Integrated Central Blood Pressure-aortic Stiffness Risk Categories and Cardiovascular Mortality in End-stage Renal Disease

BACKGROUND: Our aim was to study the predictive power of integrated central blood pressure-aortic stiffness (ICPS) risk categories on cardiovascular (CV) mortality in end-stage renal disease (ESRD) patients. METHODS: This is a secondary analysis of a prospective study of 91 ESRD patients on hemodialysis therapy. At baseline, pulse wave velocity (PWV), central systolic blood pressure (cSBP) and central pulse pressure (cPP) were measured and patients were followed up for CV mortality for a median 29.5 months. Based on the shape of the association of each individual ICPS parameter with the CV outcome, patients were assigned ICPS scores: one point was given, if either the cSBP value was in the 3rd, or if the PWV or cPP was in the 2nd or 3rd tertiles (ICPS range: 0–3). We then evaluated the role of ICPS risk categories (average: 0–1, high: 2, very high: 3 points) in the prediction of CV outcomes using Cox proportional hazard regression analysis and compared its discrimination (Harrell’s C) to that of each of its components. RESULTS: We found a strong dose–response association between ICPS risk categories and CV outcome (high risk HR = 2.62, 95% CI: 0.82–8.43, p for trend = 0.106; very high risk HR = 10.03, 95% CI: 1.67–60.42, p = 0.02) even after adjustment for multiple potential confounders. ICPS risk categories had a modest discrimination (C: 0.622, 95% CI: 0.525–0.719) that was significantly better than that of cSBP (dC: 0.061, 95% CI: 0.006–0.117). CONCLUSIONS: The ICPS risk categories may improve the identification of ESRD patients with high CV mortality risk

Serum osteoprotegerin level, carotid-femoral pulse wave velocity and cardiovascular survival in haemodialysis patients

BACKGROUND: Osteoprotegerin (OPG) is a marker and regulator of arterial calcification, and it is related to cardiovascular survival in haemodialysis patients. The link between OPG and aortic stiffening--a consequence of arterial calcification--has not been previously evaluated in this population, and it is not known whether OPG-related mortality risk is mediated by arterial stiffening. METHODS: At baseline, OPG and aortic pulse wave velocity (PWV) were measured in 98 chronic haemodialysis patients who were followed for a median of 24 months. The relationship between OPG and PWV was assessed by multivariate linear regression. The role of PWV in mediating OPG related cardiovascular mortality was evaluated by including both OPG and PWV in the same survival model. RESULTS: At baseline mean (standard deviation) PWV was 11.2 (3.3) m/s and median OPG (interquartile range) was 11.1 (7.5-15.9) pmol/L. There was a strong, positive, linear relationship between PWV and lnOPG (P = 0.009, model R(2) = 0.540) independent of covariates. During follow-up 23 patients died of cardiovascular causes. In separate univariate survival models both PWV and lnOPG were related to cardiovascular mortality [hazard ratios 1.31 (1.14-1.50) and 8.96 (3.07-26.16), respectively]. When both PWV and lnOPG were entered into the same model, only lnOPG remained significantly associated with cardiovascular mortality [hazard ratio 1.11 (0.93-1.33) and 7.18 (1.89-27.25), respectively). CONCLUSION: In haemodialysis patients OPG is strongly related to PWV and OPG related cardiovascular mortality risk is, in part, mediated by increased PWV

Soft computing and hybrid AI approaches to intelligent manufacturing

The application of pattern recognition (PR) techniques , artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in manufacturing by introducing a genetic algorithm (GA) based dynamic job shop scheduler and the integrated use of neural, fuzzy and GA techniques for modeling, control and monitoring purposes.