Electrocardiographic Algorithm Research Articles

Abstract 17188: Electrocardiographic Algorithms for Localization Ventricular Ectopy From the Ventricular Outflow Tracts Demonstrate Poor Accuracy and Agreement

Background: Premature ventricular contractions (PVCs) and ventricular tachycardia (VT) commonly arise from the outflow tracts of the left and right ventricles and can be treated with catheter ablation when symptomatic or associated with cardiomyopathy. Accurate localization of these ectopic sources aids procedural planning. Several different electrocardiographic (ECG) algorithms have been published, but their relative performance has not been assessed. Objective: Compare the accuracy and agreement of three ECG algorithms for PVC localization. Methods: ECGs from patients who underwent catheter ablation for PVCs or VT originating the outflow tract region were analyzed using three published PVC localization algorithms. The predictions from these algorithms were then compared to the site of successful catheter ablation (right ventricular outflow tract [RVOT], left ventricular outflow tract [LVOT], or epicardium). The accuracy and agreement of the three algorithms were compared. Results: A total of 43 patients were evaluated and ectopy was suppressed via ablation from a single chamber in 37 (86%) of patients. VT/PVCs originated from the RVOT (N=10), LVOT (N=26), and epicardium (N=1). The three studied algorithms demonstrated accuracy rates of 46-70%. The algorithms disagreed on the predicted ectopy location for 19 (51%) patients. When all algorithms agreed (N=18), the prediction was accurate for only 11 patients (61%). Conclusion: Rules-based ECG algorithms demonstrate poor performance and use of different published algorithms frequently result in disparate results. More accurate and consistent methods incorporating individualized electroanatomic variation may improve accuracy and periprocedural management for PVC and VT ablation.

Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.

Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes. We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan-Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI. This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases. The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions. [cris.nih.go.kr], identifier (KCT0007881).

Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm: External Validation of the AI Approach

BackgroundAtrial fibrillation (AF) may occur asymptomatically and can be diagnosed only with electrocardiography (ECG) while the arrhythmia is present. ObjectivesThe aim of this study was to independently validate the approach of using artificial intelligence (AI) to identify underlying paroxysmal AF from a 12-lead ECG in sinus rhythm (SR). MethodsAn AI algorithm was trained to identify patients with underlying paroxysmal AF, using electrocardiographic data from all in- and outpatients from a single center with at least 1 ECG in SR. For patients without AF, all ECGs in SR were included. For patients with AF, all ECGs in SR starting 31 days before the first AF event were included. The patients were randomly allocated to training, internal validation, and testing datasets in a 7:1:2 ratio. In a secondary analysis, the AF prevalence of the testing group was modified. Additionally, the performance of the algorithm was validated at an external hospital. ResultsThe dataset consisted of 494,042 ECGs in SR from 142,310 patients. Testing the model on the first ECG of each patient (AF prevalence 9.0%) resulted in accuracy of 78.1% (95% CI: 77.6%-78.5%), area under the receiver-operating characteristic curve of 0.87 (95% CI: 0.86-0.87), and area under the precision recall curve (AUPRC) of 0.48 (95% CI: 0.46-0.50). In a low-risk group (AF prevalence 3%), the AUPRC decreased to 0.21 (95% CI: 0.18-0.24). In a high-risk group (AF prevalence 30%), the AUPRC increased to 0.76 (95% CI: 0.75-0.78). This performance was robust when validated in an external hospital. ConclusionsThe approach of using an AI-enabled electrocardiographic algorithm for the identification of patients with underlying paroxysmal AF from ECGs in SR was independently validated.

A Four-Stepwise Electrocardiographic Algorithm for Differentiation of Ventricular Arrhythmias Originated from Left Ventricular Outflow Tract.

Several electrocardiographic algorithms have been proposed to identify the site of origin for the ventricular arrhythmias (VAs) from the left ventricular outflow tract (LVOT) versus right ventricular outflow tract. However, the electrocardiographic criteria for distinguishing VAs originated from the different sites of LVOT is lacking. We aimed to develop a simple and efficient ECG algorithm to differentiate LVOT VAs originated from the aortic root, AMC and LV summit. We analyzed 12-lead ECG characteristics of 68 consecutive patients who underwent successful radiofrequency catheter ablation of symptomatic VAs from LVOT. Patients were divided into RCC (right coronary cusp) group (n = 8), the L-RCC (the junction between the LCC and RCC) group (n = 21), the LCC (left coronary cusp) group (n = 24), the aortomitral continuity (AMC) group (n = 9) and the LV summit group (n = 6) according to the final ablation sites. Measurements with the highest diagnostic performance were modeled into a 4-stepwise algorithm to discriminate LVOT VAs. The performance of this novel algorithm was prospectively tested in a validation cohort of 43 consecutive patients undergoing LVOT VAs ablation. Based on the accuracy of AUC, a 4-stepwise ECG algorithm was developed. First, the QS duration in aVL > 134 ms was used to distinguish VAs from AMC, LV summit and VAs from aortic root (80% sensitivity and 76% specificity). Second, the R duration in II > 155 ms was used to differentiate VAs from LV summit and VAs from AMC (67% sensitivity and 56% specificity). Third, the ratio of III/II < 0.9 was used to discriminate VAs from RCC and VAs from LCC, L-RCC (82% sensitivity and 63% specificity). Fourth, the QS duration of aVR > 130 ms was used to discern VAs from LCC and VAs from L-RCC (75% sensitivity and 62% specificity). In the prospective evaluation, our 4-stepwise ECG algorithm exhibited a good predictive value. We have developed a novel and simple 4-stepwise ECG algorithm with good predictive value to discriminate the AVs from different sites of LVOT.

Validation of an electrocardiographic algorithm for the detection of cardiac amyloidosis

Abstract Background Despite new therapies, diagnosis of cardiac amyloidosis (CA) is often delayed. We recently developed a simple electrocardiographic (ECG) algorithm to suspect CA without the aid of advanced imaging modalities (Figure). Methods The aim of this study was to validate the algorithms' usefulness in clinical practice. ECG readings from patients with CA, heart failure with preserved ejection fraction (HFpEF), and hypertrophic cardiomyopathy (HCMP) were analyzed in a blinded fashion. Results 884 patients were included. Patients with pacemakers were excluded, leaving 827 ECGs (237 CA, 407 HFpEF, 183 HCMP) for final analysis. A characteristic pattern defined by the algorithm was visually perceptible in 165 ECGs (69.6%) of the amyloidosis patients vs. 114 (28%) of HFpEF vs. 22 (12.0%) of HCMP patients (p<0.001). The area under the curve (AUC) for the detection CA was 0.75 with a sensitivity of 69.6% and a specificity of 76.9% (Figure). Binary logistic regression analysis revealed that the presence of a distinctive pattern increased the probability of CA with an odds ratio of 7.66 (CI: 5.47–10.72; p<0.001). Conclusion This easy-to-use ECG algorithm has proven helpful to suspect CA. Our tool may significantly improve the treatment of heart failure patients by identifying those with amyloidosis-related disease. Funding Acknowledgement Type of funding sources: None.

Differential electrocardiographic changes related to the culprit coronary artery in patients with acute myocardial infarction and left bundle branch block

Abstract Background The electrocardiographic (ECG) recognition of acute myocardial infarction (AMI) in patients with concurrent left bundle branch block (LBBB) is hampered by background changes in QRS complex duration and ST segment, potential directly caused by LBBB. Currently available ECG algorithms were constructed to suspect the presence of AMI but they not take into a count the culprit coronary artery and this fact could explain their low sensitivity. Objectives To analyze the influence of the culprit coronary artery on the ECG changes and on the diagnostic yielding of ECG algorithms in patients with acute myocardial infarction and LBBB. Methods We analyzed the index ECG of 240 patients with acute chest pain and LBBB referred to emergent coronary angiography. Patients were classified in 3 groups according their coronary anatomy: a) acute coronary occlusion (n=48); b) critical non occlusive lesion (n=45); and c) absence of coronary lesions (n=76). In all ECGs we measured the ST-segment shifts and QRS duration and compared the pattern of changes in relation to the culprit coronary artery. Results Location of the culprit coronary artery induced differential ECG patterns: a) mean ST segment elevation ≥3mm in leads V2-V3 in patients with LAD occlusion (n=27); b) absence of ST segment depression in leads V5-V6 in LCX occlusion (n=11); and c) mean ST segment elevation ≥1.5 mm in leads III-aVF and attenuation of the ST elevation in leads V1-V2 (mean <1mm) in cases of RCA occlusion (n=10). Patients with critical non occlusive lesions (n=45) did not show particular ECG changes. QRS complex duration was longer in patients with LCX occlusion. Sensitivity of the Sgarbossa score was around 50%, sensitivity of the Smith score was >70% in all coronary groups and sensitivity of Barcelona score was really good for LCX and RCA occlusion. The combination of the three algorithms increased the sensitivity in all groups (>74%) but more markedly in patients with LCX occlusion (90.9%) and RCA occlusion (90%). All the ECG algorithms lose sensitivity for the critical non occlusive lesions group. Conclusions Patients with acute myocardial infarction and LBBB present distinct patterns of QRS and ST segment changes and different diagnostic yielding of ECG algorithms in relation to the location of the occluded coronary artery. Data suggest that ECG prediction of the culprit coronary artery in patients with AMI and LBBB might be possible using artificial intelligence systems in larger series of patients. Funding Acknowledgement Type of funding sources: None.

Atrial fibrillation can be diagnosed using a combined wrist-worn photoplethysmography and electrocardiography

Abstract Background Current ESC Clinical Practice Guideline for management of atrial fibrillation requires a minimum of 30-seconds of atrial fibrillation (AF) to be visually confirmed from an electrocardiography (ECG) recording for diagnosis. Single-channel ECG recorded from any measurement lead is sufficient. The current method of 24–48 h Holter ECG may not detect rarely occurring AF episodes, and thus extended duration recording devices are needed. Intermittent portable ECG devices intended for clinical use exist but those lack continuous rhythm monitoring and are thus unable to notify the user in case of asymptomatic episode. Purpose We evaluated the performance of a wrist-worn solution (Fig. 1) intended for clinical use that combines continuous photoplethysmography (PPG) -based rhythm assessment with intermittent ECG measurements in the detection of atrial fibrillation in ambulatory conditions. Methods 30 adult patients with previous diagnosis of AF were recruited for 48-hour measurements. Simultaneous 5-lead Holter monitoring was used as the reference. The patients were taking intermittent ECG measurements at pre-scheduled times (4 times a day), when notified by the device (irregular rhythm detected by PPG), and if feeling symptoms of AF. Results The obtained sensitivity and specificity for PPG-based AF detection were 95.5% and 99.2%, respectively when evaluated in 5-minute segments. Only the segments during which adequate amount, approximately 30-seconds, of good quality PPG data was available, were considered in the analysis. On average this equals 54% of the segments. Patients took in total 456 30-second ECG measurements. Out of these 411 and 392 were assessed as analysable quality by ECG algorithm and visually by two cardiologists, HS and KK, respectively. Automatic ECG analysis algorithm labelled ECG measurements taken during atrial fibrillation correctly as “Possible arrhythmia” with 95.7% sensitivity and 92.0% specificity. 99.5% of the analysable quality ECGs measured during AF were marked with any type of label e.g., tachycardia that highlights them to the clinician. An example ECG measurement with markings of the analysis algorithm an overall classification is shown in Fig. 2. All patients having AF during the 48-hour monitoring (14/30) were correctly identified based on the wrist device data. Conclusion Based on the obtained results, wrist-worn device combining continuous PPG and intermittent ECG measurements can be reliably used in detecting atrial fibrillation in ambulatory conditions. The user-friendliness and unobtrusiveness of wrist-worn technology enables virtually unlimited monitoring time thus facilitating the detection of rarely occurring AF episodes. The information of continuous rhythm monitoring can also be used for the estimation of AF burden. The solution used in the study has received medical device CE certification in early 2022. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The study was funded by PulseOn company.

Artificial intelligence-enabled electrocardiographic algorithm for the detection of left ventricular dysfunction in emergency department patients undergoing high-sensitivity cardiac troponin T

Abstract Background Artificial intelligence-augmented electrocardiogram (AI-ECG) algorithms have been developed from the standard 12-lead ECG and validated for the recognition of left ventricular systolic dysfunction (LVSD), defined as LV ejection fraction (LVEF)≤35%. Whether AI-ECG facilitates identification of LVSD and is associated with adverse outcomes in emergency department (ED) patients undergoing high-sensitivity cardiac troponin (hs-cTnT) testing is uncertain. Purpose To investigate the diagnostic and prognostic performance of AI-ECG in ED patients undergoing hs-cTnT measurement. Methods Observational US cohort study of ED patients undergoing hs-cTnT measurement. Cases with hs-cTnT increases >sex-specific 99th percentiles were adjudicated following the Fourth Universal Definition of Myocardial Infarction (MI). Post-discharge major adverse cardiac events (MACE) included death, MI, heart failure (HF) hospitalization, stroke or transient ischemic attack, and new onset atrial fibrillation/flutter during 2-years follow-up. The AI-ECG network output, which is a continuous number between 0–1, that provides a probability of LVSD, was obtained for each patient from the first ECG during the index presentation. An AI-ECG threshold of ≥0.256 indicates a positive screen that correlates with a high probability of LVSD. Results Among 1977 patients, 1729 (87%) had a negative AI-ECG screen, while 248 (13%) had a positive AI-ECG screen. Patients with a positive AI-ECG screen were older and had more comorbidities. As compared to patients with hs-cTnT≤99th percentile in whom AI-ECG was positive in 5.8%, those with hs-cTnT>99th percentile had a positive AI-ECG in 22% of cases (p<0.0001). Based on adjudicated diagnoses, the frequency of a positive AI-ECG was 20% in myocardial injury, 38% in type 1 MI, and 20% in type 2 MI. At 2-years follow-up, as compared to patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE (48% vs. 21%, p<0.0001, adjusted HR 1.39, 95% CI 1.11–1.75) (Figure 1), mainly because of more deaths (43% vs. 30%, p=0.004) and HF hospitalizations (36% vs. 13%, p<0.0001). A positive AI-ECG was associated with a higher risk for MACE (60% vs. 41%, p<0.0001, adjusted HR 1.30, 95% CI 1.02–1.64) in those with hs-cTnT increases >99th percentile, but not in those without hs-cTnT increases. Among patients with an echocardiogram during index presentation or within 30-days (n=452), the diagnostic accuracy of AI-ECG for LVEF ≤35% was 81.4% (95% CI 77.5, 84.9) with a negative predictive value of 96.5% (95% CI 94.0, 98.2). A normal LVEF (>50%) was observed in 87% of those with a negative AI ECG, whereas in those with a positive AI-ECG LVEF was reduced (<50%) in 60%. Conclusions Among ED patients evaluated with hs-cTnT, a positive AI-ECG screen for LVSD identifies patients at high risk of MACE. These findings are largely because of more deaths and HF hospitalizations in those with hs-cTnT increases >sex-specific 99th percentiles. Funding Acknowledgement Type of funding sources: None.

Simplified Integrated Clinical and Electrocardiographic Algorithm for Differentiation of Wide QRS Complex Tachycardia: The Basel Algorithm.

Prompt differential diagnosis of wide QRS complex tachycardia (WCT) is crucial to patient management. However, distinguishing ventricular tachycardia (VT) from supraventricular tachycardia (SVT) with wide QRS complexes remains problematic, especially for nonelectrophysiologists. This study aimed to develop a simple-to-use algorithm with integration of clinical and electrocardiographic (ECG) parameters for the differential diagnosis of WCT. The 12-lead ECGs of 206 monomorphic WCTs (153 VT, 53 SVT) with electrophysiology-confirmed diagnoses were analyzed. In the novel Basel algorithm, VT was diagnosed in the presence of at least 2 of the following criteria: 1)clinical high risk features; 2) lead II time to first peak >40ms; and 3) lead aVR time to first peak >40ms. The algorithm was externally validated in 203 consecutive WCT cases (151 VT, 52 SVT). Its' diagnostic performance and clinical applicability were compared with those of the Brugada and Vereckei algorithms. The Basel algorithm showed a sensitivity, specificity, and accuracy of 92%, 89%, and 91%, respectively, in the derivation cohort and 93%, 90%, and 93%, respectively, in the validation cohort. There were no significant differences in the performance characteristics between the 3 algorithms. The evaluation of the clinical applicability of theBasel algorithm showed similar diagnostic accuracy compared with the Brugada algorithm (80% vs 81%; P=1.00), but superiority compared with the Vereckei algorithm (72%; P=0.03). The Basel algorithm, however, enabled a faster diagnosis (median 36 seconds vs 105 seconds for the Brugada algorithm [P=0.002] and 50 seconds for the Vereckei algorithm [P=0.02]). The novel Basel algorithm based on simple clinical and ECG criteria allows for a rapid and accurate differential diagnosis of WCT.

CE-542-02 ISOELECTRIC OR BIPHASIC DELTA WAVE IN V1 IS A NEW ACCURATE PREDICTOR FOR EPICARDIAL LOCATION OF POSTEROSEPTAL ACCESSORY PATHWAY

Prediction of accessory pathway (AP) location is crucial for successful catheter ablation, especially for posteroseptal AP, due to its anatomical complexity. To this day, there is no validated electrocardiographic algorithm or criterion for accurate prediction of epicardial posteroseptal AP requiring ablation from coronary sinus (CS) or its branches.

Takotsubo Syndrome in the Emergency Room — Diagnostic Challenges and Suggested Algorithm

Takotsubo syndrome is an important condition to consider among patients with acute chest pain in the emergency room. It often mimics acute coronary syndrome since chest pain and ECG changes are key features in both conditions. The hallmark of takotsubo syndrome is transient left ventricular dysfunction (characterized by apical ballooning) followed by complete echocardiographic recovery in most cases. Although most patients exhibit a benign course, lethal complications may occur. The use of hand-held point-of-care focused cardiac ultrasound may be helpful for early identification of takotsubo syndrome and distinguishing it from acute coronary syndrome and other cardiovascular emergencies. Emergency room physicians should be familiar with typical and atypical presentations of takotsubo syndrome and its key electrocardiographic changes. The approach in the emergency room should be based on a combination the clinical presentation, ECG, and handheld echocardiography device findings, rather than a single electrocardiographic algorithm.

Can we improve the accuracy of electrocardiographic algorithms for accessory pathway location in children?

Predicting an accessory pathway location is extremely important in pediatric patients. We designed a study to compare previously published algorithms by Arruda, Boersma, and Chiang. This multicenter study included patients who had undergone successful ablation of one accessory pathway. Analysis of resting 12-lead electrocardiograms was carried out. An aggregated prediction score was constructed on the basis of algorithm agreement, and a structured workflow approach was proposed. The total population was 120 patients (mean age, 12.7 [± 3.6] years). The algorithm by Boersma had the highest accuracy (71.7%). The inter-rater agreement among the 3 reference algorithms, according to left-sided accessory pathway (AP) identification, was good between Boersma and Chiang (κ = 0.611; 95% confidence interval [CI], 0.468-0.753) but moderate between Arruda and Chiang and between Arruda and Boersma (κ = 0.566; 95% CI, 0.419-0.713 and κ = 0.582; 95% CI, 0.438-0.727, respectively). Regarding locations at risk of atrioventricular (AV) block, agreement was fair between Arruda and Chiang and between Boersma and Chiang (κ = 0.358; 95% CI, 0.195-0.520 and κ = 0.307; 95% CI, 0.192-0.422, respectively) but moderate between Arruda and Boersma (κ = 0.45; 95% CI, 0.304-0.597). On applying a first-step diagnostic evaluation, when concordance was achieved, we were able to correctly identify left-sided or non-left-sided ablation sites in 96.4% (n = 80) of cases. When concordance was achieved, correct prediction of risk/no risk of AV block was achieved in 92.2% (n = 59) of cases. An aggregated prediction score based on 3 reference algorithms proved able to predict an accessory pathway location very precisely and could be used to plan safely invasive procedures.

Protocolo diagnóstico de las bradiarritmiasen urgencias

The slowing of electrical stimulus conduction can occur in any area of the specific conduction system. Any heart rhythm with a heart rate (HR) below 60 bpm is called bradyarrhythmia or bradycardia. The anatomical and electrical underpinnings can be located in the sinus node, atrioventricular node, or His-Purkinje system. The spectrum of clinical presentation is very broad, ranging from an electrocardiographic finding in asymptomatic patients to potentially fatal situations that require quick, decisive action. In the diagnostic approach and risk stratification for bradyarrhythmias in the emergency department, it is essential to verify the hemodynamic condition in the initial evaluation, given that the necessity and urgency of therapeutic action will be determined based on it. Protocolizing the clinical diagnosis and establishing an electrocardiographic algorithm is key in patients with suspected bradyarrhythmia.

Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis

BackgroundDiagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.ObjectiveThe aim was to perform a thorough electrophysiological characterisation...

A machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis

Abstract Background The diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic value is limited. Purpose The aim of this study was to perform a comprehensive electrophysiological characterization in CA patients and to develop a robust, easy-to-use diagnostic tool. Methods First, we applied electrocardiographic imaging (ECGI) to generate detailed electroanatomical maps in CA patients and controls. Then, a machine learning approach was used to generate a surface ECG-based diagnostic algorithm from the complex dataset. Results Areas of low voltage were localized in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualized in the right ventricle. Potential maps showed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1 to V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in inferior leads II, III, aVF. Ten blinded cardiologists were then asked to identify CA patients by analyzing 12-lead ECGs before and after training for the defined ECG patterns. Training resulted in significant improvements in the detection rate of CA with an AUC of 0.69 before and 0.97 after training (Figure). Conclusion Using a machine learning approach, a robust ECG-based tool was developed to detect CA from detailed electroanatomical mapping of CA patients. The developed tool proved to be a simple and reliable diagnostic tool to suspect CA without the aid of advanced imaging modalities. Funding Acknowledgement Type of funding sources: None.

Prospective evaluation of a hybrid clinical and electrocardiographic algorithm to predict the origin of outflow tract PVCs. A prospective multicenter study

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Beca de la Asociación del Ritmo Cardiaco para Formación en Investigación Post-Residencia en centros españoles. Background To predict the premature ventricular complex (PVC) site of origin (SOO) before the ablation procedure has important implications. 12 lead ECG information as well clinical characteristics are related with the PVC-SOO, but there is no prospective data validation. Purpose This prospective multicenter study sought to analyze the applicability and accuracy of a simple hybrid algorithm that includes electrocardiographic and clinical information in a consecutive patient population with outflow tract (OT) PVCs undergoing catheter ablation. Methods Consecutive patients with frequent OT-PVCs and indication for catheter ablation were prospectively included in 4 centers. The vascular access (femoral vein vs femoral artery) and the first outflow tract to be mapped were decided based on a two-steps hybrid algorithm (see Figure 1) including ECG information [R/S PVC precordial transition (PT): early PT if it occurs before V3; late PT if beyond V3) and clinical information [the following variables compute 1 point in a clinical score: age (>59 y.o); presence of hypertension; and gender (man)]. Results 115 consecutive patients [42 (37%) man, 52 + 15 y.o] were included. Mean PVC burden at baseline was 20 ± 13% and mean LVEF was 59 ± 8%. Mean procedure time was 55 ± 26 min. Complete acute abolition of the PVC was achieved in 110 (96%) patients. 84 (73%) patients had the PVC’s SOO in the right ventricle OT (RVOT) whereas 31 (27%) in the left ventricular OT (LVOT). Compared with patients with a RVOT-SOO, those with a LVOT-SOO were more frequently man [18 (58%) vs 24 (29%), p = 0.004], more frequently suffered from hypertension [18 (58%) vs 15 (18%), p < 0.001) and had an early R/S PVC transition [17 (55%) early PT, 3 (10%) late PT) in the case of LVOT-SOO vs 4 (5%) early PT, 55 (65%) late PT in the case of RVOT-SOO, p < 0.001]. The hybrid algorithm accurately anticipated the PVC’s SOO in 101 (88%) patients. A reduction of at least 80% of PVC burden was achieved by 90% of patients at 6 months. Conclusions The hybrid algorithm, prospectively evaluated in an international multicenter study, has proved to permit to accurately anticipate the PVC’s SOO (right vs left) in a consultive population of patients with OT PVCs. Abstract Figure 1

Head-to-head comparison of proprietary PPG and single-lead ECG algorithms for atrial fibrillation detection

Abstract Funding Acknowledgements Type of funding sources: None. Background Population based screening for atrial fibrillation (AF) has been suggested to reduce stroke. Photoplethysmography (PPG) deriving smartphone apps and single-lead electrocardiography (ECG) tools are attractive devices for screening due to their low cost, convenience, and accessibility. Automated algorithm analysis can serve as pre-screening or remote monitoring for AF, while confirmation on an ECG trace >30s is required to establish the diagnosis. This work directly compares the performance of proprietary algorithms on PPG vs single-lead ECG for the detection of AF. Purpose To evaluate and compare the diagnostic performance of a PPG-deriving smartphone app and a single-lead ECG-deriving handheld device for AF detection. Methods Patients were recruited from the cardiology ward. After obtaining written informed consent, demographic and medical information were collected. Patients were instructed to perform one measurement using a pulse-deriving smartphone app and one via a single-lead ECG handheld device. A 12-lead electrocardiogram (ECG) was collected and interpreted by a cardiologist as gold standard. Patients with atrial flutter were excluded, with additional exclusions for insufficient quality measurements and unsuccessful measurements resulting due to technical errors. Unclassified single-lead ECG measurements were handled as test-negative. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was performed to compare the sensitivity and specificity of the proprietary PPG and single-lead ECG AF detection algorithms. Results The median age in the study population (n = 300) was 70 years (interquartile range: 51-78), 56.3% were men, and the median CHA2DS2-VASc was 3 (interquartile range: 1-4) with an AF-prevalence of 32.3%. PPG signal and single‑lead ECG quality was sufficient in 272/300 (91.0%) and 278/298 (93.3%) participants, respectively. After excluding atrial flutter patients (n = 25) and insufficient quality measurements, the sensitivity and specificity were 97.6% (95% CI 93.8 to 99.3) and 94.1% (95% CI 86.8 to 98.1) for the PPG signal versus 95.7% (95% CI 91.4 to 98.3) and 91.1% (95% CI 83.2 to 96.1) for the single‑lead ECG signal, respectively. Results demonstrated a 96.4% (95% CI 93.2 to 98.3) accuracy for PPG and 94.1% (95% CI 90.4 to 96.6) for single-lead ECG. No significant differences in sensitivity (P = 0.453) or specificity (P = 0.219) between the proprietary PPG and single-lead ECG algorithms were found. Conclusion This study demonstrated equivalent diagnostic performance of PPG and single-lead ECG proprietary AF detection algorithms in smartphone apps.

A Machine Learning-Derived Electrocardiographic Algorithm for the Detection of Cardiac Amyloidosis

Background: Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. Objectives: The aim was to perform a thorough electrophysiological characterization in CA patients and derive an easy-to-use tool for diagnosis. Methods: We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in CA patients and controls. A machine learning approach was then utilized to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. Findings: Areas of low-voltage were localized in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualized on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1 to V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III, aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify CA patients by analyzing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA with an AUC of 0.69 before and 0.97 after training. Interpretation: Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of CA patients. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.. Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interests: None to declare. Ethics Approval Statement: The study was part of a registry approved by the local ethics committee (EK #796/2010) and conducted according to good clinical practice as outlined in the declaration of Helsinki. All patients gave written informed consent.

Validation of a Simple Electrocardiographic Algorithm for Detection of Ventricular Tachycardia.

ResumoFundamentoO diagnóstico diferencial de taquicardia de QRS largo, entre taquicardia ventricular (TV) ou taquicardia supraventricular com condução aberrante (TSV-A) é algumas vezes difícil de ser feito na sala de emergência.ObjetivoAvaliar a acurácia de um algoritmo novo e simples para a detecção de TV no eletrocardiograma (ECG) em pacientes com taquicardia de QRS largo.MétodosECGs de 12 derivações para detecção de taquicardia de QRS largo foram obtidos prospectivamente de 120 pacientes durante estudo eletrofisiológico. Seis médicos com diferentes experiências analisaram os ECGs, e fizeram o diagnóstico com base no algoritmo D12V16, que envolve a análise da polaridade predominante do complexo QRS nas derivações I, II, V1 e V6. O diagnóstico foi comparado com os obtidos pelo algoritmo tradicional de Brugada e pelo estudo eletrofisiológico, o qual é considerado padrão ouro. Adotou-se um nível de significância de 5% (p<0,05) nas análises estatísticas.ResultadosDe acordo com o estudo eletrofisiológico, 82 ECGs eram de TV e 38 de TSV-A. Doenças cardíacas estruturais estavam presentes em 71 (86,6%) dos pacientes com TV e em oito (21,1%) com TSV-A. O algoritmo de Brugada teve uma maior sensibilidade global (87,2%), enquanto o algoritmo D12V16 apresentou maior especificidade global (85,1%) para TV. Tanto o algoritmo D12V16 como o de Brugada apresentou um alto valor preditivo positivo (90,9% vs. 85,8%, respectivamente) e acurácia similar (73,8% vs. 81,4%, respectivamente) para o diagnóstico de TV. Nos avaliadores experientes, a acurácia foi maior utilizando o algoritmo de Brugada que o algoritmo D12V16, mas a acurácia dos dois algoritmos foi similar segundo os avaliadores menos experientes.ConclusãoO algoritmo simplificado pode ser um método útil para reconhecer TV no ECG, principalmente para médicos menos experientes. (Arq Bras Cardiol. 2021; [online].ahead print, PP.0-0)

Telemedicine-based early rule out electrocardiography algorithm: Hydroxychloroquine in COVID-19 patients

Telemedicine-based early rule out electrocardiography algorithm: Hydroxychloroquine in COVID-19 patients