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2.  Epidemiology of Interdialytic Ambulatory Hypertension and the Role of Volume Excess 
American Journal of Nephrology  2011;34(4):381-390.
Background
The epidemiology of hypertension among hemodialysis (HD) patients is difficult to describe accurately because of difficulties in the assessment of blood pressure (BP).
Methods
Using 44-hour interdialytic ambulatory BP measurements, we describe the epidemiology of hypertension in a cohort of 369 patients. To seek correlates of hypertension control, antihypertensive agents were withdrawn among patients with controlled hypertension and ambulatory BP monitoring was repeated.
Results
Hypertension (defined as an average ambulatory systolic BP ≥135 mm Hg or diastolic BP ≥85 mm Hg, or the use of antihypertensive medications) was prevalent in 82% of the patients and independently associated with epoetin use, lower body mass index and fewer years on dialysis. Although 89% of the patients were being treated, hypertension was controlled adequately in only 38%. Poor control was independently associated with greater antihypertensive drug use. Inferior vena cava (IVC) diameter in expiration was associated with increased risk of poorly controlled hypertension both in cross-sectional analysis and after withdrawal of antihypertensive drugs.
Conclusions
Interdialytic hypertension is highly prevalent and difficult to control among HD patients. End-expiration IVC diameter is associated with poor control of hypertension in cross-sectional analyses as well as after washout of antihypertensive drugs. Among HD patients, an attractive target for improving hypertension control appears to be the reduction of extracellular fluid volume.
doi:10.1159/000331067
PMCID: PMC3182044  PMID: 21893975
Ambulatory blood pressure monitoring; Epidemiology; Epoetin; Hemodialysis; Hypertension; Vitamin D receptor activators
3.  Probing Dry-Weight Improves Left Ventricular Mass Index 
American Journal of Nephrology  2011;33(4):373-380.
Background
Although probing dry-weight improves blood pressure control, its effect on echocardiographic left ventricular mass index (LVMI) is unknown.
Methods
Shortly following dialysis, 292 echocardiograms in 150 patients participating in the DRIP trial were obtained at baseline and longitudinally every 4 weeks on 2 occasions.
Results
At baseline, LVMI was 136.3 g/m2 in the control group and 138.7 g/m2 in the ultrafiltration group (p > 0.2 for difference). The change from baseline in LVMI in the control group was +3.5 g/m2 at 4 weeks and +0.3 g/m2 at 8 weeks (p > 0.2 for both changes). The change from baseline in LVMI in the ultrafiltration group was −7.4 g/m2 at 4 weeks (p = 0.005) and −6.3 g/m2 at 8 weeks (p = 0.045). With ultrafiltration, the change in LVMI diameter was −10.9 g/m2 more compared to the control group at 4 weeks (p = 0.012) and −6.6 g/m2 more compared to the control group at 8 weeks (p = 0.21). The reduction in interdialytic ambulatory blood pressure was also greater in response to probing dry-weight in those in the top half of LVMI at baseline (p = 0.02 for interaction effect at week 8).
Conclusion
LVMI, an important determinant of prognosis among long-term dialysis patients, is responsive to probing dry-weight.
doi:10.1159/000326235
PMCID: PMC3078237  PMID: 21447945
Hemodialysis; Hypertension; Ultrafiltration; Ambulatory blood pressure; Volume overload; Echocardiogram; Left ventricular hypertrophy; Left ventricular systolic function
4.  Analyzing Change: A Primer on Multilevel Models with Applications to Nephrology 
American journal of nephrology  2008;28(5):792-801.
The analysis of change is central to the study of kidney research. In the past 25 years, newer and more sophisticated methods for the analysis of change have been developed, however as of yet these newer methods are underutilized in the field of kidney research. Repeated measures ANOVA is the traditional model that is easy to understand and simpler to interpret, but it may not be valid in complex real-world situations. Problems with the assumption of sphericity, unit of analysis, lack of consideration for different types of change, and missing data, in the repeated measures ANOVA context are often encountered. Multilevel modeling, a newer and more sophisticated method for the analysis of change, overcomes these limitations and provides a better framework for understanding the true nature of change. The present article provides a primer on the use of multilevel modeling to study change. An example from a clinical study is detailed and the method for implementation in SAS is provided.
doi:10.1159/000131102
PMCID: PMC2613435  PMID: 18477842
Longitudinal data analysis; analysis of change; change over time; repeated measures; multilevel modeling; mixed effects models; random coefficient models; hierarchical linear models; unit of analysis
5.  Analyzing Change: A Primer on Multilevel Models with Applications to Nephrology 
American Journal of Nephrology  2008;28(5):792-801.
The analysis of change is central to the study of kidney research. In the past 25 years, newer and more sophisticated methods for the analysis of change have been developed; however, as of yet these newer methods are underutilized in the field of kidney research. Repeated measures ANOVA is the traditional model that is easy to understand and simpler to interpret, but it may not be valid in complex real-world situations. Problems with the assumption of sphericity, unit of analysis, lack of consideration for different types of change, and missing data, in the repeated measures ANOVA context are often encountered. Multilevel modeling, a newer and more sophisticated method for the analysis of change, overcomes these limitations and provides a better framework for understanding the true nature of change. The present article provides a primer on the use of multilevel modeling to study change. An example from a clinical study is detailed and the method for implementation in SAS is provided.
doi:10.1159/000131102
PMCID: PMC2613435  PMID: 18477842
Longitudinal data analysis; Analysis of change; Change over time; Repeated measures; Multilevel modeling; Mixed effects models; Random coefficient models; Hierarchical linear models; Unit of analysis

Results 1-5 (5)