Diagnostic and prognostic values of the V-index, a novel ECG marker quantifying spatial heterogeneity of ventricular repolarization, in patients with symptoms suggestive of non-ST-elevation myocardial infarction

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS

AIM

Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.

METHODS AND RESULTS

This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( P < 0.001 ), higher lipid levels in the coronary group ( P < 0.005 ), significantly longer QRS duration during exercise testing ( P < 0.005 ), more positive leads ( P < 0.001 ), and a greater proportion of significant changes in HFQRS ( P < 0.001 ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.

CONCLUSION

Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.

Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities

Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.

Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome

BACKGROUND

Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion.

METHODS

This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm).

RESULTS

Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance.

CONCLUSIONS

Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects

BACKGROUND AND OBJECTIVES

In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-à-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG.

METHODS

The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time.

RESULTS

Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p < 0.05).

CONCLUSIONS

High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity.

Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

Automatically computed ECG algorithm for the quantification of myocardial scar and the prediction of mortality

BACKGROUND

Myocardial scar is associated with adverse cardiac outcomes. The Selvester QRS-score was developed to estimate myocardial scar from the 12-lead ECG, but its manual calculation is difficult. An automatically computed QRS-score would allow identification of patients with myocardial scar and an increased risk of mortality.

OBJECTIVES

To assess the diagnostic and prognostic value of the automatically computed QRS-score.

METHODS

The diagnostic value of the QRS-score computed automatically from a standard digital 12-lead was prospectively assessed in 2742 patients with suspected myocardial ischemia referred for myocardial perfusion imaging (MPI). The prognostic value of the QRS-score was then prospectively tested in 1151 consecutive patients presenting to the emergency department (ED) with suspected acute heart failure (AHF).

RESULTS

Overall, the QRS-score was significantly higher in patients with more extensive myocardial scar: the median QRS-score was 3 (IQR 2-5), 4 (IQR 2-6), and 7 (IQR 4-10) for patients with 0, 5-20 and > 20% myocardial scar as quantified by MPI (p < 0.001 for all pairwise comparisons). A QRS-score ≥ 9 (n = 284, 10%) predicted a large scar defined as > 20% of the LV with a specificity of 91% (95% CI 90-92%). Regarding clinical outcomes in patients presenting to the ED with symptoms suggestive of AHF, mortality after 1 year was 28% in patients with a QRS-score ≥ 3 as opposed to 20% in patients with a QRS-score < 3 (p = 0.001).

CONCLUSIONS

The QRS-score can be computed automatically from the 12-lead ECG for simple, non-invasive and inexpensive detection and quantification of myocardial scar and for the prediction of mortality. TRIAL-REGISTRATION: http://www.clinicaltrials.gov . Identifier, NCT01838148 and NCT01831115.

Diagnostic value of the cardiac electrical biomarker, a novel ECG marker indicating myocardial injury, in patients with symptoms suggestive of non-ST-elevation myocardial infarction

BACKGROUND

The cardiac electrical biomarker (CEB) is a novel electrocardiographic (ECG) marker quantifying the dipolar activity of the heart with higher levels indicating myocardial injury.

METHODS

We prospectively enrolled 1097 patients presenting with suspected non-ST-elevation myocardial infarction (NSTEMI) to the emergency department (ED). Digital 12-lead ECGs were recorded at presentation and the CEB values were calculated in a blinded fashion. The final diagnosis was adjudicated by two independent cardiologists. The prognostic endpoint was all-cause mortality during 2 years of follow-up.

RESULTS

NSTEMI was the final diagnosis in 14% of patients. CEB levels were higher in patients with NSTEMI compared to other causes of chest pain (median 44 (IQR 21-98) vs. 30 (IQR 16-61), p < .001). A weak but significant correlation between levels of high-sensitivity cardiac troponin T (hs-cTnT) at admission to the ED and the CEB was found (r = .23, p < .001). The use of the CEB in addition to conventional ECG criteria improved the diagnostic accuracy for the diagnosis of NSTEMI as quantified by the area under the receiver operating characteristics curve from 0.66 to 0.71 (p < .001) and the sensitivity improved from 43% to 79% (p < .001).

CONCLUSION

In conclusion, the CEB, an ECG marker of myocardial injury, significantly improves the accuracy and sensitivity of the ECG for the diagnosis of NSTEMI.