Calculated Plasma Volume Status and Prognosis in Chronic Heart Failure (original) (raw)

Journal Article

Hua Zen Ling ,

University College London Hospital NHS Trust

,

London

,

UK

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Julia Flint ,

University College London Hospital NHS Trust

,

London

,

UK

The Heart Hospital, University College London Hospital NHS Trust

,

Westmoreland Street, London, W1G 8PH

,

UK

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Morten Damgaard ,

Clinical Physiology and Nuclear Medicine, Koege Hospital

,

Koege

,

Denmark

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Peter K. Bonfils ,

Clinical Physiology and Nuclear Medicine, Koege Hospital

,

Koege

,

Denmark

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Adrian S. Cheng ,

Kettering General Hospital NHS Trust

,

Kettering

,

UK

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Suneil Aggarwal ,

University College London Hospital NHS Trust

,

London

,

UK

The Heart Hospital, University College London Hospital NHS Trust

,

Westmoreland Street, London, W1G 8PH

,

UK

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Shanti Velmurugan ,

The Heart Hospital, University College London Hospital NHS Trust

,

Westmoreland Street, London, W1G 8PH

,

UK

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Michelle Mendonca ,

University College London Hospital NHS Trust

,

London

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UK

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Mohammed Rashid ,

University College London Hospital NHS Trust

,

London

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UK

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Swan Kang

University College London Hospital NHS Trust

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London

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UK

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Revision received:

24 August 2014

Accepted:

05 September 2014

Published:

03 December 2014

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Hua Zen Ling, Julia Flint, Morten Damgaard, Peter K. Bonfils, Adrian S. Cheng, Suneil Aggarwal, Shanti Velmurugan, Michelle Mendonca, Mohammed Rashid, Swan Kang, Francesco Papalia, Susanne Weissert, Caroline J. Coats, Martin Thomas, Michael Kuskowski, Jay N. Cohn, Simon Woldman, Inder S. Anand, Darlington O. Okonko, Calculated Plasma Volume Status and Prognosis in Chronic Heart Failure, European Journal of Heart Failure, Volume 17, Issue 1, January 2015, Pages 35–43, https://doi.org/10.1002/ejhf.193
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Abstract

Aims

Plasma volume (PV) expansion hallmarks worsening chronic heart failure (CHF) but no non-invasive means of quantifying volume status exists. Because weight and haematocrit are related to PV, they can be used to calculate relative PV status (PVS). We tested the validity and prognostic utility of calculated PVS in CHF patients.

Methods and results

First, we evaluated the agreement between calculated actual PV (aPV) and aPV levels measured using 125Iodine-human serum albumin. Second, we derived PVS as: [(calculated aPV – ideal PV)/ideal PV] × 100%. Third, we assessed the prognostic implications of PVS in 5002 patients from the Valsartan in Heart Failure Trial (Val-HeFT), and validated this in another 246 routine CHF outpatients. On analysis, calculated and measured aPV values correlated significantly in 119 normal subjects and 30 CHF patients. In the Val-HeFT cohort, mean (+SD) PVS was –9 ± 8% and related to volume biomarkers such as brain natriuretic peptide (BNP). Over 2 years, 977 (20%) patients died. Plasma volume status was associated with death and first morbid events in a ‘J-shaped’ fashion with the highest risk seen with a PVS > –4%. Stratification into PVS quartiles confirmed that a PVS > –4% was associated with increased mortality (unadjusted hazard ratio 1.65, 95% confidence interval 1.44–1.88, χ2 = 54, P < 0.001) even after adjusting for 22 variables, including brain natriuretic peptide. These results were mirrored in the validation cohort.

Conclusions

Relative PVS calculated from simple clinical indices reflects the degree to which patients have deviated from their ideal PV and independently relates to outcomes. The utility of PVS-driven CHF management needs further evaluation.

Introduction

Chronic heart failure (CHF) is the final common pathway of a myriad of insults to the myocardium that eventuate in circulatory congestion and debilitating symptoms.1 Despite significant therapeutic advances, aggregate 5-year mortality rates remain high at approximately 50%, which rivals and even exceeds that of many cancers.2 Therefore, the development of novel prognostic tools with which to identify CHF patients in persistent need of escalated care remains clinically important.

Plasma volume (PV) expansion is a hallmark feature of worsening CHF that is notoriously difficult to quantify non-invasively.3-5 It may arise from excessive neurohormonal activation and can manifest with peripheral and pulmonary edema.5,6 Greater degrees of congestion relate to greater morbidity and mortality,7,8 and the relief of congestion is a fundamental goal of therapy. Despite this, limited means of quantifying PV in CHF patients exists. Radioisotope assays optimally measure PV, but are expensive and clinically impractical.9 Symptoms and signs can reflect volume overload, but are only of use when congestion is overt, and even then can be poorly reproducible.10 Echocardiography and implantable devices can assess volume status but have several limitations.11-13 While daily weights reliably reflect daily PV fluctuations, they are less informative over longer periods and are only truly of use when the patient’s ‘dry weight’ is known.14 A non-invasive index that can simply quantify the degree to which patients have deviated from their ideal PV (iPV) would be of obvious clinical importance and of potential prognostic utility.

Plasma volume is intimately related to weight and haematocrit and its actual (aPV) level can be calculated from validated equations predicated on these simple indices.15 A weight-based formula also exists with which to estimate iPV.16 Consequently, we hypothesized that the subtraction of calculated iPV from aPV to derive relative PV status (PVS), an index of deviation from individual euvolaemia, could be of prognostic utility in stable CHF patients. To test this contention, we performed a logical sequence of analyses. First, we assessed the agreement between calculated and directly measured aPV levels in healthy subjects and CHF patients. Second, we evaluated the association of calculated PVS to outcomes in 5002 patients randomised in the Valsartan in Heart Failure Trial (Val-HeFT).17 Third, we validated our prognostic results in an accrued cohort of routine CHF outpatients.

Methods

Plasma volume validation cohort

The agreement between paired calculated and measured aPV levels was assessed in otherwise healthy subjects who had had normal haematological investigations, including routine PV measurements, at Hammersmith Hospital, London, UK, from 2005 to 2011, and in stable CHF outpatients recruited into previously published prospective studies conducted at Koege Hospital and Bispebjerg Hospital, Copenhagen, Denmark from 2002 to 2008.18,19 These studies were approved by the local research ethics committee and patients gave written informed consent. The diagnosis of CHF was based on the presence of appropriate symptoms and signs and a left ventricular ejection fraction (LVEF) ≤45%.20

Plasma volume measurement

Actual PV was measured using the gold standard 125Iodine labelled human serum albumin (125I-HSA) method as described elsewhere.9 In brief, after patients had lain supine for 30 min, an initial blood sample was drawn and 185–200 KBq of 125I-human serum albumin (HSA) injected through a cubital venous catheter. Blood samples were then collected at regular intervals up to 60 min from the contralateral arm and the transcapillary escape rate of albumin was calculated. All participants received a thyroid blocking dose of 400 mg potassium iodide orally before the injection.

Plasma volume equations

Actual PV was calculated with the following equation, which has been previously validated,15 and was derived by curve-fitting techniques using subjects’ haematocrit and weight compared with directly measured PV values:

aPV=1–haematocrit×a+b×weightinkg

where haematocrit is a fraction, a = 1530 in males and a = 864 in females, and b = 41 in males and b = 47.9 in females.

The ideal PV was calculated from the following well established formula based solely on weight:16

where c = 39 in males and c = 40 in females

Relative PVS, an index of the degree to which patients have deviated from their iPV, was subsequently calculated from the following equation:

Survival cohorts

The relation between calculated PVS and outcomes was evaluated initially in patients enrolled in the Val-HeFT trial.17 The design and primary results of Val-HeFT have been reported previously.17 Briefly, Val-HeFT was a randomised, placebo-controlled, double-blind, multicenter trial that enrolled 5010 men and women with symptomatic heart failure (HF) and a reduced LVEF to evaluate the efficacy of the angiotensin receptor blocker valsartan. The study had two primary end points: mortality and the first morbid event, defined as death, sudden death with resuscitation, hospitalization for HF, or administration of intravenous inotropic or vasodilator drugs for >4 h without hospitalization. Of the 5010 patients randomised in Val-HeFT, 5002 had data for baseline PV calculations, and 4404 had data for repeat PV calculations at 4 months post randomization.

The results of survival analyses performed using the Val-HeFT cohort were then validated in a routine sample of patients with CHF and a reduced LVEF who were being followed in the outpatient departments of University College, London, and The Heart Hospital, London, UK.

Statistics

Data are presented as mean ± SD, frequencies (%), or medians (interquartile range). Intergroup comparisons were made using Student’s _t_-test, Pearson’s χ2-test, or a Mann–Whitney _U_-test as appropriate. Relations between variables were assessed using Pearson’s or Spearman’s rank correlation depending on data distribution. The agreement between paired measured and calculated aPV values was evaluated according to Bland and Altman with plots of differences against means.21 Lack of agreement between the methods was summarized by calculating the bias (mean difference) and limits of agreement (bias ± 1.96 SD).

The association between all baseline covariates and all-cause death, first morbid events, and hospitalizations for CHF were determined using Cox proportional hazards analyses. Significant univariate predictors of outcome were subsequently entered into multivariate models. The proportional hazard assumption was assessed by inspection of the log time–log hazard plot for all covariates. The significance levels for chi-square (likelihood ratio test) were calculated. Kaplan–Meier cumulative survival plots were constructed for display and assessed using the log-rank test. The relation between temporal changes in a variable and outcomes were evaluated by time-dependent Cox analyses.

Calculated PVS was assessed as both a continuous and categorical variable. To fully investigate the relation between baseline PVS and survival, patients were initially subdivided according to 2% increments of PVS and the hazard ratios for each group calculated. At the extremes of PVS with few patients, subjects were pooled to generate sufficient numbers for comparisons. This analysis demonstrated that the association between PVS and outcomes was non-linear. To simplify the prognostic associations, patients were stratified into quartiles of PVS. This revealed that subjects in the lower three quartiles had similar hazard ratios so they were combined into one group (quartiles 1–3) and compared with those in the highest PVS quartile (quartile 4).

The relation between baseline PVS and outcomes were assessed in univariable models and in multivariable models adjusting for brain natriuretic peptide (BNP), and for 21 univariate predictors including BNP, age, sex, New York Heart Association (NYHA) class, heart rate, systolic blood pressure, LVEF, body mass index, levels of sodium, albumin, estimated glomerular filtration rate and uric acid, cause of HF, presence of orthopnoea, history of hypertension, history of diabetes, and the use of beta-blockers, diuretics, digoxin, spironolactone, and angiotensin-converting enzyme (ACE) inhibitors. Owing to the potential influence of collinearity between PVS and haematocrit/haemoglobin, a separate model adjusting for the above 21 variables and for haematocrit or haemoglobin was constructed. The relation of change in PVS from baseline to 4 months was assessed using a time-dependent Cox analysis.

Data were analysed using SPSS (version 12.0, SPSS Inc, Chicago, IL, USA), Stata (version 10.0, Stata Corp, College Station, TX, USA), and Statview (version 5; SAS Institute Inc., Cary, NC). A two-tailed _P_-value <0.05 was considered statistically significant. No adjustments for multiple statistical comparisons were made.

Results

Agreement between calculated and measured plasma volumes

One hundred and nineteen healthy subjects (mean age 52 ± 15 years, 70% male) had routine outpatient PV assessments (Figure 1A). Their measured aPV was 2475 ± 438 mL (32 ± 6 mL/kg) and was higher but correlated to calculated aPV (r = 0.68, P < 0.0001) with a bias of –78 mL (–1 ± 4 mL/kg). In 30 stable male CHF outpatients (age 64 ± 10 years, LVEF 32 ± 9%, 33% NYHA class ≥3) who were all on an ACE-inhibitor/angiotensin receptor blocker and a beta-blocker (60% on diuretics), measured aPV was 3299 ± 589 mL (38 ± 7 mL/kg) and was also higher but correlated to calculated aPV (r = 0.51, P = 0.006) with a bias of –281 mL (–3 ± 7 mL/kg) (Figure 1B). Median difference between calculated and measured aPV was –184 mL (–2 mL/kg). On Spearman rank correlation, weight (r = 0.60, P = 0.001), but not haematocrit (r = 0.09, P = 0.63), related to measured aPV.

Figure 1

Agreement of plasma volume methods. Correlation and Bland–Altman plot for calculated and measured current plasma volume levels in normal healthy subjects (A) and patients with chronic heart failure (B).

Calculated PVS in the Val-HeFT cohort and its relation to volume biomarkers

The baseline characteristics of the 5002 Val-HeFT patients stratified by PVS are shown in Table 1. Their mean calculated aPV, iPV, and PVS were 2778 ± 423 ml (35 ± 4 mL/kg), 3099 ± 590 ml (39 ± 1 ml/kg), and –9 ± 8%, respectively. A PVS equal to, greater than, or less than 0 was evident in 108 (2%), 606 (12%)l and 4288 (86%) patients, respectively (Figure 2).

Table 1

Baseline characteristics of 5002 Valsartan in Heart Failure Trial (Val-HeFT) patients stratified by plasma volume status (PVS)

NYHA, New York Heart Association; MLHFQ, Minnesota Living with Heart Failure Questionnaire; LVEF, left ventricular ejection fraction; LVIDD/BSA, left ventricular internal diastolic diameter corrected for body surface area; eGFR, estimated glomerular filtration rate; BNP, brain natriuretic peptide; NT-proBNP, _N_-terminal pro-brain natriuretic peptide; CRP, C-reactive protein; ACE, angiotensin-converting enzyme inhibitor.

Table 1

Baseline characteristics of 5002 Valsartan in Heart Failure Trial (Val-HeFT) patients stratified by plasma volume status (PVS)

NYHA, New York Heart Association; MLHFQ, Minnesota Living with Heart Failure Questionnaire; LVEF, left ventricular ejection fraction; LVIDD/BSA, left ventricular internal diastolic diameter corrected for body surface area; eGFR, estimated glomerular filtration rate; BNP, brain natriuretic peptide; NT-proBNP, _N_-terminal pro-brain natriuretic peptide; CRP, C-reactive protein; ACE, angiotensin-converting enzyme inhibitor.

Figure 2

Distribution of plasma volume status (PVS) in the Valsartan in Heart Failure Trial (Val-HeFT)17 cohort (n = 5002).

On stratifying patients into PVS quartiles, those in the highest quartile (PVS > –4%) were more likely to be older females with worse NYHA class and features of greater fluid retention including orthopnoea, paroxysmal nocturnal dyspnoea, a third heart sound, jugular venous distension, lower serum albumin, and higher BNP than patients in the lower three quartiles combined (PVS ≤ –4%; Table 1). These patients also had more neurohormonal activation (higher noradrenaline and aldosterone) higher C-reactive protein and troponin levels, greater diuretic and digoxin use, but lower use of ACE-I/ARB and beta-blockers. On Pearson’s correlation, calculated PVS related to established volume biomarkers such as albumin (r = –0.21), BNP (r = 0.21), estimated glomerular filtration rate (r = –0.14), sodium (r = –0.01) and creatinine (r = 0.12) (P < 0.01 for all).

Calculated PVS and outcomes in the Val-HeFT cohort

After a median follow up of 716 (521–869) days, 977 (20%) patients died, 1522 (30%) experienced a first morbid event and 696 (13.9%) were hospitalized for worsening CHF. Baseline PVS as a continuous variable was not associated with death. To demonstrate whether this resulted from a non-linear relation between PVS and mortality, patients were stratified into 2% increments of PVS. This analysis revealed a ‘J-shaped’ relation between PVS and death (Figure 3). A significantly higher risks was seen in the subgroup of patients with PVS > –4% or < –25% compared with the group with PVS between –12% and –13.9% who had the lowest risk (referent group).

Figure 3

Calculated plasma volume status (PVS) and death. Unadjusted hazard ratios for patient groups stratified by 2% increments of PVS.

Further analysis based on baseline PVS quartiles confirmed the non-linear relation (Figure 4A) with similar risks of death in the lower three quartiles and the highest risk of death [unadjusted hazard ratio (HR) 1.65, 95% confidence intervals (CI) 1.44–1.88, P < 0.001] in the upper quartile (PVS > –4%) compared with quartile 2 (referent). Because the risks in the lower three quartiles were similar, they were grouped together for subsequent analyses (Figure 4B). Adjustment for BNP alone (HR 1.35, 95% CI 1.17–1.56, P < 0.001) and for 21 clinical variables including BNP did not alter the association with death (HR 1.26, 95% CI 1.05–1.52, _P_ = 0.01). Addition of haematocrit (HR 1.40, 95% CI 1.12–1.75, _P_ = 0.003) or haemoglobin (HR 1.27, 95% CI 1.02–1.59, HR0.03) to a model including the 21 baseline variables and PVS cut-off, did not alter the prognostic association of PVS. Similar association was also seen between the highest quartile of PVS (>–4%) and first morbid event and CHF hospitalizations (Fig 4C,D). However, only the association with first morbid events remained significant after full covariate adjustment.

Figure 4

Calculated plasma volume status (PVS) quartiles and prognosis. Relation of PVS to death (A,B), first morbid events (C) and hospitalizations for heart failure (D). Q, quartile. CHF, chronic heart failure.

The median change in PVS from baseline to 4 months was 0.8% (interquartile range 6%). Change in PVS over time was related significantly and independently to the risk of death (HR 1.08, 95% CI 1.02–1.14, P = 0.005; Figure 5) and first morbid events (HR 1.05, 95% CI 1.00–1.10, P = 0.03), but not to CHF hospitalizations (HR 1.06, 95% CI 1.00–1.13, P = 0.06) after adjustment for 22 clinical variables, including baseline PVS.

Figure 5

Change in plasma volume status (PVS) over 4 months and mortality. Kaplan–Meier plot for patients divided into quartiles of change in PVS from baseline to 4 months.

Calculated PVS and outcomes in the validation cohort

The baseline characteristics of the 246 ‘real world’ CHF patients (age 68 ± 12 years, 72% male, LVEF 28 ± 8, 42% NYHA class ≥3) stratified by PVS are shown in Table 2. Their mean aPV(c), iPV, and PVS values were 2882 ± 581 mL (36 ± 5 mL/kg), 3196 ± 877 mL (39 ± 1 mL/kg), and –8 ± 12%, respectively. In this validation cohort, 181 (74%) had hypovolaemia (PVS <0) and 65 (26%) patients had hypervolaemia (PVS >0). Calculated PVS was again related to PV biomarkers such as albumin (r = –0.33, P < 0.0001) and estimated glomerular filtration rate (r = –0.23, P = 0.0003).

Table 2

Baseline characteristics of 246 validation patients stratified by plasma volume status (PVS)

NYHA, New York Heart Association; LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate; ACE, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Table 2

Baseline characteristics of 246 validation patients stratified by plasma volume status (PVS)

NYHA, New York Heart Association; LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate; ACE, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Over a median follow-up of 387 (195–682) days, 36 (15%) patients died. Calculated PVS was independently associated with mortality as a continuous variable (unadjusted HR 1.04, 95% CI 1.02–1.07, P = 0.0005), and a PVS > –4% (vs. ≤–4%) remained significantly related to death (unadjusted HR 2.65, 95% CI 1.37–5.11, P = 0.004; Figure 6) after statistical adjustment in varying tri-variable Cox models.

Figure 6

Calculated plasma volume status (PVS) and mortality in the validation cohort. Kaplan–Meier plot for patients divided into PVS quartile 4 ( >–4% ) and quartiles 1–3 (≤–4%).

Discussion

Quantifying the degree to which patients have deviated from their iPV is intuitively attractive, particularly in disorders where PV expansion portends worse outcomes and predates clinical congestion.4,7,8 In a logical sequence of analyses we probed the validity and prognostic utility of a mathematical index of relative PVS in stable patients with CHF and a reduced LVEF. We found that (i) actual plasma volumes calculated from weight and haematocrit mirror those measured using the gold standard 125I-HSA assay validating their use to derive PVS; (ii) volume status, as estimated by the PVS equation, is contracted in stable CHF patients and correlated to other markers of intravascular filling; (iii) calculated PVS is associated with death and first morbid events in a ‘J-shaped’ fashion with the highest risk seen with values > –4%; (iv) calculated PVS relates to death and morbidity even after adjustment for a robust set of variables including BNP, and is prognostic in selected and unselected CHF cohorts. We put forward this equation as a simple non-invasive measure of volume status that could potentially facilitate CHF management.

Together with dyspnoea and exercise intolerance, PV expansion is a cardinal feature of CHF that is primarily driven by excessive neurohormonal activation, and can give rise to systemic and pulmonary congestion by elevating intracardiac filling pressures.1,3,5,6 It is congestion and not a low cardiac output that drives symptoms and hospitalizations in most CHF patients,4 and its resolution is a marker of treatment success. However, while tracer dilution methods for quantifying PV have long existed,9 relatively few studies have applied them in CHF patients, and none have examined the utility of equations for estimating PV in these individuals.

Plasma volume status was contracted in our stable CHF patients and their calculated aPV values were remarkably similar to those objectively measured in most,18,19,22,23 but not all,24 previous studies. Using Evans blue dye, Feigenbaum et al. 23 found PV to be contracted by 23% in stable CHF patients compared with controls. Using 125I-HSA, Damgaard et al. 18 and Bonfils et al. 19 found it contracted by 8%. More reassuringly, the mean aPV levels measured by these authors (34 ± 12.9 mL/kg and 37 ± 6 mL/kg) and by Abdlebrect et al. 24 (37 ± 4 mL) were similar to those we calculated for our Val-HeFT (35 ± 4 mL/kg) and validation cohort (36 ± 5 mL/kg). This striking consistency, coupled with our finding of an agreement between calculated and measured aPV values, further add to the validity of our mathematical approach, which is strengthened by the correlations seen between calculated PVS and other volume biomarkers.

Plasma volume expansion, whether measured directly25 or inferred from surrogates of congestion, such as elevated pulmonary capillary wedge pressures,26 echocardiographic indices of raised intracardiac filling,27 jugular venous distension, a third heart sound,7 congestive symptoms,8 or volume biomarkers,28 consistently predicts worse outcomes in patients with CHF. However, because such surrogates have several limitations, simpler and potentially more direct measures of PVS are desirable.

Relative PVS, calculated from simple readily available indices (weight and haematocrit), predicted death and first morbid events independently of 22 clinical variables, including BNP, in this study. Moreover, there was a ‘J-shaped’ relation with mortality and morbidity, consistent with the observation that both dehydration, via myocardial hypoperfusion, and congestion, via myocardial oedema, can worsen the failing myocardium.4 Although the deleterious effects of dehydration and congestion on cardiovascular and non-cardiovascular tissues may be multifactorial, the relation between PVS and outcomes might merely reflect the prognostic implications of weight and haematocrit, or other factors in CHF that modulate them. Nevertheless, PVS remained related to outcomes even after extensive covariate adjustment in the Val-HeFT dataset.

Stratification of patients by 2% increments of PVS revealed that those with values between –4% and –25% had the best outcomes. This raises the possibility that titrating CHF therapies to keep PVS within this range might be clinically useful and supports the findings that diuresis to achieve haemoconcentration during heart failure decompensations is associated with a reduced risk of mortality, despite an increased risk of worsening renal function.29 Future prospectively designed studies are required to confirm these findings.

In addition to its uniqueness, our study has several strengths. First, we were able to show agreements between calculated and measured aPV levels in normal subjects and in patients with CHF. Second, the prognostic utility of PVS was derived from a large well-characterised population (Val-HeFT) and validated in an additional unselected cohort of CHF patients. Third, our PVS equation relies on only two easily available indices and provides quick information on the aspect of PVS that is most clinically important—its relative and not absolute level.

This study has some limitations. First, the agreement between calculated and measured aPV levels was appraised only in male CHF patients. Future studies in both genders are needed to derive CHF-specific equation constants that would optimise the formula for use in all patients. Second, consistent with the fact that no PV equation can completely mirror true PV levels, calculated aPV values underestimated measured aPV levels here, particularly in CHF patients. This likely relates to the smaller sample size of the CHF population and larger cohorts will be needed to address this in future. Third, correlations between PVS and other volume biomarkers were modest. Fourth, although we found an association between PVS and mortality, no causal link between both has yet to be established in CHF. Despite these limitations, we believe that our study has potential clinical ramifications.

Plasma volume expansion principally drives symptoms and decompensations in CHF, and its estimation using our equation could facilitate disease management. First, the formula could be utilised to make objective decisions about the need for diuretics, and diuretic and non-diuretic therapy titrated to keep PVS within an optimum range. Because PV expansion precedes clinical congestion, the titration of therapy to estimated PVS could be particularly useful in preventing symptoms in asymptomatic patients. Thus, impending decompensations could be detected, hospitalizations averted, and mortality potentially reduced. Third, the PVS equation could be of marked utility during decompensations, not just to guide decongestive therapy but to identify those with residual subclinical congestion who may be unsafe for discharge, and those with a contracted PVS despite tissue oedema who may benefit from inotropes as well as diuretics. Because the data presented here are largely hypothesis generating, prospective studies are needed to fully appraise the clinical potential of estimated PVS.

In summary, plasma volume status calculated simply from weight and haematocrit relates to objectively measured plasma volumes, and independently predicts death and first morbid events in CHF. A PVS > –4% was associated with the worst prognosis. Mathematical estimation of volume status is a valid concept in CHF and our study constitutes a critical point from which prospective analyses can evolve.

Funding

Conflict of interest: I.S.A. received honoraria for work on the Steering committee of Val-HeFT. All other authors report no potential conflict of interest.

Acknowledgements

We thank Charles Cotton, previously of the Department of Nuclear Medicine at Hammersmith Hospital, for access to the radiolabelled plasma volume data.

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© 2014 The Authors. European Journal of Heart Failure © 2014 European Society of Cardiology