Hypervolemia is associated with increased mortality among hemodialysis patients

Abstract

Among chronic hemodialysis patients, 217 hospitalizations/1000 patient-years are due to congestive heart failure; some are attributable to unrecognized hypervolemia. Hypervolemia can be detected by relative plasma volume (RPV) monitoring. The purpose of this study was to examine among 308 patients on long-term hemodialysis the value of slope of RPV compared to either ultrafiltration volume or ultrafiltration rate index in determining all-cause mortality. RPV slopes were calculated by least-squares regression. These slopes were related to all-cause mortality in unadjusted and adjusted Cox proportional hazards models. Over a median follow up of 30 months (IQR 14 – 54 months) 96 (31%) patients died yielding a crude mortality rate of 113/1000 patient years. We found that 1) RPV slope measurements were of prognostic significance (hazard ratio of flatter slopes (>1.39%/hour) 1.72, p = 0.01); 2) the ultrafiltration volume alone was not prognostically informative (hazard ratio of higher UF volume (>2.7 liter/dialysis) 0.78, p=0.23); 3) the ultrafiltration rate index alone was also not prognostically informative (hazard ratio of higher UF rate index (>8.4 mL/kg/hr) 0.89, p=0.6); and 4) the prognostic relationship of RPV slope to mortality was independent of conventional and unconventional cardiovascular risk factors including the ultrafiltration volume, ultrafiltration rate or ultrafiltration volume/kg post weight. RPV monitoring yields information that is prognostically important and independent of several risk factors including ultrafiltration volume, aggressiveness of ultrafiltration, and interdialytic ambulatory BP. Its use to assess excess volume among chronic hemodialysis patients should be tested in randomized controlled trials.

Introduction

The annual mortality rate among chronic hemodialysis patients approaches 18%. About half of the deaths are believed to be due to cardiovascular causes. What is perhaps less appreciated is that according to the United States Renal Data System 217 hospitalizations/1000 patient-years are attributed to congestive heart failure. While congestive heart failure has numerous causes, volume excess likely plays a major role.

Currently there are no reference standards to define volume excess but several objective markers have been proposed¹. These markers include clinical examination, total body water measurement², echocardiographic assessment³, hormones (atrial natriuretic peptide, B-type natriuretic peptide, and N-terminal pro-B-natriuretic peptide⁴), bioimpedance analysis⁵, and relative plasma volume monitoring⁶. Some reports have reported that large interdialytic weight gains are a proxy of hypervolemia ^7,8. Others have reported that aggressive ultrafiltration rates are associated with mortality ⁹. While some techniques such as total body water measurement by heavy water ² or echocardiographic assessment are considered valuable, they are expensive and difficult to perform. Others such as clinical examination are simple but lack the sensitivity and specificity to identify hypervolemia ^10,11. Bioimpedance analysis has been used extensively in Europe and found to be of prognostic significance ^12,13.

Relative plasma volume (RPV) monitoring is a commercially available technology that is relatively easy and inexpensive to perform¹⁴. To monitor RPV a device is attached to the hemodialysis blood tubing that continuously and accurately measures the hematocrit by optical absorbance ¹⁵. The reason why this technique is useful in assessing RPV is the following: assuming no change in the red cell mass during hemodialysis and uniform mixing of red cells within the vasculature, the percent increase in hematocrit during ultrafiltration estimates the percent decrease in blood volume ¹⁵. RPV monitoring has been used extensively in the US and found to be prognostically useful in children ¹⁶. Although we have previously demonstrated that RPV monitoring may be useful for assessing dry-weight ¹⁷, its value in determining prognosis remains undefined among adult hemodialysis patients.

The purpose of this study was to examine, among chronic hemodialysis patients, the prognostic significance of volume excess. Volume excess in this study was assessed by RPV monitoring and prognosis by all cause mortality. We also evaluated the comparative value of the slope of RPV to ultrafiltration volume, UF volume/kg post-dialysis weight and UF rate/kg post dialysis weight—in determining all-cause mortality.

Methods

Participants

Patients 18 years or older who had been on chronic hemodialysis for more than 3 months, and were free of vascular, infectious or bleeding complications within one month of recruitment who were dialyzed three times a week dialysis at one of the four dialysis units in Indianapolis affiliated with Indiana University were enrolled in the study. Those who missed two hemodialysis treatments or more over one month, abused drugs, had chronic atrial fibrillation or body mass index of 40 kg/m² or more were excluded. Patients who had a change in dry-weight or antihypertensive drugs within 2 weeks were also excluded. The study was approved by the Institutional Review Board of Indiana University and Research and Development Committee of the Roudebush VA Medical Center, Indianapolis and all subjects gave written informed consent.

Measurements

Relative plasma volume monitoring

Relative plasma volume monitoring was performed on a single occasion with Crit-Line® III-TQA which is a clinically available device that incorporates photo-optical technology to non-invasively measure absolute hematocrit (Hemametrics, Kayesville, UT) ¹⁸. Hematocrit was measured every 20 seconds throughout the duration of hemodialysis. Measurements made by the machine have been validated against hematocrits measured by centrifugation ¹⁴. We exported the machine stored time and hematocrit data to a relational database for further analysis.

Ambulatory BP Monitoring

Ambulatory BP monitoring was performed either after the first or mid-week hemodialysis session for 44 hours. Most recordings were performed following the RPV monitoring. However, when this was not possible, then ambulatory BP monitoring was performed within 2 weeks of RPV monitoring. Ambulatory BP was recorded every 20 minutes during the day (6 AM to 10 PM) and every 30 minutes during the night (10 PM to 6 AM) using a Spacelab 90207 ambulatory blood pressure monitor (SpaceLabs Medical Inc, Redmond, WA, USA) in the non-access arm, as reported previously ¹⁹. Hourly averages were calculated. The mean of hourly average values represent the 44-hour mean systolic or diastolic BP.

Outcomes

All cause mortality was the primary focus of our study and this outcome was available in every patient. Patients were censored on the date that they had the last dialysis visit if they were transplanted or left the dialysis unit.

Data Analysis

The change in plasma volume with ultrafiltration approximates a first order elimination kinetics during hemodialysis. For each patient, relative plasma volume (RPV) slope was calculated using least-squares linear regression as follows. First, we calculated the fraction of blood free of hematocrit using the formula 100-hematocrit%. Next, we took the natural log of this fraction as the dependent variable. The independent variable was time measured in hours elapsed since initiation of dialysis. The coefficient on the dialysis time term in this model was converted back to percent change in RPV per hour by using the following formula: 100 × (1-exp(β)) where β is the coefficient on the dialysis time. This percent change in RPV was then used for further analyses. We calculated the slope till ultrafiltration was stopped or saline was administered. Blood transfusions or clotted access prompted the repetition of RPV monitoring on another occasion.

There is no reference standard which defines volume overload. We have earlier used the median of RPV slopes to classify patients who are more and less volume overloaded ¹⁷. Accordingly, splitting RPV slopes at the median provided two groups of patients—those with steeper and flatter slopes. Kaplan-Meier survival curves were created; the log-rank test was performed to evaluate the equality of the 2 survival curves. Cox proportional hazards regression was then used to determine the significance and strength of association of factors associated with mortality outcomes. The proportionality assumption was tested both by evaluating the log minus log plot as well as by testing the Schoenfield residuals. Initially, model fits between mortality and UF volume, UF volume/kg, UF rate/kg and RPV slopes were compared without adjustment. We then created multivariate adjusted models. Adjustments were made for the following variables: age, ethnicity, sex, cardiovascular disease, antihypertensive medications, serum albumin, hemoglobin, and dialysis vintage (model 1). Three further models were created that added in addition to the covariates in model 1 the following covariates: ultrafiltration volume (model 2); ultrafiltration volume/post dialysis weight (kg) (model 3), and ultrafiltration rate/post dialysis weight (model 4). Ambulatory systolic BP has previously been reported to be of prognostic value ²⁰. Therefore, an additional model was created by adding interdialytic ambulatory systolic BP to model 4. Adjusted hazard ratios were calculated with continuous covariates (age, albumin, hemoglobin, dialysis vintage, ultrafiltration volume) at their group means.

All analyses were conducted using Stata 11.0 (Stata Corp, College Station, TX). The P values reported are two-sided and taken to be significant at <0.05.

Results

Between September 2003 and March 2010, 770 patients from four dialysis units staffed by the nephrology faculty of Indiana University, Indianapolis were screened. Among the screened subjects, 548 qualified, of which 407 consented to participate. Of these, 91 had no RPV monitoring and 8 had inadequate recordings. The clinical characteristics of the remaining 308 patients dichotomized at median of RPV slopes are shown in Table 1. The median RPV slope was 1.39% per hour. All patients were on thrice weekly dialysis and were prescribed a dialysis time of about 4 hours and blood flow rate of 400 mL/min. The population was predominantly black with average age of 54.5 years. Serum albumin and hemoglobin reflect a generally healthier hemodialysis population. Cardiovascular disease defined as previous history of myocardial infarction, coronary bypass surgery or angioplasty, or stroke was present in 34% patients. Majority (79%) of the patients received antihypertensive drugs; beta-blockers were used in about two-thirds, and ACE inhibitors or angiotensin receptor blockers in about half. As expected, those who had flatter slopes and therefore possibly more volume overloaded had lower albumin and lower hemoglobin reflecting dilutional effects. Those with flatter slopes also gained less interdialytic weight gain and therefore had less ultrafiltration volumes and UF rates. Patients with flatter slopes were older, had more diabetes mellitus and had higher interdialytic ambulatory systolic blood pressure. A multivariable logistic regression model that included significant predictors from Table 1 demonstrated that only two variables were determinants of steeper slopes. These variables were ultrafiltration rate index (odds ratio 1.44 (95% CI 1.30 – 1.59, p<0.001) and serum albumin (odds ratio 2.31 (95% CI 1.04 – 5.13, p = 0.04).

Table 1.

Clinical characteristics of the study population dichotomized by RPV slope

Clinical characteristic	Flat slope	Steep slope	Total	p
n	154 (50%)	154 (50%)	308 (100%)
RPV Slope (%/hr)	0.7 ± 0.4	2.6 ± 1.0	1.6 ± 1.2	<0.0001
Age (y)	56.7 ± 11.9	52.3 ± 13.5	54.5 ± 12.9	<0.01
Men	98 (64%)	108 (70%)	206 (67%)	0.2
Racial Category				0.07
White	12 (8%)	25 (16%)	37 (12%)
Black	139 (90%)	126 (82%)	265 (86%)
Other	3 (2%)	3 (2%)	6 (2%)
Years on dialysis	3.5 ± 3.3	3.7 ± 4.4	3.6 ± 3.9	0.6
History of diabetes mellitus	85 (55%)	65 (42%)	150 (49%)	0.02
History of cardiovascular disease	56 (36%)	50 (32%)	106 (34%)	0.4
On antihypertensive medications	122 (79%)	121 (79%)	243 (79%)	0.9
Beta-blocker	102 (66%)	99 (64%)	201 (65%)	0.7
RAAS inhibitors	86 (56%)	79 (51%)	165 (54%)	0.4
Pre-HD weight (kg)	83.6 ± 18.5	84.3 ± 21.6	83.9 ± 20.1	0.8
Post-HD weight (kg)	81.0 ± 17.6	80.6 ± 20.9	80.8 ± 19.3	0.9
UF volume (mL)	2116 ± 1135	3396 ± 1291	2756 ± 1372	<0.0001
UF/Post dialysis weight (mL/kg)	26.0 ± 13.3	42.6 ± 14.6	34.3 ± 16.2	<0.0001
UFRate/Post weight (mL/kg/hr)	6.7 ± 3.3	11.3 ± 3.9	9.0 ± 4.3	<0.0001
Blood flow rate (mL/hr)	397 ± 39	400 ± 34	398 ± 37	0.5
Dialysate flow rate (mL/hr)	759 ± 80	766 ± 77	762 ± 79	0.5
Prescribed dialysis time (min)	238 ± 21	238 ± 21	238 ± 21	0.9
Kt/V	1.6 ± 0.4	1.6 ± 0.5	1.6 ± 0.4	0.9
Albumin (g/dL)	3.6 ± 0.5	3.8 ± 0.4	3.7 ± 0.4	<0.0001
Hemoglobin (g/dL)	11.9 ± 1.5	12.4 ± 1.4	12.2 ± 1.5	<0.01
44-hour ambulatory systolic BP (mmHg)	138.7 ± 20.4	133.6 ± 20.8	136.1 ± 20.7	0.04
44-hour ambulatory diastolic BP (mmHg)	78.5 ± 14.3	77.7 ± 14.3	78.1 ± 14.2	0.6

± indicates standard deviation. Parenthesis have percent of patients.

Continuous variables p-values computed through ANOVA

Categorical variables p-values computed through Pearson’s Chi-Square

Median follow up was 30 months (inter-quartile range 14 – 54 months) with the longest follow up of 6.5 years. During this follow up period 96 (31%) patients died. The crude mortality rate was 112/1000 patient-years. Figure 1 shows the Kaplan-Meier survival curves depicting the relationship between all-cause mortality and median of ultrafiltration volumes. Survival curves between ultrafiltration volumes dichotomized at the median were not found to be of prognostic importance. Similarly survival based on UF volume/kg or UF rate/kg post-dialysis weight were not of prognostic value (Table 2). Figure 2 shows the Kaplan-Meier survival curves depicting the relationship between all-cause mortality and median of RPV slopes. Survival curves between RPV slopes dichotomized at the median were found to be prognostically important.

Figure 1.

Kaplan Meier survival curves for ultrafiltration volume and mortality. The log rank test demonstrated no significant difference in survival between medians of ultrafiltration volume.

Table 2.

Hazard Ratios for all-cause mortality by median of UF volume or RPV slope

Parameter	HR	95 % CI	p value	Model fit (Chi2)
UF volume	0.78	0.51 to 1.17	0.23	1.45
UF volume/post dialysis weight	0.79	0.52 to 1.19	0.25	1.3
UF rate/post dialysis weight	0.89	0.59 to 1.34	0.58	0.6
RPV slope	1.72	1.14 to 2.58	0.01	6.85

Hazard ratio is for upper half compared to lower half. Relative plasma volume (RPV) slope compares the HR of flatter to steeper slope

Figure 2.

Kaplan Meier survival curves for RPV slope and mortality. The log rank test demonstrated a significant difference in survival between medians of RPV slopes. Multivariable adjustments did not remove the statistical significance (see Table 2).

Table 2 and 3 shows the relationship between volume markers and mortality outcomes by medians RPV slopes. Compared to steeper RPV slope, a flatter RPV slope was associated with 1.72 higher hazard of mortality. The relationship between RPV slopes and the hazard ratio for all-cause mortality persisted to show significance even after multivariate adjustment for several risk factors. Since ultrafiltration volumes were significantly higher among patients with steeper RPV slopes, we created another model that adjusted for the ultrafiltration volume. Even this model (model 2) demonstrated that RPV slope remained a significant predictor of all-cause mortality. Since the impact of UF volume can differ between patients of dissimilar sizes, model 3 that accounted for the post-dialysis weight of the patient was created. This model increased the prognostic value of RPV slope. A model (model 4) that accounted for UF rate indexed for post dialysis weight was created. This model demonstrated that a high UF rate index was associated with increased mortality but if associated with flat RPV slope was associated with worse mortality. Finally, a model was created that accounted for all variables in model 4 but also contained the mean interdialytic 44-hour ambulatory systolic BP. In this BP adjusted model, the hazard ratio for mortality for RPV slope was 2.46 (95% CI 1.42 – 4.24, p<0.001).

Table 3.

Hazard Ratios for all-cause mortality by RPV slope

Parameter	RPV slope model 1			RPV slope model 2			RPV slope model 3			RPV slope model 4
	HR	95 % CI	p value	HR	95 % CI	p value	HR	95 % CI	p value	HR	95 % CI	p value
Flatter RPV slope	1.72	1.1–2.7	0.02	2.00	1.2–3.34	0.008	2.24	1.34–3.74	0.002	2.55	1.49–4.34	0.001
Age	1.03	1.01–1.04	0.002	1.02	1.01–1.04	0.006	1.02	1.01–1.04	0.005	1.03	1.01–1.04	0.004
Ethnicity
Black	0.33	0.18–0.63	0.001	0.35	0.18–0.67	0.002	0.37	0.19–0.71	0.003	0.38	0.2–0.73	0.003
Other	0.89	0.25–3.19	0.9	0.81	0.23–2.93	0.8	0.87	0.24–3.13	0.8	0.96	0.27–3.48	1
Men	1.56	0.97–2.51	0.1	1.66	1–2.74	0.05	1.62	0.99–2.67	0.1 <0.00	1.60	0.97–2.62	0.1
Cardiovascular disease	2.05	1.35–3.12	0.001	2.25	1.45–3.48	<0.001	2.30	1.48–3.58	1	2.41	1.54–3.75	<0.001
Antihypertensive medications	0.98	0.59–1.64	0.9	1.06	0.62–1.81	0.8	1.08	0.63–1.84	0.8	1.10	0.65–1.87	0.7
Dialysis vintage (years)	1.07	1.03–1.12	0.001	1.08	1.03–1.13	0.001	1.08	1.03–1.13	0.001	1.07	1.03–1.12	0.002
Albumin (g/dL)	0.58	0.36–0.94	0.03	0.48	0.29–0.81	0.006	0.48	0.28–0.8	0.005	0.48	0.29–0.79	0.004
Hgb (g/dL)	0.93	0.79–1.1	0.4	0.95	0.81–1.12	0.6	0.95	0.81–1.12	0.5	0.95	0.81–1.12	0.5
UF volume (L)				1.06	0.87–1.3	0.6
UF/Post dialysis weight (mL/kg)							1.01	1–1.03	0.1
UF Rate/Post weight (mL/kg/hr)										1.1	1.01–1.15	0.02

Models are adjusted for age, ethnicity, gender, serum albumin, hemoglobin, dialysis vintage, use of antihypertensive medications and pre-existing cardiovascular disease. Model 2 was adjusted for UF volume in liters, model 3 for UF volume/kg and model 4 for UFR/kg. To calculate the adjusted HR, continuous variables were centered at mean.

Discussion

The results of this study demonstrate the following: 1) RPV slope measurements are of prognostic significance; 2) ultrafiltration volume alone, UF volume/kg or UF rate indexed for post dialysis weight are of no prognostic significance; and 3) the prognostic relationship of RPV slope to mortality is independent of conventional and unconventional cardiovascular risk factors including ultrafiltration volume, UF volume/kg and UF rate indexed for post dialysis weight and interdialytic ambulatory BP.

Epidemiologic studies have noted that high interdialytic weight gain to be associated with higher mortality ⁷. Another study noted high interdialytic weight gain to be associated with all-cause and cardiovascular mortality ⁸. In unadjusted analyses, we were unable to discover a relationship between interdialytic weight gain and all-cause mortality. This apparently discrepant finding could be due to several reasons. First, epidemiologic studies that report unadjusted relationship of interdialytic weight gain and all-cause mortality find only a marginal relationship (p=0.05) ⁷. This relationship strengthens only after multivariate adjustment. Second, in our study, patients who were recently hospitalized or sick were excluded. Thus our study differed in its recruitment criteria compared to epidemiologic studies which have analyzed all patients in the dialysis unit regardless of their level of illness. In fact, we believe that recent hospitalizations often provokes loss in lean body mass and relative volume excess that may be clinically undetectable. Had we recruited patients who had undergone recent hospitalization, the relationship between RPV slopes and mortality may have been even stronger.

Saran et al have reported among participants in the Dialysis Outcomes and Practice Patterns Study (DOPPS) that UF rate indexed for post-dialysis weight > 10 ml/h/kg was associated with a higher risk of mortality (RR = 1.09; P = 0.02). In a multivariate model (model 4), we confirmed and extend these observations. We found, that RPV slopes were of prognostic value above and beyond UF rate. We interpret this model to mean that patients with higher UF rates have an increased mortality. However, if they have a concomitant flatter slope they have even a higher mortality.

Among pediatric hemodialysis patients, RPV monitoring has been used to guide dry-weight reduction; this results in lower interdialytic ambulatory BP ^21,22 and reduces the rate of hospitalizations ¹⁶. Similar mechanisms are likely to operate among adults. We speculate that the steeper RPV slopes were associated with lower mortality because patients achieve a more euvolemic state and have less mortality cardiovascular causes. In fact, at least 5 groups of investigators have demonstrated the value of RPV monitoring to establish dry-weight. These investigations are as follows: Lopot et al who were among the first to suggest that RPV monitoring may be valuable in the assessment of dry-weight⁶. They reported that RPV monitor-guided reduction in dry-weight reduced echocardiographic inferior vena cava diameter among patients who were found to be volume overloaded. Rodriguez et al reported in a cohort study of 28 patients that RPV monitoring lead to changes in dry-weight in all patients ²³. Steuer et al reported that 18% of the patients in a dialysis unit had less than 5% reduction in relative blood volume ²⁴. Over 6 weeks, they reduced the weight by an average 0.8kg which resulted in larger decrease in relative blood volume with low incidence of symptoms²⁴. Dasselaar et al who evaluated the role of blood volume tracking compared to standard therapy in the management of hypertension in hemodialysis patients by reducing dry-weight ²⁵. They reported that among 14 patients randomized to blood volume tracking-guided dry-weight reduction predialysis BP was reduced by 22.5/8.3 mmHg; ECF water and cardiothoracic ratio were also reduced. Sinha et al demonstrated that RPV monitoring can assist in the assessment of dry-weight in the context of a randomized controlled trial ¹⁷. Among participants in the dry-weight reduction in hypertensive hemodialysis patients (DRIP) trial ²⁶, RPV monitoring was performed at baseline and at 8 weeks. The intervention group of 100 patients had dry-weight probed whereas 50 patients served as time controls. Probing dry-weight in these patients lead to steeper slopes; those with flatter slopes at baseline had greater weight loss. Both baseline relative plasma volume slopes and the intensity of weight loss were found to be important for subsequent change in RPV slopes. Most important, RPV slopes predicted the subsequent reduction in interdialytic ambulatory systolic BP; those with the flattest slopes had the greatest decline in BP upon probing dry-weight.

There is one notable exception, a randomized trial, which demonstrated that RPV-guided therapy was associated with worse outcomes; this deserves comment. In a multi-center Crit-Line Intradialytic Monitoring Benefit (CLIMB) study ²⁷, 227 hemodialysis patients were randomized to RPV monitoring and 216 to conventional monitoring for 6 months to test the hypothesis that RPV-guided monitoring would mitigate hospitalization rates. Hospitalization occurred 1.53 times per year in the RPV-guided monitoring group and 1.03 times per year in the conventional group. Mortality was 8.7 % and 3.3 % (p=0.021) in the RPV-guided monitoring and conventional monitoring group respectively. An elaborate protocol was available to guide fluid management based on RPV-guided monitoring. The investigators state, “Algorithm use was encouraged but not mandated, in contrast to earlier studies. This design was intended to assess the therapeutic efficacy of Crit-Line in a trial that permitted voluntary nonuse of the information from the device …. Therefore, Crit-Line was studied as a voluntary adjunct to care.” Furthermore, they state, “highly variable implementation of the monitoring and interventional algorithm occurred within and across dialysis units; the causes were not collected.” Uncertain adherence to the protocol by the investigators makes it difficult to conclude that RPV-guided monitoring was a cause of higher complication rates. In fact, at baseline, as determined by RPV slope patterns, patients in the conventional group were more volume overloaded compared to the RPV-guided group. At 6 months, both groups had similar RPV slopes. In other words, the conventional group appeared to have had greater volume challenge than the intervention group. Since this study appears to be more observational than interventional because of uncertain adherence to protocol a more valid evaluation of this technology would have been to compare the RPV slope data as we present in our analysis.

There are several strengths and limitations of our work. As with any cohort study this study cannot establish a cause and effect relationship between volume and mortality. Our study had few white people and we excluded certain patients such as those with morbid obesity and atrial fibrillation. Whether the same results would hold in people of broader clinical characteristics is not known and will require verification in future cohorts. Although this is the largest outcome study related with RPV slopes among dialysis patients reported to date, the sample size of our study was still relatively small. RPV was measured at only one time point. Repeated measurements would answer the question whether changes in RPV slopes are prognostically informative. We only studied all-cause mortality; perhaps the value of RPV monitoring would be greater had we studied heart failure hospitalization. Residual renal function was not measured, but given similar vintage of dialysis between groups, it is unlikely that the residual renal function was markedly different between groups. Some strengths of our study are as follows: 1) we used multivariable adjustment to ascertain the independent effect of RPV slope on outcomes 2) we accounted for the UF volume and UF rates in our model and 3) demonstrated that RPV slopes are independent even from interdialytic ambulatory BP. Accordingly it is unlikely that the results of our study are simply because the RPV slopes were altered by an aggressive ultrafiltration profile.

Perspective

This study extends our earlier diagnostic test study which demonstrated that RPV slopes reflect volume excess. We now show that flatter RPV slopes that appear to detect hypervolemia among chronic hemodialysis patients are of prognostic value. We believe that next steps are to perform, in a broader group of chronic hemodialysis patients, randomized trials based on RPV slopes to assess heart failure and volume related hospitalizations. Among chronic dialysis patients, close attention to volume control has the potential to make a difference to the dismal cardiovascular mortality.