Combination of single quantitative parameters into multiparametric model for ischemia detection is not superior to visual assessment during dobutamine stress echocardiography

Several studies have proposed a number of quantitative parameters vs. visual assessment for ischemia detection during dobutamine stress echocardiography (DSE) [1‐7]. However, most of these quantitative tools have remained in the research laboratory and are not implemented in the routine clinical practice. In a previous report we tried to identify a single powerful quantitative parameter for the prediction of coronary stenosis studying multiple velocity and deformation parameters during DSE but we could not demonstrate that such an approach was better than expert visual wall motion reading [8]. Several previous reports were consistent with our findings showing that visual assessment was equally accurate as quantitative assessment. However, the main limitation of stress echocardiography is related to operator’s experience and a more objective and quantitative approach is needed. The purpose of this study was to evaluate if the combination of several quantitative parameters into a mathematical model would enhance the detection of myocardial ischemia during DSE. The study hypothesis is that a multiparametric approach would provide a sound and effective diagnostic tool.

One hundred fifty-one prospectively enrolled consecutive patients in the test group underwent DSE between January 2008 and December 2010 and 105 patients in the validation group underwent DSE between January 2011 and December 2012. Decision for DSE indication was made by consulting cardiologists not involved in the research project, in the course of routine diagnostic workup. DSE was performed for recurrent symptoms in patients with known coronary artery disease (CAD) (n = 35 in the test group and n = 40 in the validation group) or suspected CAD (n = 116 and n = 65, respectively). Patients were included in the study if coronary angiography was performed within 6-8 weeks after DSE. Exclusion criteria were: previous myocardial infarction, previous cardiac surgery, non-sinus rhythm, significant valvular disease, left ventricular hypertrophy [9‐11], atrial or ventricular arrhythmias, bundle branch block or reduced left ventricular (LV) ejection fraction (EF) <50 %. Beta-blocking medications were discontinued 48 hours, nitrates and other antianginal medications – 24 hours prior to the DSE in all patients. Stress echocardiography was performed on medical therapy in 94 (62 %) patients in the test group and 82 (78 %) in the validation group (calcium-antagonists in 56 and 41, or nitrates in 38 and 21, respectively) and off therapy in 57 (38 %) and 23 (22 %) patients. Informed consent was obtained from all patients before testing, and the study protocol was approved by the Vilnius regional Bioethics committee (Approval No.158200-11-254-58). Stress echo data were collected and analysed by stress echocardiographers not involved in patient care. Hypertension and hypercholesterolemia were defined according to standard definitions [9, 11].

This study represents further consecutive step in attempts to implement quantitative tools in the detection of myocardial ischemia during stress echocardiography. It is based on the number of previous investigations showing the significant links of several myocardial motion and deformation markers with induced ischemia [1‐8, 12‐15].

However, in the vast majority of publications the diagnostic accuracy of the quantitative markers is demonstrated to be lower or only comparable to the visual assessment of stress echocardiography [1, 2, 8, 12‐14]. Current lack of evidence on effective application of quantitative methods in routine practice is reflected in recommendation documents and consensus statement of EAE and ASE [3, 15, 16].

Prior research [1, 4‐8] was mostly focused on single parameters, segment-specific or averaged for all myocardial segments that carry only fragmental information of regionally impaired myocardial mechanics. Therefore, we hypothesized that a multiparametric model, including a substantial list of informative quantitative parameters, would demonstrate better performance than separate markers alone or visual DSE assessment.

Theoretically, such mathematical model could better reflect the complicated nature of the biological phenomenon of regional ischemia and incorporate relevant interdependencies between physiologically different parameters. The feasibility was considered as one of the main requirements to the quantitative tool for routine clinical practice, therefore automatically obtainable data of speckle tracking were chosen for model creation.

In parallel with previous studies the set of predictive markers included blunted response of systolic velocity, prolonged time to peak systolic velocity [1, 12, 14], decreased E’ wave velocity [4, 5, 17], longitudinal and radial systolic, post-systolic and maximal strain, post-systolic index [6, 7, 13], systolic and diastolic longitudinal and radial strain rate, radial systolic displacement. In this study we followed the methodology of defining thresholds separately for each evaluated myocardial segment, taking into account known base-to-apex and wall-to-wall differences of myocardial velocity and strain/strain rate [10, 18, 19]. Model user should only approve peak velocity, strain, strain rate and displacement values in the commercially available 2D strain analysis software and then export data set through Excel tables to the constructed system. Originally created classifier was incorporated in daily used Access database of DSE. Visual assessment data were eligible for automatic import, and changes in scores of selected segments entered the model, too. Of note, the lower sensitivity of visual assessment in the present and some previous reports [14] reflects the limitations of subjective interpretation of regional wall motion and justifies the search of quantitative tools.

The constructed mathematical analysis tool represents a kind of machine learning methodology, namely a type of neural network with unusual fitting method. Machine learning technology is currently well suited for analysing medical data, and in particular there is a lot of work done in medical diagnosis in small specialized diagnostic problems [20‐22]. This system provides a possibility to handle an unusually large amount of data in a relatively short period of time. The created classifier automatically made the prognosis of significant coronary stenosis, and in the test group it demonstrated promising results. However, the attempt to validate the model in the similar population of consecutive patients gave disappointing results.

Failure of model validation recalls the shortcomings of single quantitative markers, having rather modest predictive ability of significant coronary stenosis (AUCs 0.60-0.72) [8, 14]. Limited value of distinguished indices could be largely attributed to known technical challenges of quantitative imaging: potentially inadequate spatial and temporal resolution, higher speckle decorrelation between subsequent frames at higher heart rates, noise and artefacts [23]. Similar to our findings, considerable inter- and intra-observer variability of 7-12 % is reported for speckle tracking technology [8, 24‐26]. Possibly, mutual interaction of ischemic and non-ischemic segments and load-dependency of deformation parameters may diminish the differences between markers of these two groups [26, 27]. Furthermore, previously demonstrated significant heterogeneity of left ventricular wall thickening during dobutamine stress even in the absence of CAD may contribute to insufficient accuracy of created model [28].

Myocardial deformation imaging provides potential for creation of automated predictive model for stress-induced ischemia detection. However, a multiparametric mathematical model based on quantitative deformation markers did not demonstrate incremental value to visual assessment of wall motion.