Electrocardiographic predictors of coronary microvascular dysfunction in patients with non-obstructive coronary artery disease: Utility of a novel T wave analysis program

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features

Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

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.

Machine learning aids clinical decision-making in patients presenting with angina and non-obstructive coronary artery disease

Aims

The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD).

Methods and results

This study included 1893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior. Endothelial-independent CMD was defined by coronary flow reserve (CFR) ≤2.5 in response to intracoronary adenosine. Endothelial-dependent CMD was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, %ΔCBF ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular clinical data. Mean age was 51 ± 12 years and 66% were females (n = 1257). Area under the curve values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 with clinical variables. Area under the curve and accuracy were 0.67% and 60%. When decreasing the threshold of positivity, sensitivity and negative predictive value increased to 92% and 90%, respectively, while specificity and positive predictive value decreased to 25% and 29%, respectively.

Conclusion

An artificial intelligence-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.

Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement

Aims

The coronavirus disease 2019 (COVID‐19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID‐19 patients and to identify ECG parameters that are associated with cardiac involvement.

Methods and results

The study included patients who were hospitalized with COVID‐19 diagnosis and had cardiac biomarker assessments and simultaneous 12‐lead surface ECGs. Sixty‐three hospitalized patients (median 53 [inter‐quartile range, 43–65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre‐existing co‐morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38–4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03–1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age.

Conclusions

In COVID‐19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification.

Ventricular repolarization alterations in women with angina pectoris and suspected coronary microvascular dysfunction

OBJECTIVES

CMD could be the explanation of angina pectoris with no obstructive CAD and may cause ventricular repolarization changes. We compared T-wave morphology and QTc interval in women with angina pectoris with a control group as well as the associations with CMD.

METHODS

Women with angina pectoris and no obstructive coronary artery disease (n=138) and age-matched controls were compared in regard to QTc interval and morphology combination score (MCS) based on T-wave asymmetry, flatness and presence of T-wave notch. CMD was assessed as a coronary flow velocity reserve (CFVR) by transthoracic echocardiography.

RESULTS

Women with angina pectoris had significantly longer QTc intervals (429±20ms) and increased MCS (IQR) (0.73 [0.64-0.80]) compared with the controls (419±20ms) and (0.63 [(0.53-0.73]), respectively (both p<0.001). CFVR was associated with longer QTc interval (p=0.02), but the association was attenuated after multivariable adjustment (p=0.08).

CONCLUSION

This study suggests that women with angina pectoris have alterations in T-wave morphology as well as longer QTc interval compared with a reference population. CMD might be an explanation.

Automated T-wave analysis can differentiate acquired QT prolongation from congenital long QT syndrome

BACKGROUND

Prolongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T-wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.

METHODS

Patients were identified through an institution-wide computer-based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T-wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10-fold cross-validation to statistically test the selected features.

RESULTS

The 12-lead ECG of 38 patients with congenital LQTS and 114 patients with drug-induced and/or electrolyte-mediated QT prolongation were analyzed. In lead V₅ , patients with acquired QT prolongation had a shallower T wave right slope (-2,322 vs. -3,593 mV/s), greater T-peak-Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).

CONCLUSION

T-wave morphological analysis on lead V₅ of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Coronary Microvascular Dysfunction - Epidemiology, Pathogenesis, Prognosis, Diagnosis, Risk Factors and Therapy

Angina has traditionally been thought to be caused by obstructive coronary artery disease (CAD). However, a substantial number of patients with angina are found to not have obstructive CAD when undergoing coronary angiography. A significant proportion of these patients have coronary microvascular dysfunction (CMD), characterized by heightened sensitivity to vasoconstrictor stimuli and limited microvascular vasodilator capacity. With the advent of non-invasive and invasive techniques, the coronary microvasculature has been more extensively studied in the past 2 decades. CMD has been identified as a cause of cardiac ischemia, in addition to traditional atherosclerotic disease and vasospastic disease. CMD can occur alone or in the presence obstructive CAD. CMD shares many similar risk factors with macrovascular CAD. Diagnosis is achieved through detection of an attenuated response of coronary blood flow in response to vasodilatory agents. Imaging modalities such as cardiovascular magnetic resonance, positron emission tomography, and transthoracic Doppler echocardiography have become more widely used, but have not yet completely replaced the traditional intracoronary vasoreactivity testing. Treatment of CMD starts with lifestyle modification and risk factor control. The use of traditional antianginal, antiatherosclerotic medications and some novel agents may be beneficial; however, clinical trials are needed to assess the efficacy of the pharmacologic and non-pharmacologic therapeutic modalities. In addition, studies with longer-term follow-up are needed to determine the prognostic benefits of these agents. We review the epidemiology, prognosis, pathogenesis, diagnosis, risk factors and current therapies for CMD.