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1Cardiovascular Mechanics and Biofluid Dynamics Research Unit, Institute of Biomedical Technology; 2Ghent University Hospital, Department of Cardiovascular Diseases; and 3Ghent University, Department of Epidemiology and Public Health, Ghent, Belgium
Submitted 14 July 2006 ; accepted in final form 17 October 2006
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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elastance; single beat; left ventricular mechanics; population
Whereas the concept of Ees is generally applied to assess changes in ventricular contractile state (e.g., due to inotropic stimulations) within a single individual, interpretation of differences in Ees in ventricles with different geometries appears cumbersome (6, 8, 9), since Ees is intrinsically influenced by LV dimensions (1, 8). Indeed, because LV volume can differ widely for various subjects while LV pressure remains constant, it is obvious that the pressure-volume ratio Ees is a function of LV volume. Normalizing or scaling ventricular contractility Ees is thus required to cancel out the effect of geometry and to obtain a useful index of myocardial contractile state, which ideally should reflect the mechanical characteristics of the myocardial fibers. Moreover, the use of such a scaled index would allow assessment of age- and sex-related differences in myocardial contractility. A number of normalization methods have already been proposed in the literature (4, 28), but a generally accepted standard has not been agreed on nor have the existing methods been compared with each other.
The primary aims of this study, conducted in a large population setting (the Asklepios study) of middle-aged untreated subjects free from overt cardiovascular disease, were to 1) calculate ventricular contractility Ees in a noninvasive way, 2) propose a novel population-specific normalization method for Ees, and 3) assess the effects of age and sex on myocardial contractility as determined with the various normalization methods.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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The population participating in the Asklepios Study consists of 2,524 subjects free from overt cardiovascular disease and between 35 and 55 yr old at the initiation of the study. All echocardiographic and blood pressure measurements were single device and single observer and performed between October 2002 and September 2004 at a single study site in Erpe-Mere/Nieuwerkerken (Belgium). The Ethical Committee of the Ghent University Hospital approved the study protocol. Written informed consent was given by all subjects. To exclude the effect of antihypertensive drugs, we selected 2,184 (1,115 women and 1,069 men) out of the original 2,524 subjects for this analysis. We excluded 340 subjects because 1) 264 patients were drug treated for hypertension and thus excluded from the study, and 2) data about stroke volumes (SVs), timings of onset and cessation of aortic flow, and/or ejection fraction (EF) data were incomplete in 76 additional subjects because of various technical reasons.
Blood Pressure Measurement
Brachial systolic (Ps) and diastolic blood (Pd) pressures were recorded using bilateral triplicate measurements on a rested subject using a validated oscillometric Omron HEM-907 device (Omron, Matsuka, Japan). Subjects were blinded to the blood pressure results during measurements. Cuff size was individually chosen based on arm circumference. Blood pressure values of these six readings were averaged, and the mean value was used throughout the whole study, except when there was a persistent discrepancy of more than 20 mmHg between left- and right-sided blood pressures after a second bilateral triplicate measurement. In that case, the mean value from the limb with the highest pressure was taken to represent blood pressure.
Echocardiography
Echocardiographic measurements were performed using a commercially available ultrasound system (Vivid 7, GE Vingmed Ultrasound, Horten, Norway) equipped with a cardiac transducer (M3S 1.7/3.4-MHz matrix transducer). Subjects were examined in the left lateral recumbent position using standard parasternal short- and long-axis and apical views. All measurements were ECG gated and consisted of cineloops of recordings of at least five (up to 30) cardiac cycles during normal breathing. Images and loops were exported in RAW Dicom format to magneto-optical disks and an image server. Measurements were performed off-line in a blinded fashion on a dedicated Compaq Evo W4000 workstation running GE Vingmed EchoPAC Version 2.0.1 software (GE Vingmed Ultrasound).
Data Analysis
LV geometric parameters.
LV geometry is commonly characterized by its resting end-diastolic volume (EDV), LV wall mass (LVM), and relative wall thickness (RWT) (Fig. 1). LV volumes at end diastole were calculated from the two-dimensional parasternal long-axis images using the standard volumetric methods (Teichholz' formula) (32):
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Normalization of Ees for LV geometry. Linear regression was used to estimate the sensitivity of Ees for geometric parameters EDV and LVM. Since Ees is intrinsically dependent on LV geometry (6, 8, 9, 27, 28), it cannot be applied to estimate and compare myocardial contractility between subjects with different LV geometries. Hence, an appropriate normalization method that converts Ees into a measure of myocardial contractility is highly desired to estimate sex- and age-related differences in myocardial contractile state. A number of normalization methods can be found in the literature (4, 28), but a single-accepted standard has not been agreed on (6).
Existing methods to normalize Ees, and applied in this work, include 1) normalizing for EDV: Ees·EDV, 2) normalizing for LVM: Ees·LVM, and 3) elastance index (EI): 0.433·Ees·LVM/RWT. EI is an index of myocardial contractility, introduced by Beyar and Sideman (4) and based on a mathematical, but physiologically realistic, thick-walled ellipsoid model of the LV: it incorporates both the passive and active properties of the myocardial fibers, accounts for the anatomical fiber angle distribution throughout the wall, and assumes a radial propagation of the electrical activation front from the endocardium toward the epicardium (3).
In addition to these methods, we developed an alternative, semiempirical approach to normalize Ees. From our population of healthy middle-aged subjects, we have first selected a subpopulation of "physiological reference" subjects, based on a list of 12 criteria (see Table 1). We subsequently assumed that, in this subpopulation, the spectrum of Ees values is largely explained by the differences in LV geometries rather than by the differences in myocardial contractility. Minimization of variance in Ees using a linear regression model would allow us to propose a formula to determine a geometry-normalized measure of contractility. We specifically opted for obtaining a formula that is similar to Beyar and Sideman's EI. In APPENDIX A, it is described how we derived our geometry-adjusted Ees (Ees,adj), which is of the general form:
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Statistics have been performed with commercially available software (SPSS 12.0, SPSS, Chicago, IL). All parameters were screened for deviation from normality. Measures of central tendency are means (SD) (normal distribution) or median and interquartile range. Pearson correlation coefficients were used to quantify the agreement between two variables. The geometry dependence (in terms LVM and EDV) of the various contractility indexes was analyzed by using univariate and multiple linear regression models. Two-way ANOVA was used to detect differences in contractility due to sex and age. When using analysis of covariance, homogeneity of the regression slopes (response variable vs. the potential covariates) was verified by testing for significance of the interaction term between the covariate and the grouping variable. All P values are specified in RESULTS, unless P < 0.001.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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Table 2 displays baseline demographic and hemodynamic data and geometric parameters for the study population and the selected "reference" subpopulation. In the study population, women had higher resting heart rates and lower brachial blood pressure values. As expected, women had smaller hearts with significantly smaller EDV and lower LVM. No age difference was observed between men and women. EF, a commonly used index of systolic function, was significantly higher in women (see also Fig. 2A).
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Ees was significantly higher in women than in men at all ages (2-way ANOVA, P < 0.001, post hoc t-test, P < 0.001). On average, Ees in men is 2.40 (SD 0.64) versus 2.89 mmHg/ml (SD 0.80) in women (P < 0.001, Table 2). When analyzing the evolution of Ees with age, the curves in Fig. 2B suggest a more or less parallel time course of Ees in men and women until the age of 50 yr. After the age of 50 yr, however, Ees in women continues to rise (P = 0.058 vs. age 46–50 yr), whereas Ees remains constant in men (P = 0.82). Figure 2, C and D, also displays the age and sex dependence of EDV and LVM. EDV and LVM were significantly higher in men than in women at all ages (2-way ANOVA, P < 0.001, post hoc t-test, P < 0.001). LVM was significantly influenced by age in both men and women (ANOVA, both P < 0.001), whereas EDV appeared unaffected by age (ANOVA: women, P = 0.928; and men, P = 0.596).
Differences in LV geometry are likely to be one of the major contributors to the sex-related differences in Ees, with EDV [115.79 (SD 23.71) vs. 92.60 ml (SD 18.26), P < 0.001] and LVM [177.22 (SD 39.53) vs. 121.40 g (SD 29.07), P < 0.001] being higher in men. In men, Ees is significantly influenced by the geometric parameters LVM and EDV (Ees = 3.616 – 0.002·LVM – 0.008·EDV; P < 0.001; R2 = 0.134) with EDV about 2.5 times as important as LVM. In women, on the other hand, Ees was only determined by the geometric parameter EDV (Ees = 4.238 – 0.015·EDV; P < 0.001; R2 = 0.111); LVM did not enter the model. Figure 3, A and B, shows the relation between Ees and both predictor variables separately. It can be seen that women tend to have smaller EDV and LVM as well as higher Ees compared with those in men.
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Normalization Based on the "Physiological Reference" Subpopulation
Two-hundred ninety-four subjects (175 women and 119 men) from the study population (13.5%) fulfilled the criteria to be designated as "physiological reference" subjects. Their baseline characteristics are presented in Table 2. The prevalence of women is somewhat higher in the subpopulation than in the complete population. Based on the principles that are outlined in APPENDIX A, the following formula was obtained, which converts Ees into a geometry-adjusted index of myocardial contractility (Ees,adj):
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Comparison of Normalization Techniques
To verify whether our normalization method worked well, linear regression was performed between Ees,adj and the geometry-related predictor variables EDV and LVM in the subpopulation. As anticipated, no statistically significant regression model could be found (Table 3). Note, however, that this was not the case for the three other normalization methods, where significant regression functions were found in the subpopulation. Whether this implies that we overcompensate the effect of geometry using our normalization approach or that it is a manifestation of the inability of the existing methods to compensate for geometry is an open question, since none of the indexes has been validated. Note also that Ees·EDV was even dependent on both EDV and LVM. Nevertheless, the coefficient of variation (CV), an indication of the relative amount of scatter in the data, is minimal in case of Ees,adj, showing that Ees,adj performed best in reducing the amount of variance (Table 3) in the subpopulation.
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Figure 4 depicts the age- and sex-related differences in myocardial contractility obtained from the various normalization methods. A common finding is that, similar to Ees, myocardial contractility continues to rise after the age of 50 yr in women, whereas myocardial contractility in men seems to top off at that age. However, there are serious inconsistencies with regard to the absolute values of the calculated indexes and, hence, the relative position of the female and male curves.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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One has to realize that the concept of Ees was originally applied in acute experiments within a single individual, where it will show an increase as a result of a positive inotropic stimulation. In these circumstances, the increase in the slope of the ESPVR is easily interpreted as a change in ventricular contractility, since only the inotropic state has altered and LV geometry stays unaffected. However, appreciation of differences in Ees becomes much more complicated when evaluating and comparing contractility in cross-sectional studies in which the ventricles have a spectrum of different geometries. Since Ees is highly affected by LV geometry (1, 8), a difference in Ees between two given ventricles does not necessarily indicate a different myocardial contractile state. Scaling or normalization of geometry is thus required to provide information about myocardial contractile state.
Stress-strain relationships, derived from invasively acquired pressure-volume signals, have been used successfully in the past as a virtually load- and geometry-independent measure of contractility (21, 33). In our large population, invasive techniques are obviously not feasible. To the best of our knowledge, normalization of Ees for LV geometry is the only approach that permits one to estimate myocardial contractility noninvasively. Even though the geometry dependence of Ees was already acknowledged in the 1970s, a single accepted standard to convert Ees into a measure of myocardial contractility (i.e., an intrinsic muscle property) has not yet been defined (6), nor have the existing normalization methods been evaluated and compared in a large population sample of apparently healthy middle-aged subjects. Sagawa (28) mentioned that normalization with respect to LVM, or EDV measured under a standardized resting condition, may provide information regarding myocardial contractility. Normalizing by the volume intercept of the ESPVR (V0) has also been suggested (22). However, the fact that V0 may take on negative values renders it practically useless as a scaling factor. From a conceptual point of view, multiplication with EDV could indeed provide an acceptable index of myocardial contractility since Ees·EDV would remain virtually constant. This method, however, can in theory only be applied accurately when Ees is inversely proportional with EDV. Under the assumptions that 1) EF is virtually constant in normal ventricles, 2) the volume intercept of the ESPVR equals zero, and 3) Pes remains constant, Ees can be expressed as Pes/ESV = Pes/[EDV·(1–EF)] = k·EDV–1 (where ESV is end-systolic volume), which indeed fulfills the criterion. Unfortunately, these assumptions are not entirely fulfilled, introducing scatter and uncertainty in this method of normalization. Other potential limitations of this normalization method include its undesired preload dependence (as it incorporates EDV) (4) and the fact that wall thickness is not directly accounted for.
Normalization by LVM can arguably be considered as a superior method, because LVM is an invariant property of ventricular muscle and is thus not subject to changes in loading conditions (although in clinical echocardiography, EDV and LVM may be related to each other due to the way LVM is measured). Beyar and Sideman (4) stated that this approach works well in ventricles with a normal RWT. However, in particular, when RWT deviates from normal (as in concentric hypertrophy or concentric remodeling), the reliability of this method becomes questionable.
Based on a mathematical model of LV mechanics, Beyar and Sideman (4) introduced the EI (EI = 0.433·Ees·LVM/RWT), a measure of myocardial contractility that was shown to be superior to Ees·EDV and Ees·LVM in their numerical experiments, because it accounts for changes in both LVM and RWT. Within a wide range of different values of RWT and LVM, their EI values showed a coefficient of variation [mean (SD)] of no more than 3.5%, in contrast to 25% and 17% in the case of Ees·EDV and Ees·LVM, respectively. We believe that by raising the main factors (LVM and RWT) in their index to a certain power, their EI could even be optimized. However, no such attempts were made in their study.
Our normalization method relies on a principle that is somewhat comparable with EI. In a subpopulation of 294 "physiological reference" subjects, selected on the basis of very strict criteria, we have made a critical, but reasonable, assumption, namely, that myocardial contractility is similar (apart from some natural dispersion) in this population sample. In other words, we assumed that there is no relationship between myocardial contractility and a geometric parameter. In this sense, we wish to draw the parallel with experimental work, where control animals would also be sampled from a normal population that would also exhibit a range of myocardial contractility values. Using linear regression on log-converted variables, we were able to define Ees,adj to quantify myocardial contractility. Belcher et al. (2) previously applied a similar method in dogs to remove the variance in Ees due to the mass of the dog or the LV mass. An important benefit of our method is that it does not require any modeling assumptions regarding passive and active stress-strain relations, activation function, depolarization sequence, or fiber orientation in contrast to the EI. A practical limitation of our method, however, lies in its population-specific character, which may render Ees,adj less suitable for making comparisons with other populations and/or species. One must realize that normalization methods based on a regression function can only guarantee reliable values for myocardial contractility when they are used within the range of the studied variables (RWT and LVM). As such, application of our method in species with much larger or smaller ventricular dimensions (such as mice or rats) should be clearly discouraged at the moment so as not to arrive at incorrect conclusions regarding myocardial contractility.
The difference between the multipliers (0.0941 vs. 0.45) of our and Beyar and Sideman's contractility index, although dramatic, is irrelevant, since both Ees,adj and EI represent (differently) scaled indexes. The exponents of RWT (–0.159) and LVM (0.455) are much lower than in EI, suggesting that Ees,adj is far less affected by LV geometry than is EI. This was confirmed by comparing the R2 values obtained from linear regression between contractility indexes and geometry (EDV and LVM): Ees,adj = 2.964 – 0.006·EDV + 0.003·LVM, R2 = 0.022, P < 0.001, whereas EI = 160.243 + 2.162·EDV + 0.371·LVM, R2 = 0.245, P < 0.001. The exact reason for this discrepancy between the exponents could not be determined. However, it might be related to the fact that we assumed a constant myocardial contractility, while in reality, even in the subpopulation, a significant relationship between myocardial contractility and geometry can probably not be excluded.
The CV corresponding with our method (22.3%, Table 3) was lower than the CV obtained from any other method. This result is in agreement with the method of Belcher et al. (2), who concluded that adjustment by regression reduces CV more effectively than mere indexing (i.e., multiplication or division by a variable). Note, however, that this finding is only by definition and by no means proves that our method is superior to other ones.
From the discussion above, it should be clear that all normalization methods have their strengths and limitations. Moreover, because these methods all rely on different assumptions, the discrepancies of sex difference are not really surprising. Ultimately, validation of the approaches is mandatory to know which one is the most reliable.
One of the major questions that has been the issue of many investigations is whether basal myocardial contractile state is different for men and women. Do men simply happen to be larger mammals than women, with a bigger ventricle, but with an identical myocardial state?
EF, a geometry-independent but load-dependent index of systolic function, was higher in women than in men in our population [66.88% (SD 7.66) vs. 64.50% (SD 8.26), P < 0.001], confirming the findings in other studies (10, 15, 25). Even though EF is load dependent and therefore not a proper index of contractility, the reported differences in EF between men and women may actually suggest a true sex-specific difference (15).
The higher Ees in women compared with that in men, as was shown in our study, was already observed in an invasive study performed in patients (age, 48–75 yr) undergoing routine cardiac catheterization (15) and in a cross-sectional population study (age 
45 yr) on ventriculo-vascular interaction (25). However, this difference in Ees only offers a limited amount information from a physiological point of view and does not provide specific mechanical information about the myocardium. Here, a higher Ees in women simply signifies an increased sensitivity of systolic pressure to changes in volume. Using linear regression analysis, Redfield et al. (25) showed that the relation between Ees and age is steeper in women than in men. Although linear regression analysis of our data (women: Ees = 2.075 + 0.018·age, P < 0.001; and men: Ees = 2.008 + 0.008·age, P < 0.012) confirms their findings, only after grouping the continuous variable age into half decades, an apparently different time course between men and women (which is also reflected in the analysis of myocardial contractility) from the age of 50 yr could be revealed. Note, however, that age in the study of Redfield et al. (25) ranged from 45 to about 95 yr, whereas the examined range in our study is much narrower and limited to the age of 55 yr.
We are convinced that differences between men and women could more objectively be assessed on the basis of direct measurement of myocardial contractility instead of indirect estimation. This is, however, a difficult, and probably virtually impossible task. As such, literature about quantification of myocardial contractility is limited and mostly restricted to papillary muscle studies in rodents. Even then, there are a number of inconsistencies between these studies, partially due to differences in age, animal strains, and measurement methods (20). Over a wide range of muscle lengths and bath concentrations of calcium, Capasso et al. (7) did not detect a significant difference in peak tension between male and female rats. In isotonic measurements, peak shortening was higher in female rats, but maximum velocity of shortening was similar in male and female animals. In another study by Leblanc et al. (19), it was found that papillary muscles of female rats aged 6 mo and older exhibited smaller isometric and isotonic contractions, smaller maximal rates of tension and shortening development and decline velocities during both the onset and relaxation phases, and shorter contractions than in age-matched males.
In our study, analysis of EI and Ees·LVM suggested that myocardial basal state is mildly higher in men than in women, whereas no statistical difference was seen in terms of Ees·EDV.The latter observation was confirmed in the study of Redfield et al. (25). Hayward et al. (15), on the other hand, found that Ees·EDV was higher in men (265.8 ± 12.5 mmHg, mean ± SE) than in women (221.5 ± 9.3 mmHg, mean ± SE). Our novel index, Ees,adj, suggests that myocardial contractility remains nearly identical in men and women until the age of 50 yr. After the age of 50 yr, a relative decline in myocardial contractility is observed in men. To approach the problem in a slightly different way, we also calculated Ees values that were statistically adjusted for the subject's length and weight using covariance analysis. These data are shown in Fig. 5. Note the remarkable resemblance with Ees,adj. This approach thus appears to confirm that there are no differences between men and women in contractility when adjusting the measurements for geometric and/or morphological differences between the sexes. It is to be mentioned that, in contrast to Ees,adj, there was no significant difference between the curves in Fig. 5 at the age of 51–55 yr (P = 0.264). All normalized contractility indexes show a time course that is very similar to the nonnormalized, but shifted vertically. This is explained by the fact that the geometric parameters EDV, LVM, and RWT, i.e., the variables that are used to convert Ees into a measure of myocardial contractility, are not, or very poorly, related with age (R2 values ranging between 0.021 and 0.045).
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However, women do have a specific preponderance for diastolic heart failure, characterised by heart failure symptoms in the presence of a preserved EF, while men more often present with systolic heart failure (decreased EF) (24, 26). In this regard, the relative decrease in Ees in men from age 50 yr and onward and the better preservation in women do have a clinical mirror image, although the underlying mechanics are probably more complex and need to be investigated.
Methodological Considerations
A considerable amount of geometric assumptions and simplifications was required to calculate LV mass, volumes, and pressures and to estimate Ees because of the noninvasive nature of our measurement methods. These assumptions are expected to reduce the accuracy of our calculations. However, each of the assumptions were based on correlations that were previously published in validation studies, and it is therefore unlikely that a bias in our results would alter our conclusions. Moreover, it should be realized that the large sample size in this study significantly strengthens our conclusions and that this study simply could not have been completed by using invasive methods.
All of the suggested normalization procedures intrinsically assume that a difference in geometry between two ventricles is accompanied by a proportional difference in volume of contractile units. As a result, potential changes in the volume or mechanical properties of the extracellular matrix, which may vary with age and between sexes, were not accounted for. In this sense, one might wonder whether geometry-normalized indexes of contractility could ever assess fiber contractility as such.
We also wish to point out that the Ees values were obtained by using a so-called single-beat method, which is noninvasively applicable and does not require changing loading conditions. Although the method has been validated (11) and has recently been applied in a population study on ventriculo-vascular interaction (25), it remains an estimate of Ees with potential limitations, as pointed out by Kjørstad et al. (18) in a validation study.
In our novel method, we assumed that the formula for Ees,adj, which is derived from the data in the subpopulation, is also applicable in the whole population. Care should be taken since the range of geometric and demographic characteristics in the study population is broader than the subpopulation range. On the other hand, there is no proof that this formula would not be valid for the whole group either.
A last potential limitation is that the Ees ultimately should be calculated with different heart stimulation frequencies (due to the force-frequency effect). However, to the best of our knowledge, no methods for accounting for heart rate have been suggested. We moreover speculate that the influence of the minor difference in heart rate between men and women is negligible.
In conclusion, the present study showed that Ees as such cannot be used to compare myocardial contractile state between men and women of various ages in a cross-sectional study. Normalization is required to cancel out the effect of geometry. Because of the difference in their underlying assumptions, the various myocardial contractility indexes do not provide consistent information with respect to sex differences. However, regardless of the index that was used, it was found that myocardial contractility appears to be better preserved in women than in men after the age of 50 yr. This finding at the population level could have potentially important clinical implications that require further investigation.
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APPENDIX A |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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Based on the linear relationship between dependent variable Ees and the independent variables LVM and RWT, a geometry-adjusted Ees can be determined as Ees,adj = mean (Ees) + dy·x, with dy·x the deviation from regression, i.e., Ees – Êes, where Êes is the predicted value. This adjustment removes the variation in Ees that is accounted for by the variation in geometry, so that the adjusted values are those to be expected if all Ees values were taken at the mean geometry (2). The adjusted Ees,adj can then be determined as Ees,adj = mean (Ees) + Ees – (a + b·LVM + c·RWT), where a, b, and c are determined from linear regression.
To obtain a formula that has the same functional form as Beyar and Sideman's EI, the very same principle is applied to the logarithms of variables Ees, LVM, and RWT. Log-converted variables are defined as follows:
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GRANTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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REFERENCES |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIX AGRANTSREFERENCES |
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