Electrocardiogram Criteria Research Articles

Horizontal QRS Axis Predicts Response to Cardiac Resynchronization Therapy in Heart Failure Patients with Left Bundle Branch Block

Electrocardiogram criteria for left bundle branch block (LBBB) inadequately predict left ventricular electrical dyssynchrony, complicating cardiac resynchronization therapy (CRT) candidate selection. To investigate the predictive value of the horizontal QRS axis for CRT response in heart failure (HF) patients with LBBB patterns. The direction and magnitude of the horizontal QRS axis were calculated using the net amplitudes in leads V2 and V6. CRT response was defined as a ≥10% increase in left ventricular ejection fraction (LVEF) and at least one New York Heart Association (NYHA) class reduction one-year post-CRT implantation. The composite endpoint included HF hospitalization or all-cause mortality. Among 244 consecutive CRT recipients, 156 (63.9%) responded favorably, while 88 (36.1%) were non-responders. The horizontal QRS axis demonstrated significant backward deviation [-75.5° (-79.7°, -69.0°) vs. -65.0° (-73.0°, -46.5°), P <0.001] and larger magnitude (35.5±10.9 mm vs. 25.5±10.5 mm, P <0.001) in CRT responders compared to non-responders. The direction and magnitude independently predicted CRT response with an area under the curve (AUC) of 0.778 (95% CI: 0.717, 0.839) and 0.749 (95% CI: 0.685, 0.814), respectively. Combining both parameters increased the AUC to 0.814 (95% CI: 0.760, 0.868). Moreover, the direction and magnitude of the horizontal QRS axis, or their combination, predicted the composite endpoint of HF hospitalization or all-cause mortality, with hazard ratios (HR) of 0.36 (95% CI: 0.22, 0.60), 0.41 (95% CI: 0.25, 0.67), and 0.25 (95% CI: 0.15, 0.41), respectively. Horizontal QRS axis accurately predicts CRT response and prognosis in HF patients with LBBB.

Myocardial single photon emission computed tomography imaging to identify non-responders to cardiac resynchronization therapy in patients with non-left bundle branch block

Abstract Background Non-response to cardiac resynchronization therapy (CRT) is an important issue in the treatment of heart failure with reduced ejection fraction (HFrEF) and non-left bundle branch block (LBBB). Although the QRS duration and morphology are crucial parameters for selecting CRT candidates, electrocardiogram (ECG) markers may inadequately represent the mechanical activation patterns within the heart. Owing to limitations associated with the ECG criteria, there is increasing interest in advanced multimodality imaging to improve patient selection. Recently, myocardial single-photon emission computed tomography imaging (G-MPI) is used for assessing left ventricular (LV) dyssynchrony. This technique is mostly automatic and has high intra- and inter-observer reproducibility and repeatability. Objectives This study aimed to examine whether preoperative G-MPI evaluation could predict non-responsiveness to CRT in patients with HFrEF with non-LBBB. Methods Between January 2021 and December 2022, 128 patients underwent CRT, of whom 73 with pre-operative evaluation using G-MPI were selected. Forty-three patients with non-LBBB (21 and 22 CRT responders and non-responders, respectively) were assessed. These patients were divided into CRT responder and non-responder groups. We evaluated the cut-off and clinical impact of pre-operative G-MPI parameters in predicting non-responsiveness to CRT. In addition, 30 patients with LBBB were analyzed. Results Among patients with non-LBBB, CRT responders and non-responders showed no significant differences in baseline characteristics, except for the LV dimension. A receiver operating characteristic curve analysis identified 108° and 27.7° as the optimal cut-offs for the bandwidth and phase standard deviation (SD), respectively, to predict non-responsiveness to CRT (area under the curve [AUC]=0.762; 95% confidence interval [CI]: 0.601–0.923 and AUC=0.742; 95% CI: 0.576–0.909, respectively). A multivariate analysis revealed that a cut-off bandwidth of ≥108° and phase SD of ≥27.7° are independent predictors of non-responsiveness to CRT in patients with non-LBBB (hazard ratios 5.65; 95% CI: 1.53–20.9; P=0.009). In contrast, there were no relationships between G-MPI parameters and non-responsiveness to CRT in patients with LBBB. Conclusions Pre-operative G-MPI findings were associated with non-responsiveness to CRT in patients with non-LBBB. The identification of potentially nonresponsive patients could influence patient management and alter outcomes.

Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination

Abstract Background Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis. Purpose The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical decision-making. Methods We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC). Results The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2). Conclusions This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.Figure 1Figure 2

N-terminal pro-B-type natriuretic peptide as a predictive marker of the outcomes of fibrinolytic therapy in acute ST-elevation myocardial infarction

Abstract Background: Risk assessment is vital in delivering appropriate treatment and enhancing patient outcomes during acute ST-elevation myocardial infarction (STEMI). This study sought to ascertain the significance of NT-proBNP in predicting the outcome of thrombolysis in acute STEMI. Materials and Methods: In this prospective study, we enrolled individuals with acute STEMI who underwent fibrinolytic therapy. Plasma N-terminal-proBNP (NT-proBNP) levels were assessed upon admission. Patients were categorized as thrombolysis success or failure groups based on electrocardiogram (ECG) criteria. The outcomes were measured in terms of in-hospital mortality and adverse cardiovascular events. Results: Thrombolysis achieved success in 59.13% of acute STEMI cases. Patients experiencing failed thrombolysis had a significantly longer mean time to reperfusion than those with successful thrombolysis (4.74 ± 2.42 vs. 5.97 ± 2.35 h, P = 0.0078). The median baseline NT-proBNP concentration was 983 pg/mL (interquartile range 777–2987 pg/mL). The plasma NT-proBNP levels on admission were notably higher in the thrombolysis failure group (P &lt; 0.001). NT-proBNP, time to reperfusion, heart rate, blood urea, and serum uric acid exhibited negative correlations with thrombolysis outcomes. The most prevalent adverse event was cardiac failure. Receiver operating characteristic (ROC) curve analysis indicated a robust association between NT-proBNP and in-hospital mortality. High NT-proBNP (&gt;983 pg/mL) and prolonged time to reperfusion (&gt;6 h) emerged as independent predictors of thrombolysis failure on multivariate logistic regression analysis (P = 0.017 and 0.035, respectively). Conclusion: Elevated plasma NT-proBNP upon admission during acute STEMI serves as a robust predictor for both fibrinolytic therapy failure and in-hospital mortality.

Impact of Quantitative ST-T Analysis in Patients With Suspected Myocardial Infarction Presenting With Right Bundle Branch Block

BackgroundWhile left bundle branch block (LBBB) is a well-known risk feature in patients with acute myocardial infarction, and a rapid invasive management is recommended, data supporting this strategy for patients with right bundle branch block (RBBB) is less robust. MethodsIn total, 2139 patients with suspected ST-elevation myocardial infarction (STEMI) were triaged to acute coronary angiography based on a prehospital 12-lead electrocardiogram (ECG). Sensitivity and specificity for STEMI-ECG criteria were compared in RBBB and non-BBB patients. Adjusted hazard ratios for 1-year overall mortality were computed. ResultsSTEMI was adjudicated in 1832/2139 (85.6%) of all patients and in 102/117 (87.2%) of RBBB patients. ST-segment deviation followed typical ST-T patterns in most RBBB patients. Of 17 RBBB patients without significant ST changes, STEMI was adjudicated in 14 (82%). Diagnostic accuracy of STEMI criteria was comparable in RBBB and non-RBBB patients for inferior (sensitivity: 51.1% vs 59.1%, P = .14; specificity: 66.7% vs 52.1%, P = .33) and anterior STEMI (sensitivity: 35.2% vs 36.6%, P = .80; specificity: 58.3% vs 49.5%, P = .55). Diagnostic performance was lower for lateral STEMI in RBBB patients (sensitivity: 14.8% vs 4.4%, P = .001; specificity: 75.0% vs 98.4%, P < .001). Patients with RBBB had higher 1-year mortality compared with non-BBB patients (hazard ratio 2.3%; 95% confidence interval, 1.25-4.21. ConclusionECG criteria used for detection of STEMI showed comparable diagnostic accuracy in RBBB and non-BBB patients. However, STEMI was frequently present in RBBB patients not fulfilling diagnostic ECG criteria. RBBB patients showed poorer outcome after 1 year. Consequently, the presence of RBBB in suspected STEMI cases signifies a high-risk feature, aligning with established guidelines.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

Background and ObjectiveLeft ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). MethodsUtilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. ResultsFor LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). ConclusionsThis study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

Electrocardiography versus Echocardiography in Severe Aortic Stenosis with the Consideration of Coexistent Coronary Artery Disease.

(1) Background: Coexistent coronary artery disease (CAD) might influence the ability of electrocardiogram (ECG) to identify echocardiographic left ventricular hypertrophy (ECHO-LVH) in patients with aortic stenosis (AS). We aimed to assess the relation between ECG-LVH (by the Sokolov-Lyon or Cornell criteria) and ECHO-LVH considering coexistent CAD. (2) Methods: We retrospectively analyzed the medical records of 74 patients (36 males) with severe AS who were hospitalized in the University Hospital in Cracow from 2021 to 2022. (3) Results: ECHO-LVH was present in 49 (66%) patients, whereas 35 (47.3%) patients had ECG-LVH. There was no difference between the rate of ECG-LVH in patients with vs. without ECHO-LVH. Single-vessel and multi-vessel CAD were diagnosed by invasive coronary angiography in 18% and 11% of patients, respectively. The sensitivity of the classical ECG-LVH criteria with regard to ECHO-LVH was low, reaching at best 41% for the Sokolov-Lyon and Cornell criteria. The results were similar and lacked a pattern when considering patients without significant stenosis, with single- and multi-vessel disease separately. Correlations between the left ventricular mass index and ECG-derived parameters were weak and present solely for the Lewis index (r = 0.31), R wave's amplitude >1.1 mV in aVL (r = 0.36), as well as the Cornell (r = 0.32) and Sokolov-Lyon (r = 0.31) voltage criteria (p < 0.01). The presence, location of stenoses, and CAD extent were not associated with the presence of either ECHO-LVH or ECG-LVH, irrespective of individual ECG-LVH criteria. (4) Conclusions: The sensitivity of classical ECG criteria for echocardiographic LVH in severe AS is low, regardless of coexistent CAD or its angiographic extent.

INTERNATIONAL CRITERIA EKG COMPARISON STUDY

https://youtu.be/4jm0uzAQNm8 BACKGROUND There has been an evolution of athlete specific Electrocardiogram (EKG) criteria over the years, resulting in improved specificity and lower false positive rates, starting with the European Society of Cardiology 2005 guidelines and most recently with the current 2017 International Recommendations. The consistency of EKG interpretation with the 2017 International Criteria have been compared between various groups, including local and specialized center physicians. Whether novice EKG interpreters (undergraduate/graduate students) can be taught to accurately interpret athletes’ EKGs with the 2017 International Criteria has not been extensively studied. This study seeks to assess the accuracy and variability of novice EKG interpreters, compared to cardiologist interpretations and expert readers. METHODS Three novice EKG interpreters (undergraduate exercise science students) were trained in interpreting EKGs of athletes with the 2017 International Criteria during one semester under the instruction of an expert reader. During an annual high school, sports screening day 1350 EKGs were collected and assigned a corresponding number. The on-site cardiologists evaluated the EKGs in real-time and classified as “normal” or “abnormal” according to the International Criteria. Following the sports physical day, three novice EKG interpreters (students), a cardiologist and a Clinical Exercise Physiology Professor (expert reader) were asked separately to classify the same EKGs as “normal or “abnormal” according to the International Criteria. All readers were blinded to the initial classifications made by the cardiologist during the sports physical event. Information regarding the athlete's age, gender, race/ethnicity, and sport was provided on the EKGs. We assessed the agreement between the cardiologist, expert reader and students in interpreting EKGs using Fleiss' kappa analysis. RESULTS 1350 athlete EKGs (males = 879; females = 471, age (mean + SD) 15.09 + 1.3y) including 37 (2.7%) abnormal cases were reviewed. The inter-rater agreement between novice readers, expert reader, and physicians in classifying an EKG as abnormal was good (k = 0.7, p &amp;lt; .001). CONCLUSION This study demonstrated that novice EKG readers could correctly classify EKGs based on the International Criteria as “normal and abnormal” to identify athletes at high risk of acute cardiovascular events

The Role of Electrocardiographic Markers for Predicting Atrial Fibrillation in Patients with Acute Ischemic Stroke: Data from the BIOSIGNAL Cohort Study.

P-wave abnormalities in the 12-lead electrocardiogram (ECG) have been associated with a higher risk of acute ischemic stroke (AIS) as well as atrial fibrillation (AF). This study aimed to assess pre-determined ECG criteria during sinus rhythm in unselected AIS patients and their value for predicting newly diagnosed atrial fibrillation (NDAF) after hospital admission. P-wave alterations were measured on 12-lead ECG on admission in all consecutively enrolled patients without known AF between October 2014 and 2017. The outcome of interest was NDAF, identified by prolonged electrocardiographic monitoring within one year after the index AIS. Univariable and multivariable logistic regression was applied to assess the magnitude and independence of the association between pre-selected ECG markers and NDAF. The discriminatory accuracy was evaluated with the area under the receiver operating characteristic curve (AUC), and the incremental prognostic value was estimated with the net reclassification index. NDAF was detected in 87 (10%) of 856 patients during a follow-up of 365 days. Out of the pre-selected ECG parameters, advanced interatrial block (aIAB) and PR interval in lead II were independently associated with NDAF in univariable regression analysis. Only aIAB remained a significant predictor in multivariable analysis. Adding aIAB to the best-performing multivariable regression model improved the discriminatory accuracy to predict NDAF from an AUC of 0.78 (95%-CI 0.77-0.80) to 0.81 (95%-CI 0.80-0.83, p < 0.001). aIAB is independently and highly associated with NDAF in patients with AIS, has high inter-rater reliability, and therefore may be helpful to refine diagnostic work-up to search for AF in AIS.

Echocardiographic and Angiographic Assessment of Right Ventricular Function and Right Coronary Artery Stenosis in Acute Inferior Wall Myocardial Infarction.

Cardiovascular diseases (CVDs) are a global concern. CVD remains a primary cause of death despite reduced coronary heart disease death rates. Acute coronary syndrome (ACS) involves myocardial infarction (MI) and unstable angina, sharing mechanisms such as plaque instability. Our study assesses the right ventricular (RV) function's predictive value in acute inferior wall MI (IWMI) to identify high-risk patients with an elevated likelihood of experiencing severe cardiac complications, hemodynamic instability, or a higher mortality risk following an acute IWMI. The research was conductedin the Department of Cardiology at theRajendra Institute of Medical Sciences (RIMS), Ranchi, from July 2021 to June 2022, following the necessary ethical approval. A cohort of 140 patients with IWMI, carefully chosen according to rigorous criteria, clearly understood the study's objectives before providing informed consent. The evaluations were conducted in the following order: clinical assessments, followed by blood testing, then echocardiography, and finally, coronary angiography. Furthermore, the study examined risk factors and utilized statistical methods to elucidate the associations between qualities and results. The study included 140 participants, with 61% being male and 39% female. Among the participants, 14% were aged 30-45, 50% were aged 46-60, and 30% were over 60. Age shows significant proportions in different categories. Diabetes, dyslipidemia, hypertension, and smoking/tobacco addiction did not differ among stenosis groups. Proximal right coronary artery (RCA) stenosis patients had elevated jugular venous pressure (JVP). The echocardiograms were performed within 48 hours of post-percutaneous coronary intervention, and significant differences between groups were observed. Participants with proximal stenosis had lower tricuspid annular plane systolic excursion (TAPSE) and right ventricular fractional area change (RVFAC), which showedcompromised RV systolic function. Proximal stenosis patients had reduced systolic motion velocity (Sm), indicating impaired myocardial contraction. Echocardiographic parameters such as early diastolic velocity (Em), atrial contraction velocity (Am), Em/Am ratio (a marker of diastolic function), isovolumic relaxation time (IVRT), isovolumic contraction time (IVCT), and ejection time (ET) between groups were different, indicating distinct cardiac functions. Proximal stenosis increased the myocardial performance index (MPI), indicating cardiac impairment. The left ventricular ejection fraction (LVEF) was comparable in the two stenosis groups, indicating similar left ventricular performance. Echocardiography showed significant RV function differences in acute inferior wall ST-segment elevation myocardial infarction (STEMI) patients with proximal and distal RCA lesions. RV dysfunction is linked to right ventricle myocardial infarction (RVMI), and echocardiographic markers can provide valuable insights. Results emphasizethat acute inferior wall STEMI is diagnosed by electrocardiogram (ECG) criteria, particularly ST-segment elevation. However, these markers emphasize the importance of RV assessment in RCA involvement assessment. These findings suggest that RV function can help diagnose acute inferior wall STEMI RCA involvement. In acute inferior STEMIs, RV function echocardiography is essential for RCA lesion location.

Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats.

Myocardial infarction (MI) is one of the leading causes of death worldwide. It is well accepted that early diagnosis followed by early reperfusion therapy significantly increases the MI survival. Diagnosis of acute MI is traditionally based on the presence of chest pain and electrocardiogram (ECG) criteria. However, around 50% of the MIs are without chest pain, and ECG is neither completely specific nor definitive. Therefore, there is an unmet need for methods that allow detection of acute MI or ischaemia without using ECG. Our hypothesis is that a hybrid physics-based machine learning (ML) method can detect the occurrence of acute MI or ischaemia from a single carotid pressure waveform. We used a standard occlusion/reperfusion rat model. Physics-based ML classifiers were developed using intrinsic frequency parameters extracted from carotid pressure waveforms. ML models were trained, validated, and generalized using data from 32 rats. The final ML models were tested on an external stratified blind dataset from additional 13 rats. When tested on blind data, the best ML model showed specificity = 0.92 and sensitivity = 0.92 for detecting acute MI. The best model's specificity and sensitivity for ischaemia detection were 0.85 and 0.92, respectively. We demonstrated that a hybrid physics-based ML approach can detect the occurrence of acute MI and ischaemia from carotid pressure waveform in rats. Since carotid pressure waveforms can be measured non-invasively, this proof-of-concept pre-clinical study can potentially be expanded in future studies for non-invasive detection of MI or myocardial ischaemia.

Development, Feasibility, and Initial Evaluation of an Active Learning Module for Teaching Pediatric ECG Interpretation and Entrustable Professional Activities to Clinical Medical Students.

Electrocardiogram (ECG) interpretation is an essential medical skill that quickly informs cardiovascular clinical decisions. Web-based modules are emerging clinical adjuncts to teach ECG interpretation, however, undergraduate and graduate medical education focus on adult ECG criteria.1,2 Asynchronous remote active learning (ARAL) pediatric ECG curricula have not been described, but are necessary to teach age-specific principles, and should be designed incorporating both student and graduate learner needs.

Physiologic Variants of Cardiac Conduction (Aberration, Gap, Supernormal Conduction).

Wide QRS complexes during supraventricular rhythms can be caused by fixed bundle branch block, functional (intermittent) bundle branch block, preexcitation, or toxic/metabolic causes. Functional bundle branch block can be caused by long-short aberrancy (usually physiologic), or acceleration/deceleration dependent aberrancy (usually pathologic). Electrocardiogram criteria have been proposed to differentiate aberration from ventricular tachycardia; they are not always accurate. The gap phenomenon "paradox" is that with increasingly premature extrastimuli, progressive proximal conduction delay allows time for distal recovery of excitability. Supernormal conduction may explain unusual conduction phenomena in patients with abnormal His-Purkinje function or poorly conducting accessory pathways.

In search of heart barometer—advantage of left atrial dimension over electrocardiographic indices of left ventricular hypertrophy in arterial hypertension

Aim: Left ventricular (LV) hypertrophy (LVH) is a common and relevant complication of arterial hypertension (AH) and 12-lead electrocardiogram (ECG) is widely used for its preliminary assessment. The aim of the study was to compare the correlations of four ECG-derived criteria of LVH and left atrial (LA) anteroposterior diameter with LVH assessed by echocardiography and expressed as left ventricular mass (LVM) index (LVMI) in search of the most accurate preliminary indicator of LVH. Methods: The study included 61 subjects with AH [age (year) 69 ± 10, 17 females] and 27 without AH, (age 40 ± 9, 10 females) evaluated with 12-lead ECG and transthoracic echocardiography (TTE). As the ECG-based criteria of LVH Sokolow-Lyon index (SLI), Cornell voltage (CV), Cornell product (CP), and Romhilt-Estes point score (RES) system were evaluated. The ECG indices and LA diameter were correlated with LVMI and correlations coefficients were compared. Results: Among ECG-LVH indicators SLI showed the closest correlation with LVMI [rank correlation coefficients (rho) = 0.38, P &lt; 0.0001], followed by CV and CP with rho = 0.33, P = 0.002 and rho = 0.32, P = 0.002, respectively, whereas RES did not correlate significantly with LVMI. The strongest correlation with LVMI was found for the LA diameter with rho = 0.73 and P &lt; 0.0001, showing an even stronger correlation in women—rho = 0.8 (P &lt; 0.0001) vs. rho = 0.65 (P &lt; 0.0001) in men. In the multivariate analysis, the LA was the only independent predictor of the increased LVMI with R2 = 0.52, P &lt; 0.0001. Conclusions: LA diameter outperformed significantly the ECG indices as far as the correlation with LVMI was concerned and emerged as the only independent predictor of mild and moderate LVH in hypertensive patients. Among the ECG criteria, the strongest correlation was shown for SLI, followed by CV and CP indices.

Classifying hypertension mediated left ventricular hypertrophy phenotypes from the 12-lead electrocardiogram using machine learning

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Pat Merriman Clinical Research Training Fellowship Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been reported using cardiac magnetic resonance (CMR) LV mass to volume ratio: normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between hypertension mediated LVH phenotypes is unknown. Purpose The aim of this study was to classify hypertension mediated LVH phenotypes using 12-lead electrocardiogram (ECG) and clinical data for cost-effective patient stratification. Methods 24,535 hypertensive participants from UK Biobank imaging study were categorised into the CMR-defined LVH phenotypes. ECG biomarkers with a physiological association with LVH (including QRS biomarkers, Sokolow-Lyon and Cornell voltage) were extracted from the 12-lead ECG of each participant using in-house algorithms. Vascular risk factors known to be associated with LVH were also ascertained from the clinical data of each participant. Classification models integrating ECG biomarkers and clinical data were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% train and 20% test sets for performance measurement. Ten-fold cross validation was applied and the training set was downsampled to the minority class. Results Among the participants (mean age ± standard deviation 65.3 ± 7.4 years), 23,312 (95.0%) had normal LV, 900 (3.7%) LV remodelling, 220 (0.8%) eccentric LVH and 103 (0.4%) concentric LVH. R wave amplitude V5, S wave amplitude V3 and QT duration V6 (P&amp;lt;0.001) were the top ECG biomarkers and systolic blood pressure (p&amp;lt;0.001) the top clinical predictor of LVH phenotypes (Figure 1). Classification with logistic regression had an accuracy of 77% (sensitivity 55%, specificity 85%, Area under the receiver operator curve [AUC] 0.76), SVM 80% accuracy (sensitivity 61%, specificity 87%, AUC 0.75) and RF 75% accuracy (sensitivity 49%, specificity 83%, AUC 0.75). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical data alone. Conclusion A combination of automatically extracted ECG biomarkers and clinical data were able to discriminate the hypertension mediated LVH phenotypes. An advanced classification approach improves selection of hypertensive patients with LVH, thereby adding prognostic value to traditional risk models. We are currently validating our findings in an external population and using deep learning for comparison.

Are Electrocardiographic Criteria Reliable for Left Ventricular Hypertrophy Detection in Indian Adults?

Left ventricular hypertrophy (LVH) detection is vital to the risk stratification of adults at risk of adverse cardiovascular events such ascoronary heart disease, cerebrovascular disease, and aortic aneurysms. Electrocardiogram (ECG), a non-invasive, cost-effective instrument has been widely used as a screening tool for LVH. The objective of this study was to determine the diagnostic accuracy of seven frequently used ECG criteria in high-risk Indian adults in comparison with echocardiography. ECG and transthoracic echocardiography were performed in adults older than 18 years with at least one cardiac risk factor (chronic hypertension, obesity, ischemic heart disease, and type 2 diabetes mellitus). Precision and accuracy were calculated for the various ECG criteria against LVH based on left ventricular mass index (LVMI) and cardiac remodeling by echocardiography. A total of 220 participants were enrolled. Of these, 96 had LVH by echocardiography. There was marked variability in LVH detection by the different ECG criteria: 28 by Sokolow-Lyon criteria, 26 by Cornell criteria, 24 by Lewis criteria, 46 by Scott criteria, eight by Romhilt-Estes criteria, six by Modified Cornell criteria, and only two by Roberts criteria. Agreement statistics between ECG criteria and LVMI showed that none of them had a good agreement for LVH detection. None of the ECG criteria were sensitive enough to rule out ventricular hypertrophy. In the context of cardiac remodeling, the ECG criteria had high sensitivity but low specificity and, hence, limited clinical relevance.

Comparison of four electrocardiographic criteria for the detection of cardiac abnormalities in Singapore athletes.

Sudden cardiac death in athletes is a rare occurrence, the most common cause being hypertrophic cardiomyopathy, which increases the risk of sustained ventricular tachycardia or ventricular fibrillation. Most of these young athletes are asymptomatic prior to the cardiac arrest. Several electrocardiogram criteria such as the European Society of Cardiology group 2 Criteria changes, Seattle Criteria, Refined Criteria, and most recently the 2017 International Criteria, have sought to improve the accuracy of identifying these at-risk athletes during pre-participation screening while minimising unnecessary investigations for the majority of athletes at low risk.We aimed to compare the above four criteria in our Singapore athlete population to identify which criterion performed the best in detecting cardiac abnormalities on echocardiography. Out of 1,515 athletes included in Changi General Hospital, Singapore registry between June 2007 and June 2014, the electrocardiograms of 270 athletes with further cardiac investigations were analysed. We compared the above four electrocardiographic criteria to evaluate which performed best for detecting cardiac abnormalities on echocardiography in our Southeast Asian athlete population. The European Society of Cardiology, Seattle, Refined and 2017 International Criteria had a sensitivity of 20%, 0%, 20% and 5%, respectively; a specificity of 64%, 93%, 84% and 97%, respectively; a positive predictive value of 4%, 0%, 9% and 11%, respectively; and a negative predictive value of 91%, 92%, 93% and 93%, respectively for detecting abnormalities on echocardiography. The latest 2017 International Criteria performed the best as it had the highest specificity and positive predictive value, joint highest negative predictive value, and lowest false positive rate.

Prediction of ablation success of idiopathic premature ventricular contractions with an inferior axis using the twelve-lead electrocardiogram

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Catheter ablation is the first line treatment for idiopathic premature ventricular contractions (PVCs) from the right ventricular outflow tract (RVOT) but not left ventricular outflow tract (LVOT). However, differentiating the two using the twelve-lead electrocardiogram (ECG) is challenging. As an alternative strategy to guide clinical decision-making, we sought to find ECG criteria that predict outcome of catheter ablation directly rather than focus only. Methods This observational single center study included patients undergoing an ablation procedure for idiopathic PVCs with an inferior axis (positive QRS in leads II, III and aVF) at Hannover Heart Rhythm Center between 2012 and 2020. Patients were excluded if no activation mapping was performed because of low PVC activity. A complete suppression of the index PVC at the end of the ablation procedure was considered as ablation success. PVC axis was estimated with the help of the hexaxial reference system, and as an objective control was also calculated measuring QRS voltages and using three formulas: Arctan (aVF/I), Arctan ((2*II-I)/(Sqr(3)*I)) and +/-Arctan ((2*aVF)/(Sqr(3)*I)). Results The analysis included 108 patients (53% male, age 53 ± 17 years) with a PVC count of 22063 ± 14426 /24h. The dominant PVC had Left Bundle Branch Block (LBBB) like morphology in 80%, a mean transition (TZ)-score of 2.3 ± 1.2 and a mean axis of 87 ± 15°, estimated with the help of the hexaxial system. There was no significant difference between axis as estimated with the help of the hexaxial system compared to calculation via any of the formulas (87 ± 14, 87 ± 12 and 86 ± 16°, respectively). Overall ablation success was 82%. Considering ablation outcome according to the identified PVC localization, 46 of 47 cases (97.9%) of RVOT focus and 38 of 39 cases (97.4%) of LVOT focus were successful. On the other hand, all 8 cases with a parahisian focus were not successful and only 4 of 14 cases (28.6%) with a CS/LV summit focus were successful. Two ECG criteria were found to predict ablation failure: a more leftward PVC axis (p=0.015), which was associated with a parahisian focus (p&amp;lt;0.001), and an early precordial transition compared to the QRS complex during AV-nodal conduction (p=0.016), which was associated with a CS/LV summit focus (p&amp;lt;0.001). Using ROC statistics, a cutoff value of 75° for PVC axis and of 2.75 for TZ-Score index predicted ablation success with a positive predictive value of 93% if neither parameter applied (prediction of ablation failure, if either applied, 53 %). Conclusion Two ECG criteria – a more leftward PVC axis and an early precordial transition compared to sinus rhythm – predicted ablation failure and a parahisian or CS/LV summit focus. These parameters should be considered when evaluating a patient for catheter ablation of PVCs with an inferior axis.

Incidence of infranodal conduction delay in patients with left bundle branch block after transcatheter aortic valve replacement: impact of the 2021 ESC guidelines for cardiac pacing

Abstract Funding Acknowledgements Type of funding sources: None. Background Left bundle branch block (LBBB) remains the most frequent conduction disorder after transcatheter aortic valve replacement (TAVR). Electrophysiological (EP) testing is recommended in the 2021 ESC Guidelines for cardiac pacing with a IIa indication in patients with new LBBB with QRS ≥ 150ms or PR ≥ 240ms or with pre-existing LBBB with a delta of QRS/PR intervals of ≥ 20ms after TAVR. A cut-off for the HV interval of ≥ 70ms to consider pacemaker implantation was proposed. However, evidence for these proposed cutoffs is scarce in LBBB patients after TAVR. Aims To assess the incidence of infranodal conduction delay in patients with LBBB after TAVR and assess safety of predefined criteria for PR and QRS width to identify infranodal conduction delay. Methods We analyzed a consecutively included cohort of patients who underwent TAVR between August 2014 and May 2022. A 12-lead surface electrocardiogram (ECG) was available in all patients before and the day after TAVR. Patients with LBBB also received an EP testing the following day, irrespective of surface ECG measurements. Results Of 1033 patients who received TAVR between August 2014 and May 2022, 708 were included in the study (Figure). Mean age was 82 years, 53% were female. The used valve types were homogenously distributed. Among 708 patients, 180 patients demonstrated LBBB (25%) the next day (86%new-onset LBBB). Infranodal conduction delay, defined as a HV interval ≥ 70ms, was observed in 18/180 patients (10%) with LBBB on the day after TAVR. Using the new ESC guidelines, EP testing was indicated in 86 of 177 patients (49%) with LBBB after TAVR, with 14 of 18 (78%) being identified with an HV interval of ≥ 70ms (sensitivity 78%, negative predictive value 94%). Thus, in 4 patients (3 men) with new LBBB, the HV interval of ≥ 70ms would have been missed based on surface ECG readings suggested by current guidelines. Stratified by sex, the predefined criteria for PR and QRS demonstrated a sensitivity and negative predictive value of 86% and 98% in women and 72% and 90% in men, respectively. Conclusion In patients with LBBB after TAVR the incidence of infranodal conduction delay defined as a HV interval ≥ 70ms is 10%. Predefined surface ECG criteria offer excellent safety for rule out of infranodal conduction delay, especially in women, while caution might be advisable using the same criteria in men.

Electrocardiographic Waveform Abnormalities in Patients With Structural Heart Disease

Scenario: This 12-lead electrocardiogram (ECG) was obtained in a 72-year-old man arriving in the emergency department with shortness of breath and dizziness. Prior medical history includes myocardial infarction (MI), heart failure (HF), chronic obstructive pulmonary disease (COPD), diabetes (type 2), and paroxysmal atrial fibrillation. Current prescribed medications include a β-blocker, aspirin, angiotensin-converting enzyme inhibitor, furosemide, and diltiazem. His blood pressure was 98/50 mm Hg, respiratory rate 24/min, and oxygen saturation as measured by pulse oximetry (Spo2) was 92% on 3 L of oxygen. He was most comfortable sitting up with his arms on the bedside table, and said he felt better after 40 mg of furosemide was administered intravenously.Sinus rhythm with premature atrial complexes (PACs), possible right and left atrial enlargement, prior inferior wall MI (q waves in leads III and aVF), incomplete left intraventricular conduction defect (fQRS waves), left ventricular hypertrophy, and ST-segment and T-wave abnormalities may suggest anterolateral ischemia.Although the underlying rhythm is irregular, suggesting atrial fibrillation, P waves are visible before every QRS complex; thus, this is sinus rhythm with frequent PACs. PACs are not uncommon in patients with structural heart disease and COPD, like this one. The pause after the PACs is a normal physiologic response and represents a compensatory pause associated with PACs. The ECG criterion for right atrial enlargement is a P-wave amplitude greater than 2.5 mm in leads II, III, and aVF (so-called P pulmonale) and can be seen with COPD. Left atrial enlargement is due to pressure or volume overload of the left atrium and can be assessed in V1 (biphasic P waves with negative terminal portion >40 ms long and >1 mm deep). Biatrial enlargement is consistent with this patient’s medical history (MI, HF, and left ventricular hypertrophy); however, ECG is not the reference standard method, so the diagnosis should be confirmed by echocardiography. The T-wave inversion and ST-segment depression most likely represent a “strain” pattern seen with left ventricular hypertrophy, but acute myocardial ischemia should be ruled out given the patient’s prior MI.Immediate management includes addressing the acute respiratory distress and low blood pressure, with HF exacerbation a likely source. This patient’s symptoms were somewhat relieved after the intravenous furosemide, which supports this diagnosis. Checking a venous blood sample for brain natriuretic peptide (BNP) to assess for HF, troponin to rule out myocardial ischemia, and electrolyte levels would help. A chest radiograph would be useful, as might arterial blood gas analysis, given his elevated rate of breathing and the 92% Spo2 with 3 L of oxygen. Findings on physical examination, especially lung and heart sounds, peripheral edema, and daily weight should be evaluated. Ultimately, this patient’s BNP level was 3400 ng/mL, which corroborates HF, and his troponin was negative for myocardial ischemia. He was discharged home 3 days later.