patients seen at Yale between 2013-2015 for aortic aneurysm and dissections were enrolled and their family histories obtained. Six indicators were examined 1) family history of dissection 2) family history of aortic aneurysm 3) family history of sudden death 4) family history of dissection or aortic aneurysm 5) family history of dissection or sudden death and 6) family history of dissection or aortic aneurysm or sudden death. Results: Patients with a family history of dissection or sudden death were 1.73 (p = 0.038; 95% CI 1.03, 2.90) times as likely to have a dissection compared to those with a negative family history. Patients with a positive family history of aortic aneurysm were 2.31 (p = 0.027; 95% CI 1.10, 4.85) times as likely to have a dissection compared to those with a negative family history (adjusted). Patients with a positive family history of dissection or aortic aneurysm or sudden death were 1.80 (p=0.0487; 95% CI 1.00, 3.23) times as likely to have a dissection compared to those with a negative family history (adjusted). Conclusion: Three indicators were significant in predicting dissection outcomes 1) family history of dissection or sudden death 2) family history of aortic aneurysm and 3) family history of dissection or aortic aneurysm or sudden death. These findings suggest increased screening for patients with positive corresponding family histories

Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores

To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers. Copyright © 2019 the Author(s)

Estimation of spatio-temporal extreme distribution using a quantile factor model

This paper describes the estimation of the extreme spatio-temporal sea surface temperature data based on the quantile factor model implemented by the SNU multiscale team. The proposed method was developed for the EVA2019 Data Challenge. Various attempts have been conducted to use factor models in spatio-temporal data analysis to find hidden factors in high-dimensional data. Factor models represent high-dimensional data as a linear combination of several factors, and hence, can describe spatially and temporally correlated data in a simple form. Meanwhile, unlike ordinary factor models, there are asymmetric norm-based factor models, such as quantile factor models or expectile dynamic semiparametric factor models, that can help understand the quantitative behavior of data beyond their mean structure. For this purpose, we apply a quantile factor model to the data to obtain significant factors explaining the quantile response of the temperatures and find quantile estimates. We develop a new method for inference of quantiles of extremal levels by extrapolating quantile estimates from the factor model with extreme value theory. The proposed method provides better performance than the benchmark, gives some interpretable insights, and shows the potential to expand the factor model with various data.N

Association between the incidence of type 1 diabetes mellitus and tuberculosis or bacillus Calmette-Guérin immunization in children and adolescents

Purpose The correlation between the incidence of type 1 diabetes mellitus (T1DM) and tuberculosis or bacillus Calmette-Guérin (BCG) vaccination rate in individuals aged <15 years was investigated using worldwide data. Methods The incidence of T1DM, rate of BCG vaccination, and incidence of tuberculosis were obtained from the Diabetes Atlas 9th edition of the International Diabetes Federation and the Global Health Observatory data repository of the World Health Organization. Gross domestic product (GDP) per capita and population data by country were obtained from the World Bank and United Nations, respectively. Results GDP per capita negatively correlated with the incidence of tuberculosis and positively correlated with the incidence of T1DM (coefficient=-0.630 and 0.596, respectively; all P<0.001). The incidence of T1DM and tuberculosis was significantly associated with the Organisation for Economic Cooperation and Development (OECD) status (P<0.001). After adjusting for GDP per capita, regional grouping, and OECD status, the incidence of T1DM negatively correlated with that of tuberculosis (R2 =0.729, P=0.009). However, there was no association between the BCG vaccination rate and incidence of T1DM (P=0.890). Conclusions There was a negative correlation between the incidence of tuberculosis and T1DM in children and adolescents aged <15 years at the country level

Differences in all-cause mortality and complications (stroke, acute myocardial infarction, and chronic renal failure) related to hospitalisations between the intervention and control groups, using the Kaplan–Meier curve, during the 5-year follow-up period.

Differences in all-cause mortality and complications (stroke, acute myocardial infarction, and chronic renal failure) related to hospitalisations between the intervention and control groups, using the Kaplan–Meier curve, during the 5-year follow-up period.</p

Trends of annual visits to the clinics per person and annual days covered for antihypertensive, antidiabetic drugs, or insulin injections per person in intervention and control groups during the period of two years prior to and five years after the baseline period.

Trends of annual visits to the clinics per person and annual days covered for antihypertensive, antidiabetic drugs, or insulin injections per person in intervention and control groups during the period of two years prior to and five years after the baseline period.</p

Hazard ratios for the intervention group vs. the control group for all-cause death and complications (stroke, acute myocardial infarction, and chronic renal failure) during the five-year follow-up period based on the Cox proportional hazard model (among excluded the pre-existing complications).

Hazard ratios for the intervention group vs. the control group for all-cause death and complications (stroke, acute myocardial infarction, and chronic renal failure) during the five-year follow-up period based on the Cox proportional hazard model (among excluded the pre-existing complications).</p

Difference-in-differences regression, including covariates of effects of the CRMHDP on the annual visits to clinics and annual days covered per person, with the control group matched by propensity scores.

Difference-in-differences regression, including covariates of effects of the CRMHDP on the annual visits to clinics and annual days covered per person, with the control group matched by propensity scores.</p

Differences-in-differences estimates for the effect of CRMHDP on annual clinic visits per person and annual days covered for antihypertensive drugs, antidiabetic drugs, and insulin injections per person in the control group matched by propensity scores.

Differences-in-differences estimates for the effect of CRMHDP on annual clinic visits per person and annual days covered for antihypertensive drugs, antidiabetic drugs, and insulin injections per person in the control group matched by propensity scores.</p