Ion in PDgroups according to the presence of AoAC at baseline. To determine differences between the two groups, a Student’s t-test and the chi-square test were performed for continuous variables and categorical variables, respectively. Multivariate 86168-78-7 binary logistic regression models were used to identify significant determinants of AoAC presence at PD initiation. Cumulative survival curves were generated by the Kaplan-Meier method, and between-group survival was compared by a log-rank test. Independent prognostic values of AoAC at baseline for all-cause and cardiovascular mortality were ascertained by Cox proportional hazards models, which included only the significant variables in univariate analysis. Meanwhile, the progression of AoAC was focused in the second analysis. In the second analysis, mean values of the biochemical parameters during the first year of PD were used. Pearson’s correlation analysis was performed to estimate association between the changes in AoACS and other continuous variables. Multivariate binary logistic regression models, which included significant variables in univariate analysis, were constructed to determine significant independent predictors of AoAC progression. Subgroup analysis was also performed according to the presence of baseline AoAC. The impact of AoAC progression on patient outcome was examined by the Kaplan-Meier method and Cox proportional hazards regression analysis. Significant variables in univariate analysis, traditional risk factors (age, sex, and diabetes mellitus), and factors associated with inflammation and nutrition (serum hs-CRP and albumin concentrations) were included in multivariate Cox proportional hazard models. A P value less than 0.05 was considered statistically significant.Table 1. Baseline characteristics of the patients with and without aortic arch calcification (AoAC).Characteristics Number ( ) Age (years) Male, n ( ) Diabetes mellitus, n ( ) Primary renal disease, n ( ) Glomerulonephritis Diabetes mellitusWith AoAC 169 (40.7 ) 66.769.3 88 (52.0 ) 104 (61.5 )Without AoAC 246 (59.3 ) 52.1613.1 146 (59.3 ) 92 (37.3 )P,0.001 NS ,0.001 NS38 (22.4 ) 86 (50.9 )73 (29.6 ) 84 (34.1 ) 21 (8.5 ) 4 (1.6 ) 64 (26.0 ) NSHypertensive nephrosclerosis 12 (7.1 ) Polycystic kidney disease Others/Unknown Peritoneal equilibration test, n ( ) High High average Low average Low Kt/V urea (per week) Cardiovascular disease, n ( ) Ejection order Tetracosactide fraction ( ) History of smoking, n ( ) 7 (4.1 ) 123 (72.7 ) 34 (20.1 ) 5 (2.9 ) 2.360.5 94 (55.6 ) 52.8617.5 41 (24.2 ) 1 (0.6 ) 32 (18.9 )24 (9.8 ) 126 (51.2 ) 90 (36.5 ) 6 (2.4 ) 2.560.7 51 (20.7 ) 61.469.8 87 (35.3 ) 24.1618.2 139.8619.8 22.663.1 9.261.6 158.8643.4 43.9612.6 213.56176.0 3.560.6 ?.260.9 65 (26.4 ) NS ,0.001 0.03 0.02 0.03 NS NS NS 0.02 NS ,0.001 0.008 ,0.001 ,0.Amount of smoking (pack-years)35.1624.Results Clinical Characteristics According to the Presence 1326631 of AoAC at BaselineBaseline patient characteristics according to the presence of AoAC at baseline are shown in Table 1. The mean age was 55.8613.8 years (21?0 years), and 234 patients (56.3 ) were male. Of 415 patients, 169 patients (40.7 ) had AoAC at baseline with a mean AoACS of 18.1611.2 . Diabetic nephropathy was the most common cause of ESRD, followed by chronic glomerulonephritis in both groups. The mean age, the proportion of patients with diabetes and previous history of cardiovascular disease, and the proportion of patients taking lipid-lowering agents and b-blockers were significan.Ion in PDgroups according to the presence of AoAC at baseline. To determine differences between the two groups, a Student’s t-test and the chi-square test were performed for continuous variables and categorical variables, respectively. Multivariate binary logistic regression models were used to identify significant determinants of AoAC presence at PD initiation. Cumulative survival curves were generated by the Kaplan-Meier method, and between-group survival was compared by a log-rank test. Independent prognostic values of AoAC at baseline for all-cause and cardiovascular mortality were ascertained by Cox proportional hazards models, which included only the significant variables in univariate analysis. Meanwhile, the progression of AoAC was focused in the second analysis. In the second analysis, mean values of the biochemical parameters during the first year of PD were used. Pearson’s correlation analysis was performed to estimate association between the changes in AoACS and other continuous variables. Multivariate binary logistic regression models, which included significant variables in univariate analysis, were constructed to determine significant independent predictors of AoAC progression. Subgroup analysis was also performed according to the presence of baseline AoAC. The impact of AoAC progression on patient outcome was examined by the Kaplan-Meier method and Cox proportional hazards regression analysis. Significant variables in univariate analysis, traditional risk factors (age, sex, and diabetes mellitus), and factors associated with inflammation and nutrition (serum hs-CRP and albumin concentrations) were included in multivariate Cox proportional hazard models. A P value less than 0.05 was considered statistically significant.Table 1. Baseline characteristics of the patients with and without aortic arch calcification (AoAC).Characteristics Number ( ) Age (years) Male, n ( ) Diabetes mellitus, n ( ) Primary renal disease, n ( ) Glomerulonephritis Diabetes mellitusWith AoAC 169 (40.7 ) 66.769.3 88 (52.0 ) 104 (61.5 )Without AoAC 246 (59.3 ) 52.1613.1 146 (59.3 ) 92 (37.3 )P,0.001 NS ,0.001 NS38 (22.4 ) 86 (50.9 )73 (29.6 ) 84 (34.1 ) 21 (8.5 ) 4 (1.6 ) 64 (26.0 ) NSHypertensive nephrosclerosis 12 (7.1 ) Polycystic kidney disease Others/Unknown Peritoneal equilibration test, n ( ) High High average Low average Low Kt/V urea (per week) Cardiovascular disease, n ( ) Ejection fraction ( ) History of smoking, n ( ) 7 (4.1 ) 123 (72.7 ) 34 (20.1 ) 5 (2.9 ) 2.360.5 94 (55.6 ) 52.8617.5 41 (24.2 ) 1 (0.6 ) 32 (18.9 )24 (9.8 ) 126 (51.2 ) 90 (36.5 ) 6 (2.4 ) 2.560.7 51 (20.7 ) 61.469.8 87 (35.3 ) 24.1618.2 139.8619.8 22.663.1 9.261.6 158.8643.4 43.9612.6 213.56176.0 3.560.6 ?.260.9 65 (26.4 ) NS ,0.001 0.03 0.02 0.03 NS NS NS 0.02 NS ,0.001 0.008 ,0.001 ,0.Amount of smoking (pack-years)35.1624.Results Clinical Characteristics According to the Presence 1326631 of AoAC at BaselineBaseline patient characteristics according to the presence of AoAC at baseline are shown in Table 1. The mean age was 55.8613.8 years (21?0 years), and 234 patients (56.3 ) were male. Of 415 patients, 169 patients (40.7 ) had AoAC at baseline with a mean AoACS of 18.1611.2 . Diabetic nephropathy was the most common cause of ESRD, followed by chronic glomerulonephritis in both groups. The mean age, the proportion of patients with diabetes and previous history of cardiovascular disease, and the proportion of patients taking lipid-lowering agents and b-blockers were significan.
Related Posts
The proportion of TB circumstances with pulmonary TB was increased in cohorts from high/intermediate load configurations when compared to individuals from low load options
Traits of cohorts from large/intermediate and low burden settings are introduced in Desk one. When in contrast to cohorts from reduced load settings, cohorts from substantial/intermediate burden were being more compact in dimension, had decrease median CD4 cell counts at research entry and experienced much less person-a long time follow up. TST positivity was claimed […]
Hondrial genes. A detailed analysis of your family tree aims to recognize minor clinical signs
Hondrial genes. A detailed analysis of your family tree aims to recognize minor clinical signs in connected parties. There is no risk for the offspring of a man carrying a point mutation. However, the risk is higher for the offspring and siblings of a woman with an mtDNA mutation. Mitochondrial mutations are heterogeneous and may […]
An basilar motion, based on the cell’s RC time constant.
An basilar motion, based on the cell’s RC time constant. This problem has been addressed by many investigators, and many ostensible resolutions to the RC time-constant problem have been proposed (15,25,59?2). Stattic web however, we must now consider the slow kinetics of prestin at physiological chloride levels that we have uncovered. This can only make […]