INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

SEVERAL ATTEMPTS TO DEVELOP a method for calculating the left ventricular (LV) contractility based on one single pressure-volume (PV) loop have been reported (14-16, 21). These efforts have been initiated by the appreciation of end-systolic PV relationship (ESPVR) as a useful index assessing the contractile state of the heart. However, the preload alterations used in constructing multiple ESPVRs necessary in the assessment of load-independent contractility have been a serious limitation to its clinical applications (4, 13, 18, 19). On the other hand, a reliable method for calculating contractility based on a single beat would have a noninvasive clinical potential because echocardiographic area measurements could be used as a substitute for volume and peripheral arterial pressure for intraventricular pressure (2, 6).

In single-beat calculations, the slope of the linear end-systolic elastance curve is determined by two points: ESPVR and a point that has to be calculated using additional information from the PV loop. The latter is either the volume-axis intercept of the elastance line (V₀) (14), or a point given by simulated end-systolic isovolumic pressure, and end-diastolic volume (15, 16, 21). The inherent problem in this approach is our limited knowledge of the heart as a mechanical pump in situ, which does not allow us to mathematically determine such a point accurately. Thus all single-beat indexes are therefore empirical approximations expressed as formulas reflecting one or more factors that are known to correlate with contractility [i.e., maximal first derivative of pressure (dP/dt_max), time to dP/dt_max, ratio between preejection period (PEP) and ejection time (ET), end-systolic volume, or stroke volume (SV)]. Most of the methods described earlier have shown very good correlation and agreement between single-beat and multiple-beat-derived elastances, but when reevaluated by other groups, they have failed to be reproducible (14, 16).

The aim of this study was therefore to evaluate the usefulness of single-beat estimations of contractility. We applied four different methods (14-16, 21) on our extensive database of PV measurements in pigs based on data from intraventricular combined pressure and conductance catheters. We compared elastance values derived from conventional multiple-beat recordings during preload alterations with calculated single-beat elastance values from experiments using inotropic, metabolic, and ischemic interventions. Because the most important application of any such index is the ability to detect altered contractility, we tested whether the four different methods could detect inotropic changes during dopamine infusions. From these comparisons, we conclude that all the evaluated methods of single-beat estimations of contractility fail to comply with reasonable accuracy demands.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

PV data from 37 pigs obtained by combined pressure and conductance catheters (Millar Instruments or Cardiodynamics) were taken from our database of cardiac function analyses. The data came from three previous protocols conducted in our laboratory (7-9). The weight of the pigs ranged from 23 to 38 kg. We analyzed 88 separate, 8-s-long, averaged steady-state PV-loop sequences with a sampling frequency of 200 or 250 Hz, and divided them into four subgroups. Group 1 consisted of 42 baseline or control loops from two previous studies: 21 loops were sampled from 7 control pigs at 3 successive time points (7), and 21 loops were baseline recordings from 21 pigs before intervention (ischemia and inotropic stimulation) (7, 9). Data were collected before any pharmacological or other interventions. In group 2, eight files sampled during dopamine infusions were used. Dopamine was given as 5-10 µg · kg¹ · min¹, adjusted to give an increased mean arterial pressure of at least 20 mmHg (9). Group 3 consisted of 18 files from a study using metabolic intervention (9 pigs) where one-half of the pigs received initial glucose-insulin-potassium (GIK) infusions, followed by Intralipid (Pharmacia), and the other half received Intralipid before GIK (8). Group 4 was a postischemic study, including 10 pigs subjected to repetitive occlusions of the left coronary main stem (2 × 1 min + 9 × 2 min, 1-min perfusion between occlusions). The left coronary artery perfused 81.5 ± 2% (mean ± SD) of the left ventricle, and there were no indications of infarction after the occlusions. In this study, PV data were sampled 30 and 90 min after ischemia (7).

The reference multiple-beat ESPVRs were derived from conventional multiple PV loops sampled immediately after the steady-state loop recording during preload alterations. A 7-Fr balloon catheter (Sorin Biomedical) in the lower caval vein was used to alter preload. In most cases, two runs were performed and a mean value was used. The slope of the regression line or the multiple-beat elastance was denoted E_es(MB). The x-axis intercept of the regression line through the points of maximal PV relation was V_0(MB).

Single-beat estimation based on simulated isovolumic pressure curves. In 1991, Takeuchi et al. (21) described a method based on a previous work by Sunagawa et al. (20), and evaluated the method in 16 patients. A theoretical peak isovolumic pressure [Pmax(E)] was determined (Fig. 1A), and single-beat elastance [Ees(Pmax)] was given by the slope of the line through the point of maximum ESPVR, and the point defined by the coordinates Pmax(E) and end-diastolic volume (EDV) (Fig. 1B). The pressure curve [P(t)] used to determine Pmax(E) is a simulated isovolumic contraction at EDV, which is based on one ejecting contraction and a nonlinear least-squares approximation technique (20, 21)

(1)

where 1/2Pmax(e) is the amplitude,  is the angular frequency, C is the phase shift angle of the sinusoidal curve, and EDP is the LV end-diastolic pressure, which in this context is the distance from the lowest point of the curve to the x-axis. The theoretical peak isovolumic pressure (source pressure) is then given by

(2)

To get a better fit when the contraction and relaxation waveforms are very different [difference in numerical value of dP/dt_max and minimal first derivative of pressure (dP/dt_min)], we also made a modified isovolumic pressure approximation using a fifth-order polynomial function. The fifth-order function was derived from repeated curve approximations to assess a best fit.

View larger version (26K): [in this window] [in a new window]   Fig. 1.   Schematic illustrations of different single-beat methods. See METHODS and the original studies for more detailed descriptions. A: Takeuchi et al.'s (21) method. Isovolumic pressure at end-diastolic volume (EDV) is simulated by fitting a curve to the pressure curve from end-diastole to maximal first derivative of pressure (dP/dtmax), and from minimal first derivative of pressure (dP/dtmin) to the same level as the end-diastolic pressure of the preceding beat. Pmax(E), maximal isovolumic pressure. B: elastance is given by the slope of the line between the point defined by EDV and calculated isovolumic end-diastolic pressure (Piso), and end-systolic pressure-volume (PV) relationship (ESPVR). Shih et al.'s method is based on the same principle, but here peak isovolumic end-systolic pressure is calculated according to a model depicted in C. Ees, single-beat elastance. C: unadjusted pressure (Punadj) is given by the regression lines through the pressure points surrounding dP/dtmax and dP/dtmin, and adjusted pressure (Padj) is then calculated. D: Senzaki et al.'s method relies on the assumption that there is a species-specific normalized time-varying elastance [E(tN)/Emax(sb) relation], which is independent of heart rate, load, and contractility. Assuming constant volume intercept (V0), a given ratio of E(tN) and Emax(sb) yields V0(SB) from which Emax(SB) is calculated. E: Shishido et al.'s method where single-beat estimation of end-systolic elastance [Ees(SB)] is given by the slope of the line through Pmax and Pes. Pad, pressure at the end of isovolumic contraction. F: Et curve (solid line) with superimposed bilinear approximation (dotted lines). The supposedly load-correcting factor  is given by the slope of the approximation line in the ejection time (ET) divided by the slope of the corresponding line in the isovolumic contraction phase. Ead, elastance at the end of isovolumic contraction; Eed, end-diastolic elastance; PEP, preejection period.

Modified isovolumic approach. A modification of this method was published by Shih et al. (15) as a computer algorithm intended for automated single-beat calculations. This method was evaluated in 16 patients after cardiopulmonary bypass. Pressure points within ±20% of either inflection point (dP/dt_max and dP/dt_min) in the upstroke and downstroke intervals were selected for linear fitting (Fig. 1C). The points around the left inflection point were fitted to a line via linear regression analysis. The same was done for the right inflection point, and the intersection point of the two lines was defined as the unadjusted pressure. The validity of the method then relies on the assumption that the unadjusted pressure, together with the pressure at the left and right inflection points, can be fitted to a sine curve with peak amplitude equals peak isovolumic pressure equals adjusted pressure (P_adj)

(3)

where P_unadj is unadjusted pressure and  is left inflection pressure + right inflection pressure/2. Single-beat elastance [Ees(P_adj)] was then given by the line through the point of ESPVR, and the point defined by the coordinates P_adj and EDV (Fig. 1B).

Single-beat estimation based on normalized, averaged elastance curves. Senzaki et al. (14) described a method for estimating LV contractility based on normalized time-varying elastance curves [E_N(t_N)]. In this method, the volume axis intercept of the elastance curve (V₀) is calculated from a single PV loop. The contractility is then given by the curve through V₀ and the point of maximal ESPVR. E_N(t_N) curves for all PV files were calculated defining time-varying elastance [E(t)] as the instantaneous ratio of P(t)/[V(t)  V_0(MB)]. The maximal value of E(t) [termed E_max(sb)] and the time from the R wave of the electrocardiogram to achieve E_max(sb) (termed t_max), were both determined. The normalized E(t) function was then defined as

(4)

where t_N = t/t_max (Fig. 1D). The individual E_N(t_N) curves were then resampled and averaged to yield E_N(t_N) curves for each subgroup and for all 88 PV loops (Fig. 2).

View larger version (21K): [in this window] [in a new window]   Fig. 2.   Normalized time-varying elastance curves for each group (see METHODS). The bold line represents the mean curve and thin lines ± SD. Figure 1A is the mean curve comprising all 88 files. n, no. of files.

(5)

(6)

View larger version (15K): [in this window] [in a new window]   Fig. 3.   Time-varying V0(SB) calculated from Eq. 5 with the use of a representative PV loop, and the corresponding volume axis intercept of the elastance curve at multiple beats [V0(MB)]. Equation 5 is unstable throughout most of the cardiac cycle, and it is therefore necessary to determine V0(SB) at times where the error of the estimate is minimal.

View larger version (28K): [in this window] [in a new window]   Fig. 4.   Analysis of physiological and mathematical stability of the V0 estimation, according to Senzaki et al. A: standard deviation (SD) for the pooled EN(tN) curve (Fig. 2A) and for each of the four subgroups (Fig. 2, B-E) as a function of tN, giving a measure of dEN(tN). B: dV0/dEN(tN) plotted against tN (Eq. 12) reflecting the time-varying sensitivity of V0 to changes in the pooled EN(tN) curve (Fig. 2A). dV0/dEN(tN) were calculated for all 88 files at tN intervals of 1:200, and the curve represents the mean values. C: Total curve in A multiplied with the curve in B gives a plot of dV0 as a function of tN. The curve stabilizes on a minimal variance at a tN of ~0.40. D: plot of the derivative of the pooled and grouped normalized elastance curves (Fig. 2, A-E) against tN. The curves show that the most linear part of the EN(tN) curve is bounded by 0.40  tN  0.50. For clarity, only selected points are shown in A and D.

(7)

Single-beat estimation using bilinearly approximated time-varying elastance. This method was described by Shishido et al. (16). It is based on the elastance curve derived from the PV loop, but is primarily focused on the shape of the curve, assuming that the curve is dependent on contractility and loading conditions. Single-beat contractility is then given by the equation

(8)

where P_ad is the pressure at the end of isovolumic contraction (the moment when the steep rise of the aortic pressure wave begins), P_ed is end-diastolic pressure defined as the pressure when LV dP/dt exceeds 30% of dP/dt_max, P_es is end-systolic pressure defined as the pressure when dP/dt decreases to 20% of dP/dt_min (Fig. 1E), PEP is the time from end diastole to end of isovolumic contraction, ET is the time from end of isovolumic contraction to end systole, and  is the ratio of the slope in the ejection phase to that in the isovolumic phase (Fig. 1F).

Computer calculations. The least-square approximations to the sine curve were done in Matlab (MathWorks) and initial values set for our calculations were 170 mmHg/ml for Pmax(e), 2/T for , where T is the duration of the approximated isovolumic pressure curve, 0 rad for C, and 8 mmHg for EDP. The maximal number of iterations was set to 30,000, which was sufficient for complete convergence in all cases. The E_N(t_N) curves were resampled by linear interpolation with spacing t_N = 0.005 by a customized algorithm written in Visual Basic for Applications (Microsoft). All other calculations were done with the use of Excel software (Microsoft), using macros as needed.

Comparisons of multiple-beat and single-beat ESPVR estimations. Ees(Pmax), Ees(Padj), E_max(SB), and E_es(SB) for all 88 steady-state files were compared with E_es(MB) by applying analysis of agreement (1), and the same comparisons were done between V_0(SB) (Eq. 5) and V_0(MB). To determine whether the single-beat methods detected acute changes in contractility, Ees(Pmax), Ees(Padj), E_max(SB), and E_es(SB) were compared with E_es(MB) in the eight animals in group 2 receiving continuous dopamine infusions (9). Changes in contractility were assessed using a paired t-test.

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

Single-beat estimations of contractility (elastance) were compared with the corresponding multiple-beat values by application of an analysis of agreement (1). These results are outlined in Table 1 and Fig. 5. All of the single-beat methods showed the same characteristics in their ability to predict elastance in terms of bias and limits of agreement (LOA). The bias was quite low for all methods, except for Ees(Pmax) using Pmax(E) based on cutoff 300. The variability (expressed as 2SD or LOA), on the other hand, was high for all methods. The precision of the estimates, in terms of LOA, was better in the baseline/control group than in the total material, whereas bias was slightly lower in the total material than in the baseline/control group for all methods but one.

View larger version (24K): [in this window] [in a new window]   Fig. 5.   Scatterplots of analysis of agreement between single-beat and multiple-beat estimates. A: our modified version of the isovolumic pressure method (21) where Pmax(E) is calculated using a fifth-order polynomial function with cutoff 100 (see text). B: modified isovolumic approach (15). C: comparing V0(SB) based on Eq. 5 (Senzaki et al.), and V0(MB). D: agreement between multiple beat estimates and single beat estimates of elastance using Senzaki et al.'s method based on normalized elastance curves. E: comparison of elastance determined by the bilinear approximation method (16), and MB elastance.

Figure 2 shows E_N(t_N) curves (means ± SD) for each subgroup and for all files. According to the original description (14), the curves should be congruent. However, there was considerable variation among the group-specific curves with respect to SD (Fig. 4A), and the angle between the two parts of the curve describing the isovolumic phase and the ejection phase. The average bias between V_0(SB) (Eq. 5) and V_0(MB) was 0.1 ml and LOA were 28.5 and 28.6 ml (Fig. 5C).

In the single-beat method based on bilinearly approximated time-varying elastance curves (16), the parameter  is supposed to operate as a correction factor for differences in loading conditions (Fig. 1F). Shishido et al. (16) found  to correlate well to other load-dependent parameters. We found weak but significant correlations between  and the parameters EF and E_a, but  did not correlate to neither E_es(MB) nor EF_e (Fig. 6).

View larger version (30K): [in this window] [in a new window]   Fig. 6.   Relationship between  and Ees(MB) (A), arterial elastance (Ea) (B), ejection fraction (EF) (C), and effective EF (EFe) (D). The lines represent linear regression (R) and 95% confidence limits of mean. As shown,  was weakly correlated to Ea and EF. NS, not significant.

Ability of the single-beat estimates of contractility to detect inotropic changes. Heart rate, cardiac output, and mean arterial pressure were slightly higher during dopamine infusions compared with baseline (see Table 1 in Ref. 9). Contractility assessed as dP/dt_max increased from 1,366 mmHg/s at baseline to 2,470 mmHg/s during dopamine infusions [dP/dt_max = 1,104 ± 397 mmHg/s (means ± SD), P < 0.001]. V_0(MB) increased from 12.4 ml at baseline to 1.4 ml during dopamine infusion (P = 0.02).

View larger version (16K): [in this window] [in a new window]   Fig. 7.   Effect of inotropic stimulation on left ventricular elastance. , elastance at baseline;  elastance during continuous dopamine infusion. A: multiple-beat-derived values. B: isovolumic pressure-based method (21) when Pmax(E) was calculated by the polynomial function with cutoff 100 (see text). C: adjusted pressure approach by Shih et al. D: using Senzaki et al.'s method based on normalized elastance curves. E: bilinearly approximated time-varying elastance method (16).

Evaluation of the multiple-beat recordings. Two consecutive multiple-beat measurements during preload reduction were done in 71 of the 88 recordings, and mean value was used as E_es(MB). The discrepancy between the two measurements, which reflects the reproducibility of multiple-beat elastance, can be expressed in percent by a modified analysis of agreement

(9)

The mean discrepancy was 5.9% for the baseline group (n = 35) and 7.9% for the intervention groups (n = 36) (Fig. 8). The multiple-beat files showed a highly linear ESPVR with a median r² at 0.98, and a 95% confidence interval of 0.97-0.99 (n = 159).

View larger version (19K): [in this window] [in a new window]   Fig. 8.   Bland-Altman plot showing the agreement between two consecutive multiple-beat elastance measurements during preload reduction. Top, baseline recordings. Bottom, results after intervention.

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

We have evaluated four different methods using PV-based single-beat estimation of contractility, and demonstrated that all methods predict elastance with good accuracy. The accuracy reflects the systematic errors of a test, and can be corrected as needed. However, all methods had low and insufficient precision, an important parameter when evaluating any diagnostic test. The precision reflects the random errors of the test, and is directly related to the predictive value of the test.

The other crucial requirement for a single-beat-based contractility index is the ability to detect acute changes in contractility induced by inotropic stimulation or pathological alteration (i.e., stunning). An index fulfilling these criteria would for instance open for a much higher reliability in the clinically important distinction between acute pump failure and suboptimal loading conditions. However, all evaluated indexes failed to comply with these requirements.

In a study by Regen et al. (11), cardioactive drugs did not affect the shape of the isovolumic pressure curve. We observed that during dopamine infusion, the pressure curve was considerably steeper during isovolumic contraction than during isovolumic relaxation compared with baseline. This implies that Pmax(e) (21) is shifted to the left (Fig. 1A). However, the rigid nature of the sine curve with respect to symmetry makes it incapable of reflecting this leftward shift, and we observed a minimum mean-square-error increase (total minimum square error divided by the number of points) in the curve fitting with a factor of 4.8 ± 2.2 (mean ± SD) compared with baseline (P = 0.002). As a consequence, mean increase in Pmax(e) was only 14 mmHg, and combined with a decrease in end-systolic volume, Ees(Pmax) turned out to be negative (Table 2). In contrast to the sine curve, a fifth-order polynomial function reflected this leftward shift of nadir and subsequently the increased source pressure. With the use of the fifth-order polynomial function with cutoff 100 and 100 mmHg/s to calculate Pmax(E), the method did detect increased inotropy as a response to continuous dopamine infusion (Table 2 and Fig. 7B). The increase was small, although statistically significant (0.6 ± 0.6 mmHg/ml, P = 0.03). This observation points to the experience gained from developing single-beat methods; empirical adaptation to the methods will be precise but probably not reproducible. Shih et al.'s (15) computer algorithm also reflects this skewness of the pressure wave because the unadjusted pressure is equally affected by the contraction and relaxation waveforms. However, even with the use of this method, increased inotropy during dopamine infusion could not be observed.

A crucial assumption in Senzaki et al.'s (14) method, is that the average E_N(t_N) curve shows very little variation in the region used to estimate the single-beat elastance. The authors found this variation, expressed as SD, to be 0.05 in their optimal time frame. In our analysis, SD was 0.07. However, there were considerable differences between the subgroups. SD in the baseline or control group was 0.05, whereas in the dopamine group it was 0.09 (Fig. 4A). In Senzaki et al.'s (14) material, the curves for each patient group and/or test condition showed very little variability with respect to the shape of the curves. In our study, there was a considerable variability among the groups, especially with respect to the angle between the isovolumic phase and the ejection phase. The same observation was made by Shishido et al. (16) when loading conditions and contractility was significantly changed. These authors subsequently incorporated this angle into their algorithm for single-beat estimation of contractility.

To quantify the influence of the variability of the E_N(t_N) curve on the accuracy and precision of the single beat elastance estimates, we recalculated E_max(SB) for all the 88 files applying the E_N(t_N) values obtained from the baseline group. We also recalculated E_max(SB) applying subgroup-specific E_N(t_N) values. In all subgroups, the accuracy improved when we used the subgroup-specific E_N(t_N), but the precision of the estimates remained unchanged (Fig. 9). The explanation for this is that accuracy largely depends on the difference between the subgroup-specific E_N(t_N) curve and the total E_N(t_N) curve (i.e., the difference in shape of the curves), whereas the precision of the estimate is a function of the variance of E_N(t_N) within each group in the optimal time frame.

View larger version (16K): [in this window] [in a new window]   Fig. 9.   Agreement between multiple-beat and single-beat elastance based on Senzaki et al.'s method showing the impact of diversity among group-specific EN(tN) curves. , Mean bias; , mean ± 2SD. SB-MB = difference between single-beat- and multiple-beat-derived elastance. 1, Results when applying the total EN(tN) curve (Fig. 2A); 2, results when applying the baseline/control EN(tN) curve (Fig. 2B); 3, results when applying the subgroup-specific EN(tN) curve (Fig. 2, C-E).

To summarize, our group-specific E_N(t_N) curves showed less congruence than those of Senzaki et al. (14). The E_N(t_N) curve for our baseline or control group showed about the same variance as the E_N(t_N) curve representing their total material, whereas the variance of E_N(t_N) in our intervention groups were considerably larger. Consequently, the assumption that normalized elastance is constant among individuals of the same species, and independent of pharmacological interventions, was not confirmed in our material.

Because Eq. 5 is inherently unstable throughout most of the cycle (Fig. 3), the V₀ estimate has to be made in an optimal time frame of the normalized PV loop. We found this time frame to be 0.40  t_N  0.50. A possible explanation for the difference from 0.25  t_N  0.35 in the original study, is that the relationship between preejection time and ejection phase probably is greater in pigs than in humans. Similar to the observations of Senzaki et al. (14), we found the optimal time period to cover late isovolumic contraction and early ejection.

In the bilinear approximated time-varying elastance model of Shishido et al. (16),  reflects the relation between the slopes of the elastance curve in the ejection phase and PEP. In this method,  is regarded as an important factor (Eq. 8) because it is sensitive to changes in contractility and loading conditions, and serves as a correction factor for the lack of congruence between individual elastance curves. These authors therefore examined the dependence of  on the parameters EF, EF_e, E_es, and E_a. They found  to be tightly positively correlated to EF_e and EF.  also correlated positively with E_es and negatively with E_a. We found only weak correlations between  and two of the parameters (EF and E_a), and no correlation with EF_e and E_es(MB) (Fig. 6). This is not surprising, because all of these parameters can influence , but to a variable extent in individual elastance curves.  is also influenced by other properties of the PV loop. Small artefacts in the PV loop, as for instance a blunted upper right corner, increases  considerably. Furthermore,  is influenced by afterload, and we observed that a significant pressure increase during ejection was associated with a high -value.

In this study, we have used hemodynamic data from pigs, which have lower contractility indexes than dogs and humans. ESPVR are low with E_es,MB = 3.4 ± 1.1 (means ± SD). It is possible that the agreement between multiple-beat and single-beat end-systolic elastance is better in species with higher elastance values. However, Shih et al. (15) reported a mean (single beat + multiple beat)/2 of 19.5 mmHg/ml, a bias of 1.42 and LOA of 10.98 and 8.15 in their material of 16 patients, indicating that the lack of precision of the estimate is independent of the absolute slope of the elastance curve.

In our group of baseline/control loops, we have included 21 measurements from 7 pigs, i.e., 3 measurements from each animal (7). These were longitudinal time controls with repeated measurements. The use of repetitive measurements could potentially introduce a bias. However, a time span of 90 min will induce different physiological states in these pigs, and should therefore allow for renewed inclusion in loop assessments.

From load-dependent to load-independent indexes and back again. Since the introduction of elastance as a measurement of contractility, it is now generally agreed that this index is not completely frequency or load independent (5, 12, 17, 22), and that the relation has contractility-dependent curvilinearity (3, 17, 22). Despite this, it reflects the contractile state of the left ventricle rather well within a physiological range of heart rate and loading conditions, given constant V₀ (5, 10, 17). During the past decade, there have been numerous attempts to make it clinically applicable by making invasive procedures obsolete, and the focus has been on predicting end-systolic elastance from one single cardiac cycle. However, the transition from measurements based on multiple beats during preload alteration to less invasive measurements based on one single beat has its cost. All of the single-beat methods contain elements that are highly load dependent, as dP/dt_max (15, 21), ET/PEP (14, 16), SV (14-16, 21), and EDV (21). Load dependency is thus reintroduced, and we are basically left with empirical indexes composed of different load-dependent measures combined with pressure and volume data at critical time points. It is therefore more meaningful to regard the single-beat indexes as load-dependent approximations of the load-independent elastance. These indexes would be clearly insufficient in predicting contractility in a large range of load and frequency situations. However, they are designed to work in a clinical setting within a physiological range of load, heart rate, and contractility, and the influence of these parameters would therefore not necessarily be of significant magnitude (3, 10). Despite this, they seem too susceptible to "noise" from varying frequency and loading conditions.