This Article Abstract Full Text (PDF) All Versions of this Article: 115/10/1252    most recent CIRCULATIONAHA.106.640334v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ingul, C. B. Articles by Marwick, T. H. Search for Related Content PubMed PubMed Citation Articles by Ingul, C. B. Articles by Marwick, T. H. Pubmed/NCBI databases Compound via MeSH Substance via MeSH Hazardous Substances DB ATROPINE Related Collections Chronic ischemic heart disease Echocardiography

(Circulation. 2007;115:1252-1259.)
© 2007 American Heart Association, Inc.

Imaging

Incremental Value of Strain Rate Imaging to Wall Motion Analysis for Prediction of Outcome in Patients Undergoing Dobutamine Stress Echocardiography

From the University of Queensland, Brisbane, Australia (C.B.I., E.R., T.H.M.), and the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway (S.A.S.).

Correspondence to Thomas H. Marwick, MD, PhD, University of Queensland, Department of Medicine, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia. E-mail tmarwick{at}soms.uq.edu.au

Received May 24, 2006; accepted November 10, 2006.

	Abstract

Top Abstract Introduction Methods Results Discussion References

 
Background— Wall motion score at dobutamine stress echocardiography is an independent predictor of mortality. We sought to determine whether quantification of DSE by strain rate imaging was incremental to wall motion score for predicting outcome.

Methods and Results— In 646 patients undergoing dobutamine stress echocardiography for the evaluation of known or suspected coronary disease, customized software was used to automatically measure peak systolic strain rate (SR_s) and end-systolic strain (S_es) in 18 segments. Results were expressed as the number of abnormal segments and the mean SR_s and S_es per patient. All-cause mortality was identified over 7 years of follow-up (mean, 5.2±1.5 years). Contributions of clinical, wall motion, and SR_s and S_es data to outcome were analyzed with Cox models, which also were used to define cut points for SR_s and S_es. Ischemia (new or worsening wall motion abnormalities) was detected in 45%, and 39% had a previous myocardial infarction. In patients with no ischemia, annualized mortality without and with previous myocardial infarction were 2% and 3% compared with 5% in patients with ischemia. Peak wall motion score index, mean SR_s, segmental S_es, and segmental SR_s were all predictors of mortality, but only segmental SR_s (hazard ratio, 3.6; 95% CI, 1.7 to 7.2) was independently predictive. In sequential Cox models, the model based on clinical data (overall ², 12.7) was improved by peak wall motion score index (18.4, P=0.002) and further increased by either segmental SR_s (31.8, P<0.001) or mean SR_s (25.7, P=0.009).

Conclusions— Segmental analysis by SR_s, derived from automated strain rate imaging analysis of dobutamine stress echocardiography response, offers prognostic information that is independent and incremental to standard wall motion score index.

Key Words: echocardiography • prognosis • stress

	Introduction

Top Abstract Introduction Methods Results Discussion References

 
Abnormal wall motion (WM) at dobutamine stress echocardiography (DSE) is an independent predictor of mortality in patients with known or suspected coronary artery disease (CAD), and risk increases in parallel with the extent of WM abnormality.^1,2 Although DSE is used widely to assess CAD, its interpretation is demanding, and its accuracy depends on the experience and training of the reader.³ Despite technical advances and digital image processing and display, both its reproducibility and accuracy are dependent on image quality. DSE also is limited by its relative insensitivity for mild single-vessel disease, low sensitivity for recognizing the presence of multivessel coronary disease, and difficulty in detection of ischemia within areas of abnormal resting WM. A more objective technique could overcome many of these limitations.

Clinical Perspective p 1259

Quantification of DSE with tissue Doppler is less dependent on 2-dimensional image quality than quantification from 2-dimensional echocardiography, and several studies have shown the technique to be feasible for DSE using either velocity or strain rate imaging (SRI).^4–6 SR and strain can characterize regional myocardial deformation at rest and can quantify normal or abnormal regional function during DSE.^5,7,8 These results may be compromised by many pitfalls, however, including reverberations, misalignment, and angulation. Indeed, none of the tissue Doppler–derived parameters are completely objective because the region of interest is set manually and therefore may be colored by the assessment of WM. To objectively analyze regional myocardial function on the basis of tissue Doppler, we have developed a quick and feasible automated SRI method that includes tracking the region of interest laterally by using the speckle pattern and axially by tissue Doppler.⁹ In the tissue Doppler method, SR is derived from the velocity gradient of 2 points at a fixed distance along the ultrasound beam, and strain is measured by the temporal integration of SR. The automated method for measuring peak systolic strain rate (SR_s) has been shown to increase the sensitivity of DSE compared with WM.⁸

The potential of SRI to predict mortality has not previously been studied. Therefore, the objective of the present study was to ascertain whether quantification of DSE by SRI is incremental to WM analysis for predicting the outcome of patients with known or suspected CAD.

	Methods

Top Abstract Introduction Methods Results Discussion References

 
Patient Selection
Between 1998 and 2000, 787 consecutive studies were performed using DSE and simultaneous tissue Doppler acquisition for evaluation of myocardial ischemia at Princess Alexandra Hospital (Brisbane, Australia). The clinical characteristics of these patients are listed in Table 1. The most common reasons for referral were evaluation after infarction (n=197, 25%), evaluation of chest pain or dyspnea (n=189, 24%), and preoperative noncardiac risk assessment (n=165, 21%).

If a patient had >1 DSE during the time period (n=17), only the first test was entered into the analysis. In all, 68 patients were excluded from the analysis because of incomplete acquisition or archiving problems (n=23) or technical problems precluding SR measurement, including low frame rate (<75 frames per second; n=25), poor image quality (n=5), or failure of the tracking algorithm (n=10). All-cause mortality was identified over 7 years of follow-up (mean, 5.2±1.5 years), which was completed by the beginning of 2006. Follow-up data were not available in 56 patients (7%). Thus, the final study sample consisted of 646 patients (241 women; age, 61±12 years).

Stress Protocol
After a resting echocardiogram was obtained, a standard DSE protocol was performed with incremental dobutamine infusion rates of 5, 10, 20, 30, and 40 µg · kg^–1 · min^–1 every 3 minutes. Patients who did not achieve 85% of the age-predicted maximal heart rate (220–age) were given atropine in 0.3-mg increments up to 1.2 mg until target heart rate was achieved. Cardiac rhythm and blood pressure were monitored before and during the test. The stress test was terminated because of completion of the protocol, severe ischemia (severe angina, extensive new WM abnormalities, horizontal or downsloping ST-segment depression >2 mm, ST-segment elevation >1 mm in patients without prior myocardial infarction [MI]), systolic blood pressure >240 mm Hg or <100 mm Hg, serious ventricular arrhythmia, or patient intolerance of side effects. The test was considered nondiagnostic if the patient failed to achieve target heart rate without inducible WM abnormality.

Echocardiographic Acquisition
Cine loops from LV apical views (4-chamber, 2-chamber, and long-axis views) were recorded simultaneously in both tissue second-harmonic mode and tissue Doppler mode (Vivid 5, GE Vingmed Ultrasound, Horten, Norway). Images were recorded at baseline, low dose, peak, and recovery in a digitized quad-screen format. The pulse repetition frequency was between 1 and 1.5 kHz, and the frame rate (both for tissue Doppler and B-mode) ranged from 75 to 133 per second (median, 90). The number of samples along the beam ranged from 102 to 258 (median, 150) for tissue Doppler and 188 to 486 (median, 420) for B-mode imaging. The number of beams ranged from 10 to 28 (median, 16) for tissue Doppler and from 20 to 87 (median, 41) for B-mode imaging. Echo data were stored digitally for subsequent offline analysis.

Echocardiographic Analysis
Two dimensional gray-scale echo images, interpreted by an experienced observer, were scored as normal (score=1), hypokinetic (2), severely hypokinetic (2.5), akinetic (3), or dyskinetic (4). Infarction was recognized on the basis of severe hypokinesis, akinesis, or dyskinesis at rest. Ischemia was identified in the presence of new or worsening WM abnormality in 1 segments during DSE, and a study was identified as abnormal if there were scarred segments with or without ischemia. WM score index (WMSI), number of ischemic segments, and number of scarred segments also were compared. A negative study was characterized by a normal response in all segments.

SRI Analysis
Automated measurements of SRI variables used a Matlab-based (MathWorks Inc, Natick, Mass) custom-made program described earlier by our group.⁹ All SRI measurements were obtained by a single observer blinded to all results (clinical and WM). Segments were tracked according to position, orientation, and length throughout the cycle, axially (along the ultrasound beam) by tissue Doppler data, and laterally by speckle tracking. The difference in acquisition time for B-mode and tissue Doppler imaging data was taken into account and compensated for by temporal interpolation in the speckle tracking algorithm. The segment was discarded if the speckle pattern region failed to track properly or if the gray-scale image showed regions with missing ultrasound data (dropouts) or large reverberations. The displacement of the kernel region was used to check the tracking; ideally, this should be 0 because the kernel returns to the same baseline position by the end of the cardiac cycle. A displacement >2 mm indicated poor tracking.

The timing of aortic valve closure was defined automatically with tissue Doppler imaging.¹⁰ For the purposes of the present study, SRI parameters were measured using velocity data. This method involved automatic placement of a region of interest in the center of the basal and mid segments at end diastole and the basal part of the apical segments to limit the effects of angulation toward the apex. The segment was tracked throughout the cardiac cycle as described. Longitudinal SR was calculated from the velocity gradient along the ultrasound beam, and strain was calculated as the temporal integral of SR corrected from Eulerian SR to Lagrangian strain, both angle dependent.¹¹ The strain length (distance for velocity gradient calculation) was 10 to 15 mm; axial averaging of 1 mm and temporal averaging of 10 ms were used for the analysis.

Peak SR_s was determined as the maximal negative SR value during ejection; end-systolic strain (S_es) was defined as the magnitude of strain at aortic valve closure. Measurements were made in 18 segments from 3 apical views at peak stress, all blinded from WM and clinical results. SR and S_es results were expressed as the mean value of all segments (mean SR_s or mean S_es) or the number of abnormal segments at risk (segmental SR_s or segmental S_es).

The series were randomly divided, with half of the patients (n=320) used for defining the optimal cut points of patients at low and high risk of mortality with respect to SR_s, S_es, WMSI, number of ischemic segments, and number of scarred segments. The number of abnormal segments at risk was defined as the number with an SR or S_es below the defined cut points. The remaining patients were used for testing these cut points.

Follow-Up Data
All-cause mortality was the primary end point. Follow-up was obtained by review of the patient’s hospital or family practice chart or telephone interview with the patient or relative. Patients were censored at the time of percutaneous or surgical revascularization.

Statistical Analysis
Categorical variables are expressed as proportions; continuous variables are expressed as mean±SD. ANOVA was used to compare the means between the 4 different groups with or without ischemia and previous MI, with Scheffé post hoc test used for multiple comparisons. Multiple measures per patient were corrected for within a general linear model. The McNemar test was used for comparison of categorical data for per-patient analysis of WM and SRI. Kaplan-Meier curves were used to estimate the survival function for time to death, and a log-rank test was used to compare differences between survival curves.

The independent association of clinical WMS and SRI variables with outcome was assessed in a series of Cox models. Cut points for each SRI variable and peak WMSI were assessed from the derivation sample initially using a receiver-operating curve analysis¹² to establish the approximate cut point and then entering this and adjacent cut points into a Cox model with the same clinical variables. Cox models were used to estimate the univariable hazard ratio of event for each variable.¹³

The incremental value of SRI variables over WMSI and clinical variables was assessed in a series of Cox models in which the first model consisted of fitting the clinical data entered as a block (diabetes mellitus, previous MI, age) in the test group. Each echo variable (rest and peak WMSI, mean SR_s, mean S_es, segmental SR_s, segmental S_es) was then entered into separate models in combination with this clinical block. Model ² was then compared for each Cox model. Analyses were performed using standard statistical software (SPSS version 12, SPSS, Chicago, Ill), and values of P<0.05 were considered statistically significant.

The authors had full access to and take responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

	Results

Top Abstract Introduction Methods Results Discussion References

 
Dobutamine Echocardiography
Adequate heart rate was achieved in 97% of the patients (n=640). In the remaining 21 patients, the protocol was terminated at a submaximal heart rate because of severe ischemia or side effects (hypotension, hypertension, ventricular arrhythmia, patient intolerance; n=15) or inability to attain target heart rate despite dobutamine and atropine (n=6). The test was nondiagnostic in 6 patients (1%), most commonly because of failure to reach target heart rate. Table 2 summarizes the hemodynamics and stress results for the 640 patients with adequate heart rate response.

A completely normal test at rest and stress was found in 267 patients (42%). Resting WM abnormality was present in 251 patients (39%), and in 87 patients (14%), this was the only abnormality. Ischemia by WM was found in 286 patients (45%); ischemia alone was detected in 122 patients (19%) and ischemia with prior MI in 164 patients (26%). The majority (82%) of patients with ischemia had >1 ischemic segment at peak stress.

A total of 11 520 myocardial segments were analyzed with tissue Doppler. Automated analysis of SR_s was possible in 93% of the segments compared with 87% for S_es. WM at peak identified 984 ischemic segments, 356 viable segments, and 979 scarred segments. Patients with a normal response to dobutamine and normal resting WM demonstrated significantly a higher magnitude of mean SR_s and mean S_es compared with the patients with ischemia and prior MI (Table 3).

Outcomes and Events During Follow-Up
In total, 107 patients (17%) died during the follow-up period. Myocardial revascularization was performed in 51 patients: 31 had PCI (5%) and 20 had CABG (3%). MI occurred in 17 patients (3%). Figure 1 summarizes the mortality according to the presence of ischemia and previous MI. Most events occurred in patients with ischemia and previous MI, with an annualized event rate of 4.7% compared with 1.9% in the group with a normal stress test and no previous MI (P=0.004).

View larger version (15K): [in this window] [in a new window]   Figure 1. Kaplan-Meier survival curves according to DSE responses. A, Subdivision of all patients into those with no ischemia at peak DSE and those with ischemia at peak DSE. Numbers of patients at risk for 2, 4, and 6 years are indicated. B, Subdivision of patients with a normal DSE response into patients without prior MI and patients with prior MI. C, Subdivision of patients who develop ischemia at peak DSE into patients without prior MI and patients with prior MI.

Clinical Predictors of Outcome
By univariable analysis, the clinical predictors of death were age, diabetes mellitus, hypertension, and previous MI; all but hypertension (P=0.1) were independently predictive in the multivariable model. Additional nonsignificant clinical variables in the univariable analysis included gender (P=0.56), smoking (P=0.89), hypercholesterolemia (P=0.46), angina (P=0.62), and congestive heart failure (P=0.2).

Deformation and Predictors of Mortality
The optimal cut point was approximated on the basis of receiver-operating characteristics curves, and this and adjacent values were then entered into a series of multivariate models, together with the same independent clinical predictors listed above. The optimal value was defined as the parameter that maximized the model ² for prediction of mortality (Figure 2). These were an SR_s >–1.2 seconds^–1 and S_es >–3.5% for mean values and an SR_s >–1.2 seconds^–1 and S_es >–9% for segmental values. Mean SR_s and segmental SR_s provided stronger models than WMSI. The identification of low risk with mean quantitative parameters, including mean SR_s >–1.2 (93%) and mean S_es >–3.5% (92%), was not significantly different from its allocation on the basis of peak WMSI >1.7 (90%). However, definition based on >10 segments with SR_s >–1.2 led more patients to be identified as low risk (95%; P=0.02 versus WMSI), whereas definition based on >3 segments with S_es >–9% led fewer to be identified as low risk (73%; P<0.001 versus WMSI).

View larger version (39K): [in this window] [in a new window]   Figure 2. Model 2 values for a series of Cox models using different cutoff values for the WM, average SRs (systolic strain rate) and Ses (end-systolic strain), and number of segments above a threshold level of SRs and Ses.

Ischemia by WM, number of ischemic segments, and peak WMSI were significant univariate predictors of death, as were all of the SRI variables at peak (except mean S_es) (Table 4). In a combined multivariable model including clinical and echocardiographic variables, the only significant independent predictor of death was segmental SR_s at peak, with a hazard ratio of 3.6 (P<0.001) (Table 4).

Incremental Value of WM and SRI for Predicting Death
A model using only clinical variables (diabetes mellitus, age, and previous MI) gave an overall ² of 12.7. The addition of WMS at either rest or stress increased the power of this model. The addition of the number of abnormal segments based on SR_s or S_es and mean SR_s further increased the prediction from the combination of clinical and stress WM data (Figure 3), although the addition of mean S_es was not significant (P=0.3).

View larger version (16K): [in this window] [in a new window]   Figure 3. Incremental value of SRI variables in a series of Cox regression models predicting all-cause mortality. The clinical variables (C; diabetes mellitus, age, previous MI) were entered together, followed by separate models by combination of these with rest WMSI (rW) and peak WMSI (pW). Then, the clinical variables with peak WMSI were entered together, and each SRI variable added in separate analyses. sSes indicates segmental end-systolic strain; mSRs, mean peak systolic strain rate; and sSRs, segmental peak systolic strain rate.

The incremental value of SR appears to be independent of whether WMS demonstrated scar or ischemia. In patients with abnormal WM at rest, mean peak SR_s added incremental value to both rest and peak WMSI, increasing ² from 4.2 to 10.0 (P=0.02) at rest and from 4.4 to 9.5 at peak (P=0.02). For the number of abnormal segments based on SR_s, the increase was more modest, from 3.9 to 8.3 (P=0.03) at rest and from 3.4 to 8.3 at peak (P=0.03).

	Discussion

Top Abstract Introduction Methods Results Discussion References

 
The present study demonstrates for the first time that the use of SRI provides additional information to WM analysis alone. Specifically, SR_s was found to be an independent predictor of all-cause mortality in patients with known or suspected CAD, incremental to other variables.

Current Status of SRI
SRI derived from high-frame-rate tissue Doppler data has been studied intensely over the last decade. Although the technique is limited by a number of technical challenges,¹⁴ it has been well validated as a means of accurate measurement of myocardial deformation^15,16 and has been shown to quantify regional function in infarct populations and to demonstrate recovery of stunned myocardium.^17,18 The incorporation of SRI into DSE has been most effective in measuring the low-dose response, where it has been effective in the detection of viable myocardium.^19–22

However, the use of SRI during peak-dose DSE is difficult because of signal noise, with only 1 published report suggesting incremental benefit.⁵ The use of both tissue Doppler and speckle techniques to track the myocardial wall and to perform an automated measurement of SR_s appears to increase the sensitivity of DSE compared with expert conventional manual reading.⁸ Clearly, although the validation of the technique for identifying anatomic evidence of CAD is important, evidence that the findings are predictive of outcome would help to define the value of the technique. To date, however, no reports of the prognostic value of SRI have been published.

Prognostic Value of DSE
The clinical markers of outcome in patients with CAD are well defined.²³ Resting left ventricular function is well known to be of incremental value in predicting outcome.^24,25 Although the risk of patients undergoing DSE exceeds that of those who are able to exercise, reflecting the association of events with reduced exercise capacity,²⁶ a normal DSE carries a good prognosis, with an annualized event rate for cardiac death or infarction of 1.3% and all-cause mortality of 1.8%,²⁵ similar to the all-cause mortality rate of 1.9% per year in the present study. Earlier studies have shown that abnormal WM analysis at both rest and stress are independently predictive of cardiac and all-cause mortality.^{1,2,24,27–30} Our findings support this previous experience, showing resting WM abnormality and ischemia at peak stress to add value to the usual predictors of outcome.

However, even though conventional DSE is predictive of mortality, there are 2 important problems. First, it remains a subjective tool; its accuracy depends on the skills of the user.³ Second, although the extent and severity of abnormality at peak stress are predictive of outcome,³¹ WM abnormality is known to be relatively poor for distinguishing the true extent of CAD. Indeed, previous work has documented the incremental information obtained by averaging peak systolic velocity in patients with abnormal DSE.³² However, tissue velocity imaging has important limitations in the assessment of regional function, and the present study has shown that SR provides a solution not only to the problem of subjectivity but also to the need to quantify the extent of ischemic and infarcted tissue. Importantly, strain was inferior to peak SR, supporting the findings of diagnostic studies.^5,8,33

Prognostic Value of Global Versus Regional Function
Previous studies have shown that peak WMSI response to DSE is one of the strongest independent predictors of adverse cardiac outcomes, adding incremental prognostic information to both clinical data and resting left ventricular function.³⁴ However, the incremental information provided by SR_s to WMSI suggests that SRI provides an additional window to cardiac function. First, the use of SRI overcomes potential misinterpretations of WM analysis. For example, SR_s may be less load sensitive than standard parameters such as ejection fraction or regional function. Second, SR_s may provide information that WM cannot. In this respect, it is of interest that although the mean peak SR_s for all segments and the use of peak SR_s to categorize segments as normal and abnormal were both predictors of outcome, the segmental value of SR_s was the only independent predictor of mortality. Mean SR_s was a better predictor for patients with abnormal WM at rest.

Study Limitations
The present prognostic study was set up in 1998, when tissue Doppler acquisitions were less sophisticated and informative than they are now. The frame rates for both tissue Doppler and B-mode images were low in some patients, and only 1 cine loop was stored. Nonetheless, only 5 patients were excluded because of bad image quality.

We followed up patients for all-cause mortality in the belief that the definition of cardiac mortality is unreliable.³⁴ Although it is possible that the predictors of cardiac mortality are different, the main cause of death in this elderly group can be expected to be cardiac, and we would expect the association of cardiac function with cardiac events to be even stronger.

Coronary angiography or myocardial perfusion scintigraphy was not performed in most patients. An alternative design strategy would have been to study patients with defined coronary anatomy, thus linking the predictive capacity of WMS or SR_s to anatomic evidence of CAD. However, this approach would not provide information relevant to normal patterns of patient selection.

Unfortunately, changes in medical therapy in the course of follow-up were not tracked in the present study. Although this lack of information is unfortunate, it is unlikely to have influenced the relative prognostic importance of deformation imaging and WM assessment.

Finally, the current measurement of SR_s is time consuming. Nonetheless, the adoption of an automated quantitative technique may overcome these limitations.

Conclusions
In patients with known or suspected CAD, mortality can be predicted by the SRI response to DSE. Segmental analysis by SR_s at peak appears to be the optimal approach that provides incremental value to WM analysis.

	Acknowledgments

 
Sources of Funding

The present study was supported by grants from the Norwegian University of Science and Technology, the Norwegian Research Foundation, and the National Health and Medical Research Council (project grant 210218), Canberra, Australia.

Disclosures

Professor Marwick received research grant funding from General Electric Medical Systems that is unrelated to this work. The other authors report no conflicts.

	References

Top Abstract Introduction Methods Results Discussion References

 

1. Marwick T, Case C, Sawada S, Rimmerman C, Brenneman P, Kovacs R, Short L, Lauer M. Prediction of mortality using dobutamine echocardiography. J Am Coll Cardiol. 2001; 37: 754–760.[Abstract/Free Full Text]
   
2. Sicari R, Pasanisi E, Venneri L, Landi P, Cortigiani L, Picano E. Stress echo results predict mortality: a large-scale multicenter prospective international study. J Am Coll Cardiol. 2003; 41: 589–595.[Abstract/Free Full Text]
   
3. Picano E, Lattanzi F, Orlandini A, Marini C, L’Abbate A. Stress echocardiography and the human factor: the importance of being expert. J Am Coll Cardiol. 1991; 17: 666–669.[Abstract]
   
4. Madler CF, Payne N, Wilkenshoff U, Cohen A, Derumeaux GA, Pierard LA, Engvall J, Brodin LA, Sutherland GR, Fraser AG. Non-invasive diagnosis of coronary artery disease by quantitative stress echocardiography: optimal diagnostic models using off-line tissue Doppler in the MYDISE study. Eur Heart J. 2003; 24: 1584–1594.[Abstract/Free Full Text]
   
5. Voigt JU, Exner B, Schmiedehausen K, Huchzermeyer C, Reulbach U, Nixdorff U, Platsch G, Kuwert T, Daniel WG, Flachskampf FA. Strain-rate imaging during dobutamine stress echocardiography provides objective evidence of inducible ischemia. Circulation. 2003; 107: 2120–2126.[Abstract/Free Full Text]
   
6. Kowalski M, Herregods M, Herbots L, Weidemann F, Simmons L, Strotmann J, Dommke C, D’hooge J, Claus P, Bijnens B, Hatle L, Sutherland GR. The feasibility of ultrasonic regional strain and strain rate imaging in quantifying dobutamine stress echocardiography. Eur J Echocardiogr. 2003; 4: 81–91.[CrossRef][Medline] [Order article via Infotrieve]
   
7. Voigt J, Nixdorff U, Bogdan R, Exner B, Schmiedehausen K, Platsch G, Kuwert T, Daniel WG, Flachskampf FA. Comparison of deformation imaging and velocity imaging for detecting regional inducible ischaemia during dobutamine stress echocardiography. Eur Heart J. 2004; 25: 1517–1525.[Abstract/Free Full Text]
   
8. Ingul C, Støylen A, Slordahl S, Wisety R, Burgess M, Marwick TH. Application of strain rate imaging with automated analysis to dobutamine stress echocardiography. J Am Coll Cardiol. In press.
   
9. Bjork Ingul C, Torp H, Aase S, Berg S, Stoylen A, Slordahl S. Automated analysis of strain rate and strain; feasibility and clinical implications. J Am Soc Echocardiogr. 2005; 18: 410–417.
   
10. Aase S, Stoylen A, Bjork Ingul C, Frigstad S, Torp H. Automatic timing of aortic valve closure in apical tissue Doppler images. Ultrasound Med Biol. 2006; 32: 19–27.[CrossRef][Medline] [Order article via Infotrieve]
    
11. Heimdal A, Abraham T, Pislaru C, Belohlavek M. Angle dependency of strain rate imaging in an animal model. J Am Soc Echocardiogr. 2000; 13: 484. Abstract.
    
12. Rosner B. ROC curves. In: Vezilich M, Purrington L, Campobasso L, eds. Fundamentals of Biostatistics. Duxbury, Mass: Harvard University; 2005: 63–65.
    
13. Cox D. Regression models and life-tables. J R Stat Soc Series B. 1972; 34: 187–220.
    
14. D’hooge J, Heimdal A, Jamal F, Kukulski T, Bijnens B, Rademakers F, Hatle L, Suetens P, Sutherland GR. Regional strain and strain rate measurements by cardiac ultrasound: principles, implementation and limitations. Eur J Echocardiogr. 2000; 1: 154–170.[Medline] [Order article via Infotrieve]
    
15. Urheim S, Edvardsen T, Torp H, Angelsen B, Smiseth OA. Myocardial strain by Doppler echocardiography: validation of a new method to quantify regional myocardial function. Circulation. 2000; 102: 1158–1164.[Abstract/Free Full Text]
    
16. Edvardsen T, Gerber BL, Garot J, Bluemke DA, Lima JA, Smiseth OA. Quantitative assessment of intrinsic regional myocardial deformation by Doppler strain rate echocardiography in humans: validation against three-dimensional tagged magnetic resonance imaging. Circulation. 2002; 106: 50–56.[Abstract/Free Full Text]
    
17. Jamal F, Kukulski T, Strotmann J, Szilard M, D’hooge J, Bijnens B, Rademakers F, Hatle L, De Scheerder I, Sutherland GR. Quantification of the spectrum of changes in regional myocardial function during acute ischemia in closed chest pigs: an ultrasonic strain rate and strain study. J Am Soc Echocardiogr. 2001; 14: 874–884.[CrossRef][Medline] [Order article via Infotrieve]
    
18. Stoylen A, Heimdal A, Bjornstad K, Wiseth R, Vik-Mo H, Torp H, Angelsen B, Skjaerpe T. Strain rate imaging by ultrasonography in the diagnosis of coronary artery disease. J Am Soc Echocardiogr. 2000; 13: 1053–1064.[CrossRef][Medline] [Order article via Infotrieve]
    
19. Hoffmann R, Altiok E, Nowak B, Heussen N, Kuhl H, Kaiser HJ, Bull U, Hanrath P. Strain rate measurement by Doppler echocardiography allows improved assessment of myocardial viability inpatients with depressed left ventricular function. J Am Coll Cardiol. 2002; 39: 443–449.[Abstract/Free Full Text]
    
20. Lyseggen E, Skulstad H, Helle-Valle T, Vartdal T, Urheim S, Rabben SI, Opdahl A, Ihlen H, Smiseth OA. Myocardial strain analysis in acute coronary occlusion: a tool to assess myocardial viability and reperfusion. Circulation. 2005; 112: 3901–3910.[Abstract/Free Full Text]
    
21. Hanekom L, Jenkins C, Jeffries L, Case C, Mundy J, Hawley C, Marwick TH. Incremental value of strain rate analysis as an adjunct to wall-motion scoring for assessment of myocardial viability by dobutamine echocardiography: a follow-up study after revascularization. Circulation. 2005; 112: 3892–3900.[Abstract/Free Full Text]
    
22. Ingul CB, Stoylen A, Slordahl SA. Recovery of stunned myocardium in acute myocardial infarction quantified by strain rate imaging: a clinical study. J Am Soc Echocardiogr. 2005; 18: 401–410.[CrossRef][Medline] [Order article via Infotrieve]
    
23. Chaowalit N, Arruda AL, McCully RB, Bailey KR, Pellikka PA. Dobutamine stress echocardiography in patients with diabetes mellitus: enhanced prognostic prediction using a simple risk score. J Am Coll Cardiol. 2006; 47: 1029–1036.[Abstract/Free Full Text]
    
24. Poldermans D, Fioretti PM, Boersma E, Cornel JH, Borst F, Vermeulen EG, Arnese M, el-Hendy A, Roelandt JR. Dobutamine-atropine stress echocardiography and clinical data for predicting late cardiac events in patients with suspected coronary artery disease. Am J Med. 1994; 97: 119–125.[CrossRef][Medline] [Order article via Infotrieve]
    
25. Poldermans D, Fioretti PM, Boersma E, Bax JJ, Thomson IR, Roelandt JR, Simoons ML. Long-term prognostic value of dobutamine-atropine stress echocardiography in 1737 patients with known or suspected coronary artery disease: a single-center experience. Circulation. 1999; 99: 757–762.[Abstract/Free Full Text]
    
26. Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med. 2002; 346: 793–801.[Abstract/Free Full Text]
    
27. Biagini E, Elhendy A, Bax JJ, Rizzello V, Schinkel AF, van Domburg RT, Kertai MD, Krenning BJ, Bountioukos M, Rapezzi C. Seven-year follow-up after dobutamine stress echocardiography: impact of gender on prognosis. J Am Coll Cardiol. 2005; 45: 93–97.[Abstract/Free Full Text]
    
28. Elhendy A, Schinkel AF, Bax JJ, Van Domburg RT, Poldermans D. Prognostic value of dobutamine stress echocardiography in patients with normal left ventricular systolic function. J Am Soc Echocardiogr. 2004; 17: 739–743.[CrossRef][Medline] [Order article via Infotrieve]
    
29. Tsutsui JM, Elhendy A, Anderson JR, Xie F, McGrain AC, Porter TR. Prognostic value of dobutamine stress myocardial contrast perfusion echocardiography. Circulation. 2005; 112: 1444–1450.[Abstract/Free Full Text]
    
30. Krivokapich J, Child JS, Walter DO, Garfinkel A. Prognostic value of dobutamine stress echocardiography in predicting cardiac events in patients with known or suspected coronary artery disease. J Am Coll Cardiol. 1999; 33: 708–716.[Abstract/Free Full Text]
    
31. Hoffman R, Lethen H, Kuhl H, Lepper W, Hanrath P. Extent and severity of test positivity during dobutamine stress echocardiography. Eur Heart J. 1999; 20: 1485–1492.[Abstract/Free Full Text]
    
32. Marwick TH, Case C, Leano R, Short L, Baglin T, Cain P, Garrahy P. Use of tissue Doppler imaging to facilitate the prediction of events in patients with abnormal left ventricular function by dobutamine echocardiography. Am J Cardiol. 2004; 93: 142–146.[CrossRef][Medline] [Order article via Infotrieve]
    
33. Hanekom L, Moir S, Jeffries L, MacNab D, Marwick T. Selection of optimal strain rate imaging parameter for the diagnosis of coronary artery disease during dobutamine stress echocardiography: an angiographic comparison. Eur Heart J. 2005; 26: 210. Abstract.[Free Full Text]
    
34. Lauer MS, Blackstone EH, Young JB, Topol EJ. Cause of death in clinical research: time for a reassessment? J Am Coll Cardiol. 1999; 34: 618–620.[Free Full Text]
    

This article has been cited by other articles:

				T. H. Marwick Straining to detect ischaemia Eur. Heart J., June 6, 2007; (2007) ehm166v1. [Full Text] [PDF]

This Article Abstract Full Text (PDF) All Versions of this Article: 115/10/1252    most recent CIRCULATIONAHA.106.640334v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ingul, C. B. Articles by Marwick, T. H. Search for Related Content PubMed PubMed Citation Articles by Ingul, C. B. Articles by Marwick, T. H. Pubmed/NCBI databases Compound via MeSH Substance via MeSH Hazardous Substances DB ATROPINE Related Collections Chronic ischemic heart disease Echocardiography