Myocardial performance index, a Doppler-derived index of global left ventricular function, predicts congestive heart failure in elderly men

Johan Ärnlöva,*, Erik Ingelssona, Ulf Risérusb, Bertil Andrénc and Lars Lindc,d

a Department of Public Health and Caring Sciences, Section of Geriatrics, Uppsala University, Uppsala, Sweden
b Department of Public Health and Caring Sciences, Section of Clinical Nutrition Research, Uppsala University, Uppsala, Sweden
c Department of Medical Sciences, Uppsala University, Uppsala, Sweden
d Astra Zeneca R&D, Mölndal, Sweden

Received 24 June 2004; revised 8 October 2004; accepted 14 October 2004 * Corresponding author. Present address: Department of Public Health and Caring Sciences, Section of Geriatrics, Uppsala University, P.O. Box 609, S-751 25 Uppsala, Sweden. Tel.: +46 18 6117972; fax: +46 18 6117976 (E-mail: johan.arnlov{at}pubcare.uu.se).

See page 2185 for the editorial comment on this article (doi:10.1016/j.ehj.2004.10.017)


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
AIMS: There is limited data on echocardiographic and Doppler indices of cardiac function as predictors for congestive heart failure (CHF) in the general population. Myocardial performance index (MPI, also denoted TEI-Doppler index) reflects both left ventricular (LV) systolic and diastolic function.

METHODS AND RESULTS: We compared eight different echocardiographic and Doppler indices of cardiac function as predictors of CHF using a population-based cohort of 552 seventy-year-old men without CHF and significant valve disease at baseline (median follow-up time 8.2 years).

In a stepwise multivariable Cox proportional-hazard analysis including the different indices of cardiac function, high MPI (above the 90th percentile of MPI [≥0.91]), abnormal LV wall motion score index and a pseudo-normalized/restrictive E/A-ratio pattern independently predicted future CHF morbidity. After adding traditional CHF risk factors (age, previous myocardial infarction, hypertension, diabetes mellitus, hyperlipidaemia, smoking, LV hypertrophy and body mass index) to the above model, only a high MPI remained a significant predictor (hazard ratio 4.72, 95% CI 1.75–12.76, p=0.002).

CONCLUSION: MPI provides important prognostic information for the risk of future CHF, beyond other measurements of cardiac function and traditional heart failure risk factors in elderly men. MPI seems to be a clinically relevant indicator of cardiac function and may prove to be a valuable tool in assessing the risk of future CHF.

Keywords Echocardiography; Heart failure; Left ventricular dysfunction


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
Congestive heart failure (CHF) is a major cause of morbidity and mortality.1 Previous studies have demonstrated that patients with heart failure may have gone through a phase of asymptomatic left ventricular (LV) dysfunction, where objective LV measurements reveal impairment of cardiac contractility but overt heart failure is not present.2–5 There is limited data on the predictive value of different echocardiographic and Doppler measurements for the development of heart failure in the general population. A few recent studies have shown indices of both LV systolic and diastolic function to be predictors for subsequent heart failure.2–5

The Doppler-derived myocardial performance index (MPI, also denoted the TEI-Doppler index), a fairly new index of combined systolic and diastolic function, is defined as the sum of isovolumic contraction time and isovolumic relaxation time divided by the ejection time.6 MPI has previously been shown to be a sensitive indicator for symptomatic heart failure in a cross-sectional study7 but whether MPI predicts future development of heart failure independently of other echocardiographic measurements is still to be examined.

The aim of this study was to investigate the ability of eight different echocardiographic and Doppler measurements of LV function to predict the development of heart failure in a community-based sample of elderly men, free of heart failure and valvular disease at baseline. We further examined whether the predictive value of these indices of LV function were independent of each other and of traditional risk factors for heart failure.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
Study population
A re-investigation of the population-based ULSAM-cohort (The Uppsala Longitudinal Study of Adult Men) was performed in 1990–1994 when the subjects were {approx}70 years of age. A wide spectrum of metabolic, haemodynamic and anthropometric measurements was performed, as previously described8,9 (www.pubcare.uu.se/ulsam/). An echocardiographic examination was performed in the first 579 consecutive men. Of these, nine subjects had previously been hospitalized for heart failure and 18 were found to have clinically significant valve disease leaving 552 subjects who comprise the present study population (baseline characteristics, see Table 1). All subjects gave written consent and the Ethics Committee of Uppsala University approved the study.


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Table 1. Baseline characteristics
 
Follow-up
The follow-up was performed between the examination date of the participants (1990–1994) to the end of 2000. The subjects had a median follow-up time of 8.2 years (inter-quartile range 7.2–8.7 years, total range 0.8–9.8 years), contributing to 4104 person-years. The possible cases were selected by linking the ULSAM participants to the Swedish hospital discharge register by the unique personal identification number of all citizens of Sweden. None of the subjects were lost to follow-up. As a possible diagnosis of heart failure, we considered ICD heart failure codes 428 (ICD-9), I50 (ICD-10) and hypertensive heart disease with heart failure, I11.0 (ICD-10). The medical records from those who had received an in-hospital heart failure diagnosis were evaluated by a review board consisting of two experienced doctors. The review board defined the heart failure cases according to the definition proposed by the European Society of Cardiology.10 During follow-up 47 of 552 subjects (rate 1.1/100 PYAR) had a heart failure event and 103 subjects died (45 deaths were from cardiovascular disease).

Echocardiography
Two-dimensional (2D) echocardiography and Doppler examination was performed with a 2.5 MHz transducer (Sonos 1500, Hewlett Packard Andover, MA, USA). All examinations were performed with the subjects in the standard left lateral position. An experienced physician (B.A.) did both the examination and the reading of the images, unaware of the clinical data of the subjects.

LV dimensions were measured with M-mode using a leading edge-to-edge convention. The measurements included LV diameter in end-diastole and end-systole. LV volumes were calculated according to the Teichholz M-mode formula volume=7D3/(2.4+D), D=diameter11,12 and from that, ejection fraction was calculated (LV end-diastolic volume–LV end-systolic volume)/LV end-diastolic volume.

LV mass was determined by using the M-mode formula of Troy according to the recommendations by the American Society of Echocardiography (ASE).13 LV mass was divided with body surface area giving the LV mass index.

LV wall motion score was calculated as the mean score in a 16-segment model of the left ventricle using 2D images, in which each segment was given a score in the range 1–4, using 1=normal wall motion, 2=hypokinetic wall motion, 3=akinetic and 4=paradoxical wall motion (dyskinesia).14 In the statistical analyses, LV wall motion score was dichotomized into two groups (LV wall motion score=1 or >1).

Pulsed Doppler from the apical position was used to measure LV inflow through the mitral valve. The peak velocities of the early rapid filling (E-wave) and filling during atrial systole (A-wave) were recorded and the E/A-ratio was calculated. The deceleration time was measured as the interval between the peak of the E-wave and the point at which the descending segment of the E-wave crosses the zero velocity line. LV isovolumic relaxation time was measured as the interval between aortic valve closure and the onset of mitral flow. E/A-ratio was both analysed as a continuous variable and stratified variable (normal E/A-ratio [>0.65], low E/A-ratio [<0.65] and pseudo-normalized/restrictive E/A-pattern [E/A-ratio >2.5 or E/A-ratio >1.2 with LV systolic dysfunction]). In analyses with the E/A-ratio as a continuous variable, subjects with a pseudo-normalized/restrictive pattern were excluded (n=8).

LV ejection time (et) was measured from the onset to the end of LV outflow velocity pattern. The mitral closing-to-opening time (a) was measured as the interval from the end to the onset of the mitral inflow velocity pattern. Mean values of three measurements were used and the MPI was calculated as (a–et)/et.15

LV outflow tract (LVOT) diameter was obtained from a parasternal long axis view, while the flow velocity integral (FVI) was determined by Doppler from the apical view. From these two variables, stroke volume was calculated by ([p*LVOT2]/4)*FVI.

Cardiac output was calculated as stroke volume*heart rate. Stroke index and cardiac index were obtained by dividing stroke volume and cardiac output with body surface area, respectively.

A complete repeat investigation was performed in 22 randomly selected subjects approximately one month after the initial investigation. Intra-class correlation co-efficients for the M-Mode measurements: LV mass index 0.65 and ejection fraction 0.52. For the Doppler measurements the intra-class correlation co-efficients were as follows: E/A-ratio 0.72, isovolumic relaxation time 0.84, stroke volume 0.82, cardiac output 0.74, deceleration time 0.71, and myocardial performance index 0.66. The kappa-value for left ventricular wall motion score (considered a dichotomous variable) was 1.16

Heart failure risk factors
Seven traditional heart failure risk factors were selected and defined as follows: hypertension (systolic blood pressure >160 mm Hg and/or diastolic blood pressure >95 mm Hg and/or anti-hypertensive medication), hyperlipidaemia (serum cholesterol >6.5 mmol/l and/or serum triglycerides >2.3 mmol/l and/or lipid lowering medication), diabetes (blood glucose ≥6.7 mmol/l (fasting) and/or ≥10.0 mmol/l (2-h oral glucose tolerance test value) and/or anti-diabetic medication), LV hypertrophy (LV mass index >150 g/m2), smoking, history of previous myocardial infarction, age and body mass index.

Statistical methods
Logarithmic transformation was performed when W (according to Shapiro–Wilk's test) was <0.95. Two-tailed 95% confidence intervals and p values were given, with p<0.05 regarded as significant. Subjects who developed or did not develop CHF during follow-up were compared at baseline with ANOVA and {chi}2-analyses.

Assumption of linearity was assessed by visual inspection of CHF incidence rates in deciles of the continuous variables. No obvious deviations from linearity were seen in all continuous explanatory variables except for ejection fraction and MPI (see Fig. 1 for incidence rates in deciles of MPI). Based on this we used these variables as dichotomous variables in all analyses (the lowest decile vs. decile 2–10 for ejection fraction (cut-off value 0.51) and decile 1–9 vs. the highest decile for MPI (cut-off value 0.91).



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Fig. 1 Incidence rates of congestive heart failure (CHF) in different deciles of myocardial performance index (MPI).

 
The prognostic value of the variables, were investigated with Cox proportional hazard ratios. Proportionality of hazards was confirmed by visually examining Nelson–Aalen curves. We used three sets of models in a hierarchical fashion: (A) unadjusted analyses; (B) multivariable-adjusted analyses using the following baseline covariates: age, previous myocardial infarction, hypertension, diabetes mellitus, hyperlipidaemia, smoking, left ventricular hypertrophy and body mass index; (C) multivariable Cox proportional hazard backwards-stepwise analyses. Only explanatory variables with univariate p<0.01 were analysed in the stepwise model in order to limit the risk of type 1 error (ejection fraction ⩽0.51, abnormal LV wall motion score index, E/A-ratio pattern and MPI ≥0.91). A level of p<0.05 was used for inclusion/exclusion in the stepwise model. As a final step, the covariates in model B were added to the model.

STATA 6.0 (Stata Corporation) was used for analyses.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
Crude and multivariable analyses
In univariate Cox proportional hazard analyses, indices reflecting LV function (ejection fraction ⩽0.51, abnormal LV wall motion score index, stroke index, E/A-ratio and MPI ≥0.91) were all significant predictors for heart failure morbidity even after adjusting for traditional risk factors for heart failure (age, previous myocardial infarction, hypertension, diabetes mellitus, hyperlipidaemia, smoking, left ventricular hypertrophy and body mass index (Table 2)). Cardiac index, isovolumic relaxation time and deceleration time did not predict heart failure morbidity.


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Table 2. Indices of cardiac function as predictors for heart failure morbidity
 
Stepwise multivariable analyses
All measurements that were found to be independent predictors of heart failure morbidity with p<0.01 in the univariate analyses were included in a multivariable Cox proportional hazard backwards-stepwise analysis (ejection fraction ⩽0.51, abnormal LV wall motion score index, E/A-ratio pattern and MPI ≥0.91). In this multivariable model, MPI ≥0.91 (HR 4.00, 95% CI 1.52–10.44, p=0.005), abnormal LV wall motion score index (HR 2.93, 95% CI 1.16–7.42, p<0.03) and a pseudo-normalized/restrictive E/A-ratio pattern (HR=9.45, 95% CI 1.81–49.52, p=0.008) predicted future heart failure morbidity. When adjusting for above-mentioned traditional heart failure risk factors, only MPI ≥0.91 remained a significant predictor (HR 4.72, 95% CI 1.75–12.76, p=0.002, Fig. 2). The interaction term between MPI and myocardial infarction at baseline was not significant.



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Fig. 2 Nelson Aalen curve of cumulative incidence for heart failure morbidity in 70-year-old men, by myocardial performance index ≥0.91 vs. myocardial performance index <0.91. Data are adjusted for age, previous myocardial infarction, hypertension, diabetes mellitus, hyperlipidemia, smoking, left ventricular hypertrophy and body mass index, abnormal LV wall motion score index and E/A-ratio pattern.

 

    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
In this community-based prospective study of elderly men, we found several echocardiographic and Doppler measurements of LV function to be risk factors for heart failure, independently of traditional risk factors. In multivariable Cox proportional hazard analyses, MPI provided prognostic information beyond that of other measurements of cardiac function and traditional risk factors.

Our knowledge of the natural history of heart failure indicates that both asymptomatic LV systolic and diastolic dysfunction can precede the onset of overt heart failure.2–5 In a previous study comparing MPI to simultaneous cardiac catheterization measurements of left ventricular function, MPI was found to reflect both systolic and diastolic function.6 The ratio of isovolumic contraction time and ejection time was closely correlated to +dP/dt (reflecting systolic function) and the ratio of isovolumic relaxation time and ejection time was closely correlated to –dP/dt and s (reflecting diastolic function). Therefore, MPI may be considered the sum of an index reflecting systolic function and an index reflecting diastolic function. Thus, the superior predictive capacity of MPI in this study could be explained by the fact that MPI reflects global LV function, while other measurements are limited to reflect mainly either LV systolic or diastolic function. It should be pointed out that as our heart failure diagnosis were based on data from the hospital discharge register we could not distinguish between systolic heart failure and heart failure with preserved systolic function.

The isovolumic contraction time corresponds to when calcium enters the myoplasm from the sarcolemma, while isovolumic relaxation time reflects the removal of Ca2+ from the myoplasm by Ca2+-ATPases. Consequently, MPI mirrors both the depolarization and repolarization process. It seems like changes in cellular Ca2+ handling in the myocardium underlie much of the abnormal contractility and relaxation.17 In the failing heart, the contraction and relaxation becomes slower18 explaining why MPI increases with deterioration of cardiac function. This study supports the notion of sub-clinical depolarization (and repolarization) defects to be early phenomena in the natural history of heart failure.

MPI has previously been shown to have prognostic value in patients with dilated cardiomyopathy, amyloidosis, coronary heart disease and symptomatic heart failure as well as in the general population.19–25 MPI is a reliable and easily assessable measurement of LV function, and as such, suitable for large-scale examinations.24 Nevertheless, further studies are needed in order to define the role of MPI in clinical practice. Clinically significant age- and gender-specific cut-off points need to be defined; furthermore it has to be determined if pharmacological and/or non-pharmacological interventions lower MPI and whether lowering MPI modifies the risk associated with a high MPI.

It is noteworthy that the most commonly used index of LV function, LV ejection fraction, did not remain an independent predictor in the multivariable stepwise analyses. In 1990, when the echocardiographic examinations in this study started, ejection fraction measurement using the Teichholz M-mode formula was most often used. However, ejection fraction measured by M-mode has its limitations, as the Teichholz formula does not take into account any local hypokinesia outside the segments where the LV end-diastolic and LV end-systolic diameter are measured. We cannot exclude that ejection fraction measured by the biplane Simpson rule method would have performed better in the multivariable analyses.

There are some other limitations in the present study. As we only examined men of similar age with the same ethnic background, the results may have limited generalizability to women and other age and ethnic groups. As we identified the potential heart failure cases via the hospital discharge registry, we could not identify heart failure patients that had not been hospitalized. Since this is a retrospective population-based study it is not possible to differentiate between systolic and diastolic heart failure, as an echocardiography was not available at the time of diagnosis for most cases. Furthermore, as we performed many statistical tests there is always a risk for type 1 error and as there were differences in the degree of missing data between the different indices of LV function there may be an increased risk for selection bias. However, as MPI consistently predicted CHF in all models, regardless of sub-sample, it is not probable that our findings are due to either type 1 error or selection bias.

In conclusion, MPI provides prognostic information independently of other measurements of cardiac function and of traditional risk factors for heart failure, in a population sample of elderly men free from heart failure and valvular disease at baseline. Therefore, MPI seem to be a clinically relevant measurement of LV global function and may prove to be a valuable tool in assessing the risk of developing heart failure.


    Conflicts of interest
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 
There are no potential conflicts of interest related to the manuscript for any of the authors.


    Acknowledgments
 
Axel and Margaret Ax:son Johnson Foundation, Ernfors Foundation, King GustafV and Queen Victoria Foundation, Primary Health Care in Uppsala County, Royal Scientific Society Foundation (Kungliga vetenskapssamhällets fond), the Swedish National Association against Heart and Lung Disease (Hjärt-Lungfonden), Thuréus Foundation, and Uppsala University.


    References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflicts of interest
 References
 

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