Hyperuricemia management in chronic kidney disease is a challenging task. We encounter this dilemma on regular basis. Kidney disease patients have wide range (CKD population, Hemodialysis & peritoneal dialysis cohort and renal transplant patients).In clinical practice wide range of opinions exists. This dubious area intrigued us to look into it. Looking into available published data majority of studies are observational and few are randomized control trials. All studies favor that high uric acid level has accelerated effect on CKD progression. Controversy is on its management, whether by treating it we are able to slow down CKD progression or not. Data supports that CKD progression is not slowed down but needs more studies to give conclusive answer. In dialysis and renal transplant patients studies showed inverse relationship of high uric levels with all-cause mortality. However, in peritoneal dialysis data suggests linear relationship of hyperuricemia with mortality.A pro as well as anti-oxidant effect of uric acid has been discussed in literature. Variable cut off for hyperuricemia has been used but more census is on 7 mg/dl. Symptomatic gout defi nitely needs uric acid lowering therapy but in asymptomatic hyperuricemia no conclusion so far. There is paucity of data in maintenance dialysis and renal transplant patients.
This study aims to utilize body composition monitor (BCM) device to achieve euvolemic status in problematic dialysis patients and to evaluate its clinical outcome. One hundred and five hemodialysis (HD) patients were enrolled based on difficulty in achieving dry weight. The reasons for enrollment in the study were (a) recurrent intradialytic hypotension, (b) intradialytic hypertension, (c) intradialytic muscle cramps, or (d) the presence of comorbid conditions that make clinical assessment of dry weight difficult (e.g., cirrhosis of liver, heart failure, severe malnutrition, or morbid obesity). Following initial assessment of hydration status using BCM device, dry weight for each patient was adjusted accordingly (upward, downward, or unchanged). The patients were, thereafter, monitored over a 15-week period for possible resultant change in the clinical and hemodynamic parameters. Forty-two patients were monitored due to hypertension, 18 due to hypotension, 10 due to hypotension and cramps, and 35 due to comorbid conditions that make clinical assessment of dry weight difficult. At the conclusion of study period, there was improvement in the monitored parameters. Hypertension improved in 79% of the patient with hypertension, hypotension in 90%, and hypotension with cramps in 90%. In the comorbid group, BCM monitoring provided better insight to clinical problem management in 80% cases. Overall quality of BCM assessments was 96.1%. In the hypertension group, mean blood pressure decreased by 10.9 mm Hg in the whole group (P = 0.0006), the drop was 3 mm Hg in the patients dialyzing with HD (P = 0.0006) and 8.6 mm Hg in those on hemodiafiltration (HDF) (P = 0.08). In the comorbid conditions group, the mean blood pressure rose by 22.5 mm Hg in the whole group (P 0.00001), 21.5 mm Hg in the patients dialyzing with HD (P = 0.00001) and 21.5 mm Hg in those on HDF (P = 0.0004). BCM monitoring together with clinical assessment is a useful tool which when appropriately applied reduces the incidence of dialysis-related complications.
Objective: To develop a simple, objective, cheap scoring tool incorporating nutritional parameters and other variables to predict hospitalization and mortality among hemodialysis patients – a tool that could be utilized in low resource countries. Methods: The following variables were scored according to severity into 0, 1, 2 or 3: BMI, functional capacity, HD vintage in years, serum albumin, serum ferritin, and the number of comorbid conditions (diabetes mellitus, hypertension, ischemic heart disease, cerebrovascular disease). This tool was evaluated on our regular hemodialysis patients who were followed up for 24 months (June 2015 till July 2017). In our study population, the maximum score recorded was 12; accordingly, a score of 6 was used to differentiate between a low-risk group (score < 6) or a high-risk group (score ≥6). The 2 groups were compared (using the Chi square test) for possible differences in mortality and hospitalization rates during the follow-up period. Results: One hundred and forty adult hemodialysis patients were monitored over 2 years; 83 were males and 57 females; 59% of the patients had diabetes mellitus. Twenty-nine patients (30.7%) were found to be in the high-risk group and 111 (79.3%) in the low-risk group. The high-risk patients were almost one and a half times more likely to be hospitalized for vascular access issues than the low-risk group (p = 0.056) and 3 times more likely to be hospitalized for non-vascular access issues than the low-risk group (p = 0.0001). The mortality rate in the high-risk group was 3.1 times that in the low-risk group, but this was not statistically significant. Conclusion: Using a simple and cheap assessment tool in hemodialysis patients, we have identified patients at high risk for hospitalization rates and mortality. Video Journal Club “Cappuccino with Claudio Ronco” at http://www.karger.com/?doi=490544.
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