Difference vectors to describe dynamics of the ST segment and the ventricular gradient in acute ischemia

Vectorcardiography Predicts Heart Failure in Patients Following ST Elevation Myocardial Infarction

BACKGROUND

Modeling outcomes, such as onset of heart failure (HF) or mortality, in patients following ST elevation myocardial infarction (STEMI) is challenging but clinically very useful. The acute insult following a myocardial infarction and chronic degeneration seen in HF involve a similar process where a loss of cardiomyocytes and abnormal remodeling lead to pump failure. This process may alter the strength and direction of the heart's net depolarization signal. We hypothesize that changes over time in unique parameters extracted using vectorcardiography (VCG) have the potential to predict outcomes in patients post-STEMI and could eventually be used as a noninvasive and cost-effective surveillance tool for characterizing the severity and progression of HF to guide evidence-based therapies.

METHODS

We identified 162 patients discharged from Michigan Medicine between 2016 and 2021 with a diagnosis of acute STEMI. For each patient, a single 12-lead ECG > 1 week pre-STEMI and > 1 week post-STEMI were collected. A set of unique VCG parameters were derived by analyzing features of the QRS complex. We used LASSO regression analysis incorporating clinical variables and VCG parameters to create a predictive model for HF, mortality, or the composite at 90, 180, and 365 days post-STEMI.

RESULTS

The VCG model is most predictive for HF onset at 90 days with a robust AUC. Variables from the HF model mitigating or driving risk, at a p < 0.05, were primarily parameters that assess the area swept by the depolarization vector including the 3D integral and convex hull in select spatial octants and quadrants.

Do electrocardiographic changes induced during intracoronary vasospasm provocation testing reflect those during spontaneous angina episodes in patients with vasospastic angina?: a case series

Background

According to the Coronary Vasomotor Disorders International Study (COVADIS) group, the ECG criteria supporting the diagnosis of vasospastic angina (VSA) in spontaneous episodes or induced during intracoronary spasm testing are similar. However, it remains elusive whether acetylcholine-induced ECG changes during epicardial spasms reflect ECG changes that occur during the height of a spontaneous episode.

Case summary

We present four patients diagnosed with VSA during intracoronary spasm testing, of whom the ECG characteristics during spasm testing and a spontaneous angina episode are described. All patients have >90% coronary epicardial vasoconstriction in one or more vessels during acetylcholine provocation. ECGs at the height of a spontaneous episode and during acetylcholine-induced coronary spasm are found to be different in three out of four patients.

Discussion

In patients with VSA, the ECG at the height of a spontaneous episode and during acetylcholine-induced coronary artery spasm may differ substantially. In patients with symptoms suspicious of VSA, every effort should be undertaken to obtain ECGs during the height of a spontaneous episode of angina pectoris and there should be a low threshold to perform intracoronary function testing.

Comparison of the Diagnostic Yield of Intracoronary Acetylcholine Infusion and Acetylcholine Bolus Injection Protocols During Invasive Coronary Function Testing

Coronary endothelial dysfunction (CED) and coronary artery spasm (CAS) are causes of angina with no obstructive coronary arteries in patients. Both can be diagnosed by invasive coronary function testing (ICFT) using acetylcholine (ACh). This study aimed to evaluate the diagnostic yield of a 3-minute ACh infusion as compared with a 1-minute ACh bolus injection protocol in testing CED and CAS. We evaluated 220 consecutive patients with angina and no obstructive coronary arteries who underwent ICFT using continuous Doppler flow measurements. Per protocol, 110 patients were tested using 3-minute infusion, and thereafter 110 patients using 1-minute bolus injections, because of a protocol change. CED was defined as a <50% increase in coronary blood flow or any epicardial vasoconstriction in reaction to low-dose ACh and CAS according to the Coronary Vasomotor Disorders International Study Group (COVADIS) criteria, both with and without T-wave abnormalities, in reaction to high dose ACh. The prevalence of CED was equal in both protocols (78% vs 79%, p = 0.869). Regarding the endotypes of CAS according to COVADIS, the equivocal endotype was diagnosed less often in the 3 vs 1-minute protocol (24% vs 44%, p = 0.004). Including T-wave abnormalities in the COVADIS criteria resulted in a similar diagnostic yield of both protocols. Hemodynamic changes from baseline to the low or high ACh doses were comparable between the protocols for each endotype. In conclusion, ICFT using 3-minute infusion or 1-minute bolus injections of ACh showed a similar diagnostic yield of CED. When using the COVADIS criteria, a difference in the equivocal diagnosis was observed. Including T-wave abnormalities as a diagnostic criterion reclassified equivocal test results into CAS and decreased this difference. For clinical practice, we recommend the inclusion of T-wave abnormalities as a diagnostic criterion for CAS and the 1-minute bolus protocol for practicality.

Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients

Non-traumatic chest pain is a frequent reason for an urgent ambulance visit of a patient by the emergency medical services (EMS). Chest pain (or chest pain-equivalent symptoms) can be innocent, but it can also signal an acute form of severe pathology that may require prompt intervention. One of these pathologies is cardiac ischemia, resulting from a disbalance between blood supply and demand. One cause of a diminished blood supply to the heart is acute coronary syndrome (ACS, i.e., cardiac ischemia caused by a reduced blood supply to myocardial tissue due to plaque instability and thrombus formation in a coronary artery). ACS is dangerous due to the unpredictable process that drives the supply problem and the high chance of fast hemodynamic deterioration (i.e., cardiogenic shock, ventricular fibrillation). This is why an ECG is made at first medical contact in most chest pain patients to include or exclude ischemia as the cause of their complaints. For speedy and adequate triaging and treatment, immediate assessment of this prehospital ECG is necessary, still during the ambulance ride. Human diagnostic efforts supported by automated interpretation algorithms seek to answer questions regarding the urgency level, the decision if and towards which healthcare facility the patient should be transported, and the indicated acute treatment and further diagnostics after arrival in the healthcare facility. In the case of an ACS, a catheter intervention room may be activated during the ambulance ride to facilitate the earliest possible in-hospital treatment. Prehospital ECG assessment and the subsequent triaging decisions are complex because chest pain is not uniquely associated with ACS. The differential diagnosis includes other cardiac, pulmonary, vascular, gastrointestinal, orthopedic, and psychological conditions. Some of these conditions may also involve ECG abnormalities. In practice, only a limited fraction (order of magnitude 10%) of the patients who are urgently transported to the hospital because of chest pain are ACS patients. Given the relatively low prevalence of ACS in this patient mix, the specificity of the diagnostic ECG algorithms should be relatively high to prevent overtreatment and overflow of intervention facilities. On the other hand, only a sufficiently high sensitivity warrants adequate therapy when needed. Here, we review how the prehospital ECG can contribute to identifying the presence of myocardial ischemia in chest pain patients. We discuss the various mechanisms of myocardial ischemia and infarction, the typical patient mix of chest pain patients, the shortcomings of the ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) ECG criteria to detect a completely occluded culprit artery, the OMI ECG criteria (including the STEMI-equivalent ECG patterns) in detecting completely occluded culprit arteries, and the promise of neural networks in recognizing ECG patterns that represent complete occlusions. We also discuss the relevance of detecting any ACS/ischemia, not necessarily caused by a total occlusion, in the prehospital ECG. In addition, we discuss how serial prehospital ECGs can contribute to ischemia diagnosis. Finally, we discuss the diagnostic contribution of a serial comparison of the prehospital ECG with a previously made nonischemic ECG of the patient.

Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs

Objectives. Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible. Serial electrocardiography, a comparison of the acute electrocardiogram with a previously recorded (reference) ECG of the same patient, aids in identifying ischemia-induced electrocardiographic changes by correcting for interindividual ECG variability. Recently, the combination of deep learning and serial electrocardiography provided promising results in detecting emerging cardiac diseases; thus, the aim of our current study is the application of our novel Advanced Repeated Structuring and Learning Procedure (AdvRS&LP), specifically designed for acute myocardial ischemia detection in the pre-hospital phase by using serial ECG features.Approach. Data belong to the SUBTRACT study, which includes 1425 ECG pairs, 194 (14%) ACS patients, and 1035 (73%) controls. Each ECG pair was characterized by 28 serial features that, with sex and age, constituted the inputs of the AdvRS&LP, an automatic constructive procedure for creating supervised neural networks (NN). We created 100 NNs to compensate for statistical fluctuations due to random data divisions of a limited dataset. We compared the performance of the obtained NNs to a logistic regression (LR) procedure and the Glasgow program (Uni-G) in terms of area-under-the-curve (AUC) of the receiver-operating-characteristic curve, sensitivity (SE), and specificity (SP).Main Results. NNs (median AUC = 83%, median SE = 77%, and median SP = 89%) presented a statistically (Pvalue lower than 0.05) higher testing performance than those presented by LR (median AUC = 80%, median SE = 67%, and median SP = 81%) and by the Uni-G algorithm (median SE = 72% and median SP = 82%).Significance. In conclusion, the positive results underscore the value of serial ECG comparison in ischemia detection, and NNs created by AdvRS&LP seem to be reliable tools in terms of generalization and clinical applicability.

Coronary Artery Occlusion Detection Using 3-Lead ECG System Suitable for Credit Card-Size Personal Device Integration

Background

Early coronary occlusion detection by portable personal device with limited number of electrocardiographic (ECG) leads might shorten symptom-to-balloon time in acute coronary syndromes.

Objectives

The purpose of this study was to compare the accuracy of coronary occlusion detection using vectorcardgiographic analysis of a near-orthogonal 3-lead ECG configuration suitable for credit card-size personal device integration with automated and human 12 lead ECG interpretation.

Methods

The 12-lead ECGs with 3 additional leads ("abc") using 2 arm and 2 left parasternal electrodes were recorded in 66 patients undergoing percutaneous coronary intervention prior to ("baseline", n = 66), immediately before ("preinflation", n = 66), and after 90-second balloon coronary occlusion ("inflation", n = 120). Performance of computer-measured ST-segment shift on vectorcardgiographic loops constructed from "abc" and 12 leads, standard 12-lead ECG, and consensus human interpretation in coronary occlusion detection were compared in "comparative" and "spot" modes (with/without reference to "baseline") using areas under ROC curves (AUC), reliability, and sensitivity/specificity analysis.

Results

Comparative "abc"-derived ST-segment shift was similar to two 12-lead methods (vector/traditional) in detecting balloon coronary occlusion (AUC = 0.95, 0.96, and 0.97, respectively, P = NS). Spot "abc" and 12-lead measurements (AUC = 0.72, 0.77, 0.68, respectively, P = NS) demonstrated poorer performance (P < 0.01 vs comparative measurements). Reliability analysis demonstrated comparative automated measurements in "good" agreement with reference (preinflation/inflation), while comparative human interpretation was in "moderate" range. Spot automated and human reading showed "poor" agreement.

Conclusions

Vectorcardiographic ST-segment analysis using baseline comparison of 3-lead ECG system suitable for credit card-size personal device integration is similar to established 12-lead ECG methods in detecting balloon coronary occlusion.

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

PURPOSE

Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD.

AIM

To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD.

METHODS

Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme.

RESULTS

ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8.

CONCLUSIONS

Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S I , THI, and SHI, where S I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

An initial exploration of subtraction electrocardiography to detect myocardial ischemia in the prehospital setting

BACKGROUND

In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG-patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography.

METHODS

SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in-hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni-G ECG Analysis Program was used for state-of-the-art single-ECG interpretation of the ambulance ECG.

RESULTS

In 1,229 patients, the mean area-under-the-curve of subtraction electrocardiography was 0.80 (95%CI: 0.77-0.82). The performance of our new method was comparable to single-ECG analysis using the Uni-G algorithm: sensitivities were 66% versus 67% (p-value > .05), respectively; specificities were 80% versus 81% (p-value > .05), respectively.

CONCLUSIONS

In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state-of-the-art automated single-ECG analysis by the Uni-G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection.

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach

BACKGROUND

Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs.

METHODS

We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization.

RESULTS

Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively).

CONCLUSIONS

Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change.

Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology

This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

Performance of ST and ventricular gradient difference vectors in electrocardiographic detection of acute myocardial ischemia

INTRODUCTION

Serial analysis could improve ECG diagnosis of myocardial ischemia caused by acute coronary occlusion.

METHODS

We analyzed ECG pairs of 84 cases and 398 controls. In case-patients, who underwent elective percutaneous coronary intervention, ischemic ECGs during balloon occlusion were compared with preceding non-ischemic ECGs. In control-patients, two elective non-ischemic ECGs were compared. In each ECG the ST vector at the J point and the ventricular gradient (VG) vector was computed, after which difference vectors ΔST and ΔVG were computed within patients. Finally, receiver operating characteristic analysis was done.

RESULTS

Areas under the curve were 0.906 (P<0.001; CI 0.862-0.949; SE 0.022) for ΔST and 0.880 (P<0.001; CI 0.833-0.926; SE 0.024) for ΔVG. Sensitivity and specificity of conventional ST-elevation myocardial infarction (STEMI) criteria were 70.2% and 89.1%, respectively. At matched serial analysis specificity and STEMI specificity, serial analysis sensitivity was 78.6% for ΔST and 71.4% for ΔVG (not significantly different from STEMI sensitivity). At matched serial analysis sensitivity and STEMI sensitivity, serial analysis specificity was 96.5% for ΔST and 89.3% for ΔVG; ΔST and STEMI specificities differed significantly (P<0.001).

CONCLUSION

Detection of acute myocardial ischemia by serial ECG analysis of ST and VG vectors has equal or even superior performance than the STEMI criteria. This concept should be further evaluated in triage ECGs of patients suspected from having acute myocardial ischemia.

Vectorcardiographic diagnostic & prognostic information derived from the 12-lead electrocardiogram: Historical review and clinical perspective

In the course of time, electrocardiography has assumed several modalities with varying electrode numbers, electrode positions and lead systems. 12-lead electrocardiography and 3-lead vectorcardiography have become particularly popular. These modalities developed in parallel through the mid-twentieth century. In the same time interval, the physical concepts underlying electrocardiography were defined and worked out. In particular, the vector concept (heart vector, lead vector, volume conductor) appeared to be essential to understanding the manifestations of electrical heart activity, both in the 12-lead electrocardiogram (ECG) and in the 3-lead vectorcardiogram (VCG). Not universally appreciated in the clinic, the vectorcardiogram, and with it the vector concept, went out of use. A revival of vectorcardiography started in the 90's, when VCGs were mathematically synthesized from standard 12-lead ECGs. This facilitated combined electrocardiography and vectorcardiography without the need for a special recording system. This paper gives an overview of these historical developments, elaborates on the vector concept and seeks to define where VCG analysis/interpretation can add diagnostic/prognostic value to conventional 12-lead ECG analysis.

Role of the ECG in initial acute coronary syndrome triage: primary PCI regardless presence of ST elevation or of non-ST elevation

The major initial triaging decision in acute coronary syndrome (ACS) is whether or not percutaneous coronary intervention (PCI) is the primary treatment. Current guidelines recommend primary PCI in ST-elevation ACS (STEACS) and initial antithrombotic therapy in non-ST-elevation ACS (NSTEACS). This review probes the question whether this decision can indeed be based on the ECG. Genesis of STE/NSTE ECGs depends on the coronary anatomy, collateral circulation and site of the culprit lesion. Other causes than ischaemia may also result in ST-segment changes. It has been demonstrated that the area at risk cannot reliably be estimated by the magnitude of the ST change, that complete as well as incomplete occlusions can cause STE as well as NSTE ECGs, and that STE and NSTE patterns cannot differentiate between transmural and non-transmural ischaemia. Furthermore, unstable angina can occur with STE and NSTE ECGs. We conclude that the ECG can be used to assist in detecting ischaemia, but that electrocardiographic STE and NSTE patterns are not uniquely related to distinctly different pathophysiological mechanisms. Hence, in ACS, primary PCI might be considered regardless of the nature of the ST deviation, and it should be done with the shortest possible delay, because ‘time is muscle’.

Directionality and proportionality of the ST and ventricular gradient difference vectors during acute ischemia

BACKGROUND

The ECG is important in diagnosis and triage in the initial phase of the acute coronary syndrome (ACS). The primary goal of making an ECG at first medical contact should be the reliable detection of cardiac ischemia, thus facilitating a correct triage by corroborating the diagnosis of ACS. Ischemia detection by ST amplitude analysis is limited to situations in which there is an identifiable J point. The ventricular gradient (VG) is independent of conduction and might be an alternative ECG-based variable for ischemia detection.

METHODS

We studied vectorcardiograms (VCGs) synthesized of the ECGs of 67 patients who underwent elective PTCA with prolonged balloon occlusions (mean±SD occlusion duration 214±77s), and computed, during occlusions, the changes of the ST and VG vectors with respect to baseline, ΔST and ΔVG, and the angle between these vectors, ∠(ΔST, ΔVG). We then analyzed directionality and proportionality of ΔST and ΔVG by performing linear regressions of ∠(ΔST, ΔVG) on time after occlusion, and of ΔVG on ΔST, respectively.

RESULTS

Linear regression of ∠(ΔST, ΔVG) on time after occlusion yielded a slope of 1.55*10(-3) °/s and an intercept of 11.96°; r(2)<0.001 (NS). Linear regression of ΔVG on ΔST on all data yielded a slope of 253mV and an intercept of 14.4mV•ms; r(2)=0.75 (P<0.001). Broken stick linear regression (breakpoint ΔST=0.255mV) yielded slopes of 330mV and 160mV, intercepts of 5.6mV•ms and 47.2mV•ms, and r(2) values of 0.66 (P<0.001) and 0.63 (P<0.001) for the smaller and larger ΔST values, respectively.

CONCLUSION

Our study suggests that, because of the directionality and proportionality between ΔST and ΔVG, the change in the ventricular gradient, ΔVG, between a reference ECG and an ischemic ECG is a meaningful measure of ischemia.