WO2016177796A1

WO2016177796A1 - Method for estimating a plasma volume and applications thereof

Info

Publication number: WO2016177796A1
Application number: PCT/EP2016/060031
Authority: WO
Inventors: Patrick ROSSIGNOL; Faiez Zannad; Jean-Marie MONNEZ; Kévin DUARTE; Eliane ALBUISSON
Original assignee: INSERM (Institut National de la Santé et de la Recherche Médicale); Université De Lorraine; Centre Hospitalier Et Universitaire De Nancy (Chu); Centre National De La Recherche Scientifique (Cnrs)
Priority date: 2015-05-05
Filing date: 2016-05-04
Publication date: 2016-11-10

Abstract

A method for monitoring the evolution of congestive status of a patient previously hospitalised for congestive heart failure, comprising estimating in vitro a plasma volume in the patient, comprising the steps consisting of: i) collecting from the patient at time t1 a human physiological fluid sample suitable for measuring values of hemoglobin level and hematocrit level, ii) measuring values of hemoglobin level and hematocrit level from the human physiological fluid sample obtained at time t1, iii) estimating the plasma volume according to a formula I wherein hemoglobin represents the hemoglobin level measured on the sample and hematocrit represents hematocrit level measured on the sample, and iv) evaluating the evolution of congestive status of the patient from the value ePVS of the plasma volume obtained in step iii), by comparing this value to a reference value ePVSref specific of the patient, and applications thereof.

Description

Method for estimating a plasma volume and applications thereof

The present invention relates to a method for estimating a plasma volume in humans, devices for implementing the method and applications of the same.

Congestive heart failure is a medical condition in which the heart cannot pump enough blood to meet the body's needs. This inability may result in fluid retention, which causes swelling, for example, in the legs, feet, or abdomen. Congestive heart failure is the main cause or reason for the hospitalization.

Although recommended by current congestive heart failure (HF) management guidelines, long term diuretic use is still a matter of controversy, since it may be associated with worse prognosis and is not supported by large-scale randomized controlled studies. However, congestion is associated with worse outcome in patients with HF.

In outpatients, congestion is routinely evaluated with physical examination (jugular vein pressure, weight gain, edema, rales) which are imprecise and have poor sensitivity, or BNP or NT pro-BNP (B-type natriuretic peptide) measurements which are highly variable on a day-to-day basis and have specificity problems, influenced, also by kidney function (being defined as the glomerular filtration rate) and age.

A study named "Epierenone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study" (EPHESUS) evaluated the effects of epierenone (a mineralocorticoid receptor blocker diuretic compound) on morbidity and mortality among patients with acute myocardial infarction (Ml) complicated by left ventricular dysfunction and heart failure.

The EPHESUS study underlined that the addition of a low-dose of epierenone to standard medical therapy (including other diuretics) in patients with acute myocardial infarction and heart failure with left ventricular systolic dysfunction improved survival by 15%, with significant reductions in cardiovascular death, sudden death and hospitalization for heart failure. In addition to a variety of pleiotropic effects, epierenone exerts a diuretic, as well as a potassium-sparing effect (Rossignol P et al, Jacc 201 1 ).

The inventors have considered the results of this study and they have notably focused attention to know whether a diuretic effect (defined as a weight and a plasma volume variation) and/or a potassium-sparing (K-sparing) effect could be detected in patients treated with eplerenone in an EPHESUS substudy and, if any, whether these effects influenced cardiovascular outcomes.

The inventors have found that independently from eplerenone use, and without any significant interaction, estimated plasma volume depletion was consistently significantly associated with a 1 1 -19 % improvement in most of the tested cardiovascular outcomes (all- cause death, cardiovascular death or cardiovascular hospitalization, all-cause death or cardiovascular hospitalization, hospitalization for heart failure) but not to sudden death. Accordingly, they discovered that the assessment of the plasma volume variation is a good parameter for monitoring the progression of heart failure in a patient (Rossignol P et al, Jacc 201 1 )..

WO2012/172097 describes a method for determining a plasma volume variation in view of assessing the progression of a condition where plasma volume variation is representative of the condition or its evolution, particularly for determining whether a patient suffering from heart failure is at risk of having a cardiovascular event, comprising the steps consisting of:

i) measuring values of two or more human parameters such as hemoglobin level, hematocrit level, weight of a patient, etc. (P¹ , P², ..P^N) at time t1 wherein one or more of the human parameters

, P²n, ..P^Nti ) is obtained from a human physiological fluid sample, ii) measuring values of the same two or more parameters (Ρ¹ ₁₂, P²t2, --P t2) at time t2 different from time t1 , and

iii) determining the physiological human parameter wherein the physiological human parameter is a plasma volume variation AV between time t2 and time t1 from the values (P¹,i , P²,i , ..P^N,i , P¹ _t2, P²t2, ..P^N _t2) obtained at times t1 and t2.

iv) evaluating the evolution of heart failure in the patient from the plasma volume variation.

The variation of the plasma volume AV can be determined according to any mathematical formula well-known from a person skilled in the art, from the values obtained. Each time a practitioner wants to make the above medical assessment, two consecutive samples are taken, the latter being taken one day to 2 months after the former.

The plasma volume variation AV between time t1 and time t2 was particularly determined using hemoglobin level and hematocrit level as human parameters in combination with the Strauss formula given below (Strauss MB, Davis RK, Rosenbaum JD, Rossmeisl EC. Water diuresis produced during recumbency by the intravenous infusion of isotonic saline solution. J Clin Invest 1951 ;30:862-8 ; Kalra PR, Anagnostopoulos C, Bolger AP, Coats AJ, Anker SD. The regulation and measurement of plasma volume in heart failure. J Am Coll Cardiol 2002;39:1901-8):

, . . _{1 ΛΛ} hemoglobin (tl) I - hematocrit (tl)

Δν = lOO x — - x — - ! OO

hemoglobin (t2) I - hematocrit (tl)

This method implies obtaining two values for each parameter and therefore making several analysis.

Additionally, both values for each parameter are assessed at different times, one month after the first assessment according to the examples of WO2012/172097.

The inventors set themselves the task of simplifying the above method.

Now the inventors have discovered a new method which provides a quick result and additionally needs a single assessment of each parameter and therefore a single sample collection.

Ling et al. describe in the European Journal of Heart Failure (2015) 17, 35-43 relations between calculated plasma volume status and heart failure. However, Hakim's formula used by Ling has insufficiencies as will be shown hereafter in table 1 (no significant association with the cardiovascular outcomes). In fact, Hakim's formula takes into account the dry body weight of a patient, which is from practical point of view difficult to evaluate and thus unreliable in patients with heart failure because of their congestive status (with the associated fluctuations in the fluid retention).

A subject of the present application is therefore a method for monitoring the evolution of congestive status of a patient previously hospitalised for congestive heart failure, comprising estimating in vitro a plasma volume in the patient, comprising the steps consisting of:

i) collecting from the patient at time t1 a human physiological fluid sample suitable for measuring values of hemoglobin level and hematocrit level,

ii) measuring values of hemoglobin level and hematocrit level from the human physiological fluid sample obtained at time t1 ,

iii) estimating the plasma volume according to a formula I

1 - hematocrit

ePVS = x O.01 (I)

hemoglobin(g I cli )

wherein hemoglobin represents the hemoglobin level measured on the sample and hematocrit represents hematocrit level measured on the sample, and iv) evaluating the evolution of congestive status of the patient from the value ePVS of the plasma volume obtained in step iii), by comparing this value to a reference value ePVSref specific of the patient.

It should be noted that in the present application, in a standard fashion the indefinite article "a" must be considered as a generic plural (meaning "at least one" or also "one or more"), except when the context indicates the contrary (1 or "one only"). Thus, for example, when it says above that the method comprises the collection of a suitable human physiological fluid sample from a patient at time t1 , this refers to the collection of one or more samples at time t1 for example if a sample were damaged.

As previously mentioned, congestive heart failure is the main cause or reason for the hospitalization. The reference value ePVS_ref of the plasma volume value specific of the patient is a plasma volume value measured for instance when the patient is authorized by practitioners to leave the Hospital after hospitalisation for congestive heart failure, because his health no longer requires hospitalization, or anytime thereafter when the patient is evaluated for the first time. The reference value ePVS_rer is then used for later assessments. Accordingly, the reference value ePVS_ref of the plasma volume value is specific of a given patient.

Suitable fluid sample is whole blood. A single drop of sample, preferably of blood, may be sufficient.

According to the invention, means for determining hematocrit level or hemoglobin level at time t1 are for example a commercial apparatus such as HemoPoint® H2 Analyzer which, using a single drop of blood, offers results for hemoglobin and hematocrit (calculated) tests in a few seconds. The test for hematocrit as part of the i-STAT® system of Abbott provides hematocrit level and calculated hemoglobin level. The Chemistry Analyzer commercialized by Roche Diagnostics under the trade name Reflotron® Plus allows determination of 17 parameters from whole blood, serum or plasma. When implementing the invention, a device providing hemoglobin level and calculated hematocrit level, wherein said hematocrit level is calculated from said hemoglobin level, should not be used for avoiding inaccurate results for ePVS.

Accordingly, in contrast with the prior art, the present method provides accurate results very quickly, since the reference value is known and used for the further assessments, and accordingly a single sample is sufficient without waiting for the collection of a second sample. Therefore, under other conditions for the implementation of the invention, a device for performing the method for monitoring the evolution of heart failure in a patient comprising determining in vitro a plasma volume ePVS in a patient comprises:

- means for measuring values of hemoglobin level and hematocrit level from the human physiological fluid sample obtained at time t1 without calculating one of the values from the other; and

- means specially designed for automatically calculating the plasma volume according to formula I.

The means for automatically calculating the plasma volume according to formula I include the automatic use of formula I for the calculation.

The means for automatically calculating the plasma volume according to formula I further preferably include data concerning the reference value ePVS_ref specific of the patient.

The method and devices which are the subject of the present invention possess very useful properties. They make it possible to provide a reliable assessment of plasma volume.

Using devices such as HemoPoint® H2 Analyzer using a single drop of blood, and providing results for hemoglobin and hematocrit (calculated) tests in a few seconds in combination with calculating means including formula I above, allows a very rapid assessment of plasma volume and therefore of monitoring of the congestive status of heart failure in a patient.

These properties justify the use of the methods and devices described above, in a method for monitoring the progression of a condition where plasma volume ePVS is representative of the condition or of its evolution, such as congestive heart failure in a patient, and more particularly, for determining whether a heart failure post myocardial infarction patient having a treatment (in particular decongestive) is at risk of having a cardiovascular event such as death, cardiovascular death, cardiovascular hospitalization, hospitalization for heart failure.

The methods and devices described above also find a use particularly in a method for assessing the results of the treatment of different conditions such as hypertension, chronic kidney insufficiency, or cirrhosis i.e. diseases involving plasma volume, preferably involving plasma volume and potassium variation, and more preferably involving plasma volume and potassium and kidney function variations. The methods and devices described above find also a use in a method for assessing the results of the treatment of different non therapeutic conditions involving plasma volume.

This is why a further subject of the present application is also a method for determining the progression of a condition where plasma volume ePVS is representative of the condition or its evolution comprising the method steps defined above.

A preferred method is also a method for determining whether a patient suffering from heart failure, particularly after post myocardial infarction, is at risk of having a cardiovascular event comprising the method steps defined above.

According to the method, it is particularly possible to know whether a patient is at risk of having a cardiovascular event when the plasma volume ePVS is higher than said reference value ePVS_ref-

The methods and devices described above also find a use particularly in a method of treatment of congestive heart failure. The methods and devices described above allow the practitioner to easily determine whether fluid retention increases or decreases. The therapeutic treatment may be adjusted or instituted accordingly. When the skilled person is aware that the plasma volume ePVS has changed in comparison with the reference value, a modification in his treatment can be done or a treatment can be instituted. For example, if fluid retention increases, the dose regimen of a drug used should be increased, and conversely.

However, in order to change for example the dosage of drugs, or the amount of drug administered to a patient, for some conditions such as heart failure, it is interesting if the skilled person has also knowledge of the potassium or creatinine level (or both of course) of the patient. Under other preferred conditions, it is interesting if the skilled person has also knowledge of the potassium, creatinine level, BNP level or NT-ProBNP level of the patient.

Therefore, under preferred conditions for the implementation of the invention, the above method for determining the progression of a condition where plasma volume ePVS is representative of the condition or its evolution further comprises the step consisting of determining the potassium level of said patient and preferably the step consisting of comparing said potassium level to a reference value. This assessment helps particularly when a treatment comprises using K-sparing agents or K- depleting diuretics.

Under other preferred conditions for the implementation of the invention, the above method for determining the progression of a condition where plasma volume ePVS is representative of the condition or its evolution further comprises the step consisting of determining the creatinine level of said patient and preferably the step consisting of comparing said creatinine level to a reference value.

The assessment of the creatinine level is particularly interesting since it allows the determination of the glomerular filtration rate which itself allows estimating the kidney function being a strong predictor of mortality and morbidity in general populations as well as in heart failure patients. Indeed, Kidney function may influence both serum potassium levels, and drugs efficiency and side effects. It is therefore preferable to take its variations into account when adapting patient treatment.

Under other preferred conditions for the implementation of the invention, the above method for determining the progression of a condition where plasma volume ePVS is representative of the condition or its evolution further comprises the step consisting of determining the serum BNP level or serum NT-ProBNP level of said patient and preferably the step consisting of comparing said serum BNP level or serum NT-ProBNP level to a reference value.

The present invention is now illustrated with the following example.

DESCRIPTION OF THE DRAWINGS FIG. 1 represents ROC curves related to BNP and ePVS measurements at Month

1 .

Example

Figure 1 shows ROC curves related to BNP and ePVS measurements at Month 1 : At Monthl , 13 out of 14 patients with subsequent cardiovascular events had both BNP and ePVS > median and only 27% of patients with no event, while AUC was better with BNP and ePVS > median at M1 (AUC=0.83), compared to BNP or ePVS considered alone (AUC=0.76 with BNP and 0.72 with ePVS)

Accordingly, assessment of plasma volume is representative of the evolution of the condition and may be targeted for treatment adaptation, independently from BNP (or NT-proBNP). EXPERIMENTAL DATA

The analysis was performed in a subset of 4957 patients with available data (within a full dataset of 6632 patients: The EPHESUS study enrolled 6632 patients with HF following acute myocardial infarction (AMI) complicated by left ventricular systolic dysfunction (ejection fraction < 40%). HF had to be documented by at least one of the following: presence of pulmonary rales, chest radiography showing pulmonary venous congestion, or the presence of a third heart sound. Clinical signs of pulmonary congestion were not required at inclusion in patients with diabetes mellitus. Patients were entered into the study from 3 to 14 days post infarction (with inclusion (MO) performed pre-discharge in 80% of patients). All patients were randomly assigned to treatment with eplerenone 25 mg/day or placebo. EPHESUS was an event-driven study with a mean duration of follow-up of 16 months. Clinical assessments were made at inclusion (MO), at month 1 (M1 ), at month 3 (M3), and every three months thereafter. Among the 6632 patients included in the EPHESUS study, 1675 were excluded from the analysis because of unavailable data at baseline and/or at month 1 (259 died before 5 weeks and 1416 did not have the clinical and/or biological data required for all the analyses conducted in the present study). The present analysis was therefore performed on the 4957 remaining patients.).

Study endpoint was cardiovascular death and/or hospitalization for HF between month 1 and month 3 after post- Eplerenone Post-Acute Myocardial Infarction (AMI) Heart Failure (HF).

Estimated plasma volume variation (AePVS) between baseline month 0 and month 1 was estimated by the Strauss formula (WO2012/172097), which includes hemoglobin and hematocrit ratios. Other potential predictors including congestion surrogates, hemodynamic and renal variables, and medical history variables were tested.

An estimation of plasma volume, according to the present invention, ePVS M1 , was defined and also tested at month 1 (M1 ).

Estimation of change in plasma volume

To estimate relative changes in plasma volume (PV) between MO and M1 three different formulas were tested. The Strauss formula (AePVS) uses changes in hemoglobin and hematocrit concentrations between two time points, while the Kaplan and Hakim formulas respectively estimate instantaneous PV taking into account weight and hematocrit concentration at a given time point. See Kalra PR, Anagnostopoulos C, Bolger AP, Coats AJ, Anker SD. The regulation and measurement of plasma volume in heart failure. J Am Coll Cardiol. 2002;39:1901 -1908, Kaplan AA. A simple and accurate method for prescribing plasma exchange. ASAIO Trans. 1990;36:M597-599 and Hakim RM, Neyra R, Ismail N. Plasmapheresis. Handbook of dialysis.248:280. Results are provided in table 1 hereunder.

The only formula associated with cardiovascular events in this analysis was the Strauss formula, defined by:

AePVS = 100 x 100

hemoglobin(Ml) I - hematocrit M0)

According to the present invention, PV is estimated by using the following formula I

1 - hematocrit

ePVS ^■x O.01 (I)

hemoglobin (g / dL)

Variables

Measurements at M0 and M1 included ePVS, NYHA stage, KILLIP class (available at M0 only), weight, estimated glomerular filtration rate (eGFR) assessed by the MDRD formula (Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, Hogg RJ, Perrone RD, Lau J, Eknoyan G. National kidney foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann Intern Med. 2003;139:137-147) blood pressure (BP), hemoglobin and hematocrit concentrations, serum sodium, left ventricular ejection fraction (LVEF) (available at M0 only). AePVS and change in the continuous variables between M0 and M1 were also considered together with medical history (age, sex, race, previous hospitalization for HF, reperfusion therapy, previous AMI, diabetes, prior episodes of HF and hypertension). Owing to the number of missing values of albumin and serum protein at MO and M1 (25%), these variables were not considered in the present analysis. Both were associated with outcomes as well as albumin but not the change in serum protein in univariate analysis (data not shown).

Concise Statistical Analysis section

All analyses were performed using SAS version 9.1 .3 (SAS Institute, Cary, North Carolina) and R software (R Development Core Team, 2005). Continuous variables are described as median and interquartile range, and categorical data as proportions. The Chi-square test or Fisher's exact test was used for categorical variables and the nonparametric Kruskal-Wallis test for continuous variables. Correlations were obtained with Spearman's rho. The 2-tailed significance level was set to p < 0.05.

In order to select a set of predictors for multivariate analysis, a univariate analysis was performed to test the existence of a significant dependence between each of the initial variables and the two-class variable "event / non-event". A variable was retained if the corresponding p-value was smaller than 0.15 in order not to eliminate too many variables. Moreover, any variable highly correlated with another variable and with a less significant p-value was not retained.

To examine association with event, a stepwise logistic regression based on the remaining variables was used. This analysis automatically excluded insufficiently predictive variables. Prognostic gain of AePVS or ePVS was assessed by the integrated discrimination improvement (IDI), the continuous net reclassification improvement (NRI) and the increased area under ROC curve (IAUC). Stepwise discriminant analysis and linear discriminant analysis (LDA) were also performed to verify the stability of the set of retained variables (See Table 2 hereunder).

Variables retained by the model Coefficient F (Λ Wilks) P

NYHA 1 > 3 3.4337 78.27 <.0001 eGFR M1 -0.0331 21 .20 <.0001

KILLIP M0 > 3 1 .2138 12.84 0.0003

AePVS 0.0243 8.82 0.0030

LVEF M0 -0.0495 4.73 0.0297

Previous Hospitalization for HF 1 .4717 8.33 0.0039

Hypertension 0.8356 8.69 0.0032 Systolic BP M1 -0.0230 10.40 0.0013

Weight M1 -0.0195 4.75 0.0294

Table 2

p is the p- value associated to the Wilks lambda test. "Coefficient" stands for "Coefficient of the variable in the difference d^o d , ^■>

BP: blood pressure, HF: heart failure, eGFR: estimated glomerular filtration rate, AePVS: plasma volume variation estimated by the Strauss formula, LVEF: left ventricular ejection fraction, M0: baseline measurement, M1 : measurement at month 1 , NYHA: New York Heart Association functional class.

Furthermore, the quality and stability of all models were tested by cross- validation (See Table 3 hereunder).

Table 3

AePVS: plasma volume variation estimated by the Strauss formula, LR: logistic regression, LDA: linear discriminant analysis, AUC: area under ROC curve, Cr: criterion (1 -Se)² + (1 -Sp)², Res: Resubstitution, Se: sensitivity, Sp: specificity, Th^*: optimal threshold, VC4: 4 fold cross-validation, VC10: 10 fold cross-validation.

Finally, subgroup analyses were performed using a stepwise logistic regression: with and without anemia, anticoagulants, antithrombotic and reperfusion therapy at baseline. Anemia was defined according to the World Health Organization (WHO) criteria as a baseline hemoglobin < 13g/dL for men and < 12 g/dL for women. RESULTS

Comparison of the characteristics at baseline between included and non-included patients shows that the 1675 non-included patients generally had more severe HF (Table

4)

Values are expressed as medians [inter-quartile range] or proportions (%), where appropriate. ACEI: Angiotensin-converting enzyme inhibitor, AMI: acute myocardial infarction, ARB: angiotensin receptor blocker, BP: blood pressure, eGFR: estimated glomerular filtration rate, ePV: estimated plasma volume, HF: heart failure, LVEF: left ventricular ejection fraction, NYHA: New York Heart Association functional class. Baseline. 1 -month, and in-between features associated with cardiovascular events in univariate analysis

Patients with events (Table 5) were older and had a lower LVEF, weight and eGFR at baseline and M1 , as well as higher NYHA and KILLIP classes, lower hemoglobin and hematocrit concentrations.

Non-event Event

Variables

n=4697 n=260 P

NYHA MO > 2 70 77 0.013

NYHA MO > 3 16 34 <.0001

NYHA M1 > 2 66 81 <.0001

NYHA M1 > 3 13 37 <.0001

KILLIP M0 > 2 85 91 0.008

KILLIP M0 > 3 18 34 <.0001

Weight MO (kg) 78 [191 74[17] 0.003

Weight M1 (kg) 77 [19] 74 [171 0.0005

Δ Weight (kg) ^* 0 [3] -1 [3] 0.014

A ePVS (%) -2 [20] 0 [21 ] 0.0009 ePVS MO 4.478 [1 .189] 4.701 [1 .269] <.0001 ePVS M1 4.348 [0.978] 4.71 1 [1 .321 ] <.0001 eGFR MO (mL/min/1 .73 m²) 68 [26] 62 [26] <.0001 eGFR M1 (mL/min/1 .73 m²) 67 [25] 57 [27] <.0001

Δ eGFR (mL/min/1 .73 m²) ^* 0 [17] -3 [191 0.015

Systolic BP MO (mmHg) 120 [20] 1 18 [24] 0.22

Systolic BP M1 (mmHg) 120 [30] 120 [28] 0.022

Δ Systolic BP (mmHg) ^* 5 [22] 3 [20] 0.14

Diastolic BP MO (mmHg) 70 [15] 70 [16] 0.042

Diastolic BP M1 (mmHg) 76 [10] 75 [12] 0.061

Δ Diastolic BP (mmHg) ^* 0 [15] 0 [15] 0.75

Hemoglobin MO (g/dL) 13.4 [2.2] 12.9 [2.1 ] <.0001

Hemoglobin M1 (g/dL) 13.6 [1 .9] 12.9 [2.2] <.0001

Δ Hemoglobin (g/dL) ^* 0.2 [1 .6] 0 [1 .9] 0.001

Hematocrit MO (%) 40 [6] 39 [6] 0.0001

Hematocrit M1 (%) 41 [5] 39 [6] <.0001

Δ Hematocrit (%)^* 1 [5] 0 [5] 0.002

Sodium MO 140 [5] 139 [5] 0.018

Sodium M1 141 [5] 141 [4] 0.32

Δ Sodium 1 [4] 1 [4] 0.29

LVEF MO (%) 35 [8] 34 [9] <.0001

Age (years) 64 [17] 70 [15] <.0001

Male 71 64 0.014

Caucasian 91 89 0.39

Previous hospitalization for HF 7 16 < 0001

Reperfusion therapy 46 37 0.002

Previous AMI 26 37 <.0001

Diabetes 31 39 0.005

Prior episodes of HF 14 26 <.0001 Hypertension 61 71 0.001

Medications

Eplerenone 51 42 0.007

ACE! / ARB 86 89 0.17

Beta-blockers 76 70 0.017

Loop diuretics 52 79 <.0001

Table 5: Characteristics of patients with and without events

The events considered between month 1 and month 3 after acute myocardial infarction were cardiovascular death and/or hospitalization for heart failure. Values are expressed as medians [inter-quartile range] or proportions where appropriate.

M0: baseline measurement, M1 : measurement at month 1 , AePVS: plasma volume variation estimated by Strauss formula. See legends of Tables 1 for remaining abbreviations.

^* Absolute change between month one and baseline. # Relative change between month 1 and baseline.

The results show that AePVS was significantly associated with early cardiovascular (CV) events (p=0.0009). Of note, ePVS at baseline and M1 were also significantly associated with CV events (p<0.0001 ). Patients losing weight experienced more frequent events. Of note, AePVS and changes in body weight were not significantly correlated (rho=0.02; p=0.093).

Multivariate analysis including AePVS (according to Strauss formula)

AePVS was retained in the logistic regression model (OR=1 .01 , p=0.004) (Table 6): if plasma volume increased, the probability of CV event also increased.

Table 6: Stepwise logistic regression with AePVS p is a p-value associated to the likelihood ratio test. OR: odds-ratio, CI: confidence interval. See legends of Tables 1 and 2 for abbreviations.

With regard to the added predictive ability of AePVS in the model beyond clinical variables, both NRI and IAUC measures were positive but not significant: NRI=0.09 (p=0.18), IAUC=0.0012 (p=0.39). AePVS significantly improved the ID! by 7.57 % (p=0.01 ).

Table 6 shows that AePVS is linked to the medical outcome.

Of note, in a sensitivity analysis in the subgroups with and without anemia, AePVS was also retained in the models (Table 7).

Table 7. Stepwise logistic regression with ePVS at M1 p is the p-value associated to the likelihood ratio test. When the variable was not retained in the final model (-), p corresponds to the last p-to-enter value. AePVS: plasma volume variation estimated by the Strauss formula, ePVS: plasma volume estimated by the Strauss formula-derived formula, M1 : measurement at month 1 , OR: odds-ratio. CI: confidence interval.

Table 4: Stepwise logistic regression with ePVS at M1

Multivariate analysis including ePVS according to the present invention

interval. See legends of Tables 2 and 3 for abbreviations. ePVS at M1 was retained in the logistic regression model (OR=1 .38, p<0.0001 )

(Table 8).

The three measures of added predictive ability of ePVS at M1 were positive and significant: relative ID! = 15.06 % (p=0.004), NRI=0.18 (p=0.004), IAUC=0.01 (p=0.035). ePVS M1 is associated with CV outcomes beyond routine clinical or biological parameters.

With regard to sensitivity analyses:

i) ePVS M1 was a better predictor of early cardiovascular events than AePVS. ii) In the subgroups with and without anemia at baseline, ePVS M1 was retained in the models as was the case in the subgroups with and without anticoagulants, antithrombotics and reperfusion therapy at baseline ( See Table 4).

iii) In a larger EPHESUS dataset (i.e. which included 5845 or 5880 patients with available hemoglobin or hematocrit measurements at M0), ePVS M0 was only marginally associated with event occurrence at M1 (p=0.051 ), whereas it was significantly associated with 90-day events (OR=1 .12, p=0.007; NRI: p=0.027; IDI: p=0.075) and 180-day events (OR=1 .14, p=0.0006; NRI: p=0.0003; IDI: p=0.002). Of note, when ePVS M1 was considered in lieu of ePVS MO, it was retained in the model (p<0.0001 ) and significantly increased the predictive capacity of the model (data not shown), iv) In a subset of the EPHESUS population with available Brain Natriuretic Peptide (BNP) measurements, we previously reported significant positive correlations between changes in BNP and plasma volume, as assessed by the Strauss Formula between baseline and month 1 (Rossignol P, Menard J, Fay R, Gustafsson F, Pitt B, Zannad F. Eplerenone survival benefits in heart failure patients post-myocardial infarction are independent from its diuretic and potassium-sparing effects. Insights from an ephesus (eplerenone post-acute myocardial infarction heart failure efficacy and survival study) substudy. J Am Coll Cardiol. 201 1 ;58:1958-1966). Present analysis of this subset of 346 patients revealed that 14 patients experienced a CV event between month 1 and month 3. BNP and instantaneous ePVS at MO and M1 were significantly but weakly correlated (rho=0.23, p<0.0001 at MO, and 0.25, p<0.0001 at M1 ). Owing to the small number of CV events, only univariate associations were assessed. At M1 , 13 out of 14 patients with CV event had both BNP and ePVS > median and only 27% of patients with no event, while AUC was better with BNP and ePVS > median at M1 (AUC=0.83), compared to BNP or ePVS considered alone (AUC=0.76 with BNP and 0.72 with ePVS) {Figure 1).

In summary, multivariate analysis was performed using stepwise logistic regression. AePVS was selected in the model (OR=1 .01 , p=0.004). The corresponding prognostic gain measured by integrated discrimination improvement (ID!) was significant (7.57 %, p=0.01 ). Nevertheless, ePVS M1 was found to be a better predictor than AePVS

Conclusions:

In HF complicating myocardial infarction (Ml), congestion as assessed by the Strauss formula and measurement of plasma volume according to the present invention provided a predictive value of early cardiovascular events, beyond routine clinical assessment. Prospective trials assessing congestion management guided by this simple tool to monitor plasma volume are warranted.

The method of the present invention allows predicting early cardiovascular events i.e. cardiovascular death and/or hospitalization for heart failure between 1 month and 3 months after AMI with heart failure. An estimation of plasma volume displayed greater prognostic value than an assessment provided by using the Strauss formula. The method of plasma volume estimation of the present invention enables physicians having knowledge of the reference value ePVS_rf)i specific of a patient to immediately and reliably assess the patient's congestive status beyond usual routine clinical assessment and natriuretic peptide measurement.

Abbreviations

HF: heart failure

PV: plasma volume

BP: blood pressure

LVEF: left ventricular ejection fraction

AMI: acute myocardial infarction

eGFR: estimated glomerular filtration rate

LR: logistic regression

LDA: linear discriminant analysis

Claims

What is claimed is:

1 . A method for monitoring the evolution of congestive status of a patient previously hospitalised for congestive heart failure, comprising estimating in vitro a plasma volume in the patient, comprising the steps consisting of:

iii) estimating the plasma volume according to a formula I

1 - hematocrit

ePVS = x O.01 (I)

hemoglobin{g I cIL)

wherein hemoglobin represents the hemoglobin level measured on the sample and hematocrit represents hematocrit level measured on the sample, and

iv) evaluating the evolution of congestive status of the patient from the value ePVS of the plasma volume obtained in step iii), by comparing this value to a reference value ePVSref specific of the patient.

2. A method according to claim 1 , characterized in the reference value ePVS_ref specific of the patient is a plasma volume value measured when the patient is authorized by practitioners to leave an after hospitalisation for congestive heart failure.

3. A method for determining whether a heart failure for instance post myocardial infarction patient having a decongestive treatment is at risk of having a cardiovascular event such as death, cardiovascular death, cardiovascular hospitalization, hospitalization for heart failure, comprising the steps of the method of one of claims 1 and 2.

4. A device for performing the method of any one of one of claims 1 and 2 comprising determining in vitro a plasma volume ePVS in a patient, said device comprising:

- means for measuring values of hemoglobin level and hematocrit level from the human physiological fluid sample obtained at time t1 without calculating one of the values from the other; and - means specially designed for automatically calculating the plasma volume according to formula I.

5. A device of claim 4 , wherein the means for automatically calculating the plasma volume according to formula I further comprise data concerning the reference value ePVSref specific of the patient.

6. A method for adjusting or instituting an anticongestive therapeutic treatment comprising the steps of the method of one of claims 1 and 2 and adjusting or instituting the anticongestive therapeutic treatment, according to the increase or decrease of the ePVS value of the plasma volume obtained in step iii), in comparison with the reference value ePVSref specific of the patient.