Cardiovascular Risk In Hemodialysis Patients Research Articles

Modification of cardiovascular risk in hemodialysis patients: An evidence‐based review

Cardiovascular disease accounts for 40% to 50% of deaths in dialysis populations. Overall, the risk of cardiac mortality is 10-fold to 20-fold greater in dialysis patients than in age and sex-matched controls without chronic kidney disease. The aim of this paper is to review critically the evidence that cardiac outcomes in dialysis patients are modified by cardiovascular risk factor interventions. There is limited, but as yet inconclusive controlled trial evidence that cardiovascular outcomes in dialysis populations may be improved by antioxidants (vitamin E or acetylcysteine), ensuring that hemoglobin levels do not exceed 120 g/L (especially in the setting of known cardiovascular disease), prescribing carvedilol in the setting of dilated cardiomyopathy, and by using cinacalcet in uncontrolled secondary hyperparathyroidism. Similarly, there are a number of negative controlled trials, which have demonstrated that statins, high-dose folic acid, angiotensin-converting enzyme inhibitors, multiple risk factor intervention via multidisciplinary clinics, and high-dose or high-flux dialysis are ineffective in preventing cardiovascular disease. Although none of these studies could be considered conclusive, the negative trials to date should raise significant concerns about the heavy reliance of current clinical practice guidelines on extrapolation of findings from cardiovascular intervention trials in the general population. It may be that cardiovascular disease in dialysis populations is less amenable to intervention, either because of the advanced stage of chronic kidney disease or because the pathogenesis of cardiovascular disease in dialysis patients is different from that in the general population. Large, well-conducted, multicenter randomized-controlled trials in this area are urgently required.

Body Composition and Cardiovascular Risk in Hemodialysis Patients

Death rate is unacceptably elevated in end-stage renal disease patients treated with hemodialysis. Excessive body fat, or obesity, is the well-known risk factor for cardiovascular disease and other health problems in the general population. However, hemodialysis patients with a higher body mass index (BMI) have a lower risk of death, as shown by many studies. There are several explanations for the paradox of BMI in dialysis patients. First, although body mass is composed of fat mass and fat-free mass (lean mass), it is unknown which is more important, fat mass or lean mass, in predicting outcome of hemodialysis patients. Second, it is also possible that functions of adipose tissue are altered in renal failure so that accumulation of body fat leads to less atherogenicity and beneficial properties become predominant. Third, an increased fat mass may be protective against death after harmful events. In this article, we explore these possibilities using either the data of our own cohort of hemodialysis patients or the existing registry data of Japan. We conclude that in hemodialysis patients, fat mass rather than lean mass plays a protective role against mortality, that the fat mass-adipocytokine relationship is altered, and that a low BMI is associated with increased risk of fatality after cardiovascular events rather than the risk of occurrence of such events.

Variable Effects of Soy Protein on Plasma Lipids in Hyperlipidemic and Normolipidemic Hemodialysis Patients

Hyperlipidemic factors contribute to the high cardiovascular risk in hemodialysis patients. Soy protein has decreased some atherogenic lipid concentrations in subjects with normal renal function. This study evaluates the effect of soy protein on serum lipid profiles in hyperlipidemic and normolipidemic hemodialysis patients. Nineteen hyperlipidemic and 18 normolipidemic hemodialysis patients were enrolled in a randomized, double-blind, placebo-controlled, clinical trial. After a 4-week run-in phase, subjects in each category were randomly assigned to 2 groups. Thirty grams of isolated soy protein or milk protein was consumed daily as a beverage at breakfast or postdialysis for 12 weeks. In hyperlipidemic subjects, soy protein intake significantly decreased total cholesterol levels by 18.6% (95% confidence interval [CI], -11.4 to -25.8; P = 0.04), triglyceride levels by 43.1% (95% CI, -34.0 to -52.2; P = 0.02), non-high-density lipoprotein cholesterol levels by 23.6% (95% CI, -14.7 to -32.5; P < 0.01), apolipoprotein B levels by 15.4% (95% CI, -5.4 to -25.4; P = 0.01), and insulin levels by 49.8% (95% CI, -23.3 to -66.1; P < 0.01). Low-density lipoprotein cholesterol concentration was decreased significantly (-25.8%; 95% CI, -8.3 to -42.7; P = 0.01), and high-density lipoprotein cholesterol level was increased significantly (17%; 95% CI, 2 to 32.0; P = 0.03), but there was no significant difference compared with the milk protein group (-5.5% +/- 16.9% and 7.0% +/- 11.8%, respectively). There were no significant changes in serum lipid and lipoprotein concentrations in normolipidemic subjects. These results indicate soy protein substitution has lipid-lowering effects in hyperlipidemic hemodialysis patients. However, soy protein intake had little effect on plasma lipid levels in normolipidemic hemodialysis patients.

Discordance of influence of hypertension on mortality and cardiovascular risk in hemodialysis patients

Hemodialysis patients are at significantly increased risk for both morbidity and mortality from cardiovascular disease. However, most recent reports have indicated elevated mortality risk associated with low blood pressure, rather than high blood pressure. We added nonfatal cardiovascular events as an outcome in addition to cardiovascular and all-cause mortality to analyze the risk of hypertension. One hundred sixty-four patients receiving regular hemodialysis between January and December 1998 were examined and prospectively followed up until the end of 2003. The primary end point was hospital admission or death from cardiovascular disease. Secondary end points were cardiovascular and all-cause mortality. During the 5-year follow-up period, 52 patients experienced cardiovascular events and 45 patients died (18 patients, from cardiovascular disease). Based on Cox analysis, high systolic blood pressure (relative risk [RR], 1.23; 95% confidence interval [CI], 1.07 to 1.43; P = 0.004) and older age were independently associated with cardiovascular events. Elevated systolic blood pressure (RR, 1.25; 95% CI, 0.99 to 1.59; P = 0.063) was a marginal predictor for cardiovascular mortality. Age, serum albumin level, malignant neoplasm, and diabetes were independent risk factors for all-cause mortality, whereas there was no association between blood pressure and all-cause mortality. The hazard ratio for cardiovascular events after adjustment for age, sex, and diabetes was lowest in patients with systolic blood pressure of 140.1 mm Hg or less and progressively increased with the increase in systolic blood pressure. Hypertension is a potent risk factor for cardiovascular disease in hemodialysis patients, as in the general population, whereas there is no association of hypertension with mortality. Active reduction in systolic blood pressure is important to minimize the occurrence of cardiovascular events.

Heart valve calcifications, survival, and cardiovascular risk in hemodialysis patients

Background: Cardiovascular (CV) calcifications constitute a strong risk marker, and recent studies in continuous ambulatory peritoneal dialysis patients have associated valvular calcifications to inflammation, mortality, and CV events. The prognostic value of cardiac valve calcifications and their relationship with left ventricular hypertrophy and background cardiovascular risk in hemodialysis patients is still unknown. Methods: The prognostic value of heart valve calcifications (detected by echocardiography) for all-cause and CV death was tested in a cohort of 202 hemodialysis (HD) patients. Results: Forty-seven patients had 1 or more calcified valves. Background CV complications were more frequent (P = 0.001) and left ventricular hypertrophy was more severe (P < 0.001) in patients with calcified valves than in those without this alteration. During the follow-up period (44 ± 23 months), 96 patients died, 66 patients (69%) of CV causes. Valve calcifications were significantly associated with all-cause (P = 0.02) and CV mortality (P ≤ 0.001). However, in statistical models adjusting for traditional and nontraditional CV risk factors and background CV complications and left ventricular mass index (LVMI), the relationship between calcified valves and incident all-cause and CV mortality was not significant. Conclusion: In HD patients, cardiac valve calcifications predict all-cause and CV mortality in unadjusted analyses, but these associations are not evident in models adjusting for background CV complications, LVMI, and other risk factors. Cardiac valve calcifications do not provide an independent contribution in the prediction of death and CV mortality.

Prediction of Cardiovascular Risk in Hemodialysis Patients by Data Mining

The objective of this work was to contribute to the development, validation and application of data mining methods for prediction in decision support systems in medicine. The particular focus was on the prediction of cardiovascular risk factors in hemodialysis patients, specifically the interventricular septum (IVS) thickness of the heart of individual patients as an important quantitative indicator to diagnose left ventricular hypertrophy. The work was based on data from 63 long-term hemodialysis patients of the KfH Dialysis Centre in Jena, Germany. The approach applied is based on data mining methods and involves four major steps: data based clustering, cluster based rule extraction, rulebase construction and cluster and rule based prediction. The methods employed include crisp and fuzzy algorithms. At each step, logical and medical validation of results was carried out. Different sets of randomly selected patient data were used to train, test and optimize the clusterbases and rulebases for prediction. Using the best clusterbase/rulebase combination designed, the IVS thickness cluster ('small' or 'large') was predicted correctly for 30 of the 35 patients with known IVS values in the training data set; no patient was predicted incorrectly and 5 were parity predicted. For the test data set, 4 of the 6 patients with known IVS values were predicted correctly, no patient incorrectly and 2 parity. These results did not substantially differ from those obtained using the second best clusterbase/rulebase combination which was finally recommended for use based on further performance criteria. The prediction of the IVS thickness clusters of the 22 patients with unknown IVS values also yielded good results that were (and could only be) validated by a medical individual risk assessment of these patients. The approach applied proved successful for the cluster and rule based prediction of a quantitative variable, such as IVS thickness, for individual patients from other variables relevant to the problem. The results obtained demonstrate the high potential of the approach and the methods developed and validated to support decision-making in hemodialysis and other fields of medicine by individual risk prediction.