Objective: Atrial cardiomyopathy (ACM) is associated with development of AF, left atrial (LA) thrombogenesis, and stroke. Diagnosis of ACM is feasible using both echocardiographic LA strain imaging and measurement of the amplified p-wave duration (APWD) in digital 12-lead-ECG. We sought to determine the thresholds of LA global longitudinal strain (LA-GLS) and APWD that identify patients with AF at risk for LA appendage (LAA) thrombogenesis. Methods: One hundred and twenty-eight patients with a history of AF were included. Left atrial appendage maximal flow velocity (LAA-Vel, in TEE), LA-GLS (TTE), and APWD (digital 12-lead-ECG) were measured in all patients. ROC analysis was performed for each method to determine the thresholds for LA-GLS and the APWD, enabling diagnosis of patients with LAA-thrombus. Results: Significant differences in LA-GLS were found during both rhythms (SR and AF) between the thrombus group and control group: LA-GLS in SR: 14.3 ± 7.4% vs. 24.6 ± 9.0%, p < 0.001 and in AF: 11.4 ± 4.2% vs. 16.1 ± 5.0%, p = 0.045. ROC analysis revealed a threshold of 17.45% for the entire cohort (AUC 0.82, sensitivity: 84.6%, specificity: 63.6%, Negative Predictive Value (NPV): 94.3%) with additional rhythm-specific thresholds: 19.1% in SR and 13.9% in AF, and a threshold of 165 ms for APWD (AUC 0.90, sensitivity: 88.5%, specificity: 75.5%, NPV: 96.2%) as optimal discriminators of LAA-thrombus. Moreover, both LA-GLS and APWD correlated well with the established contractile LA-parameter LAA-Vel in TEE (r = 0.39, p < 0.001 and r = −0.39, p < 0.001, respectively). Conclusion: LA-GLS and APWD are valuable diagnostic predictors of left atrial thrombogenesis in patients with AF.

Background: Atrial cardiomyopathy is associated with increased risk for future atrial fibrillation (AF) and ischemic stroke. Several ECG-derived parameters have been reported to assess the risk for future ischemic stroke. Purpose: To predict risk of future ischemic stroke by comparison of the novel parameter duration of the amplified digital sinus-p-wave (APWD; 80mm/mV; 175mm/sec) vs previously described p-wave indices and clinical risk factors (cerebrovascular disease (CVD), CHADS-VASc-score). Methods: P-wave-derived parameters were measured on digital 12-lead-sinus-ECGs (recorded +/- 6 months from the clinical stroke event) using the software "ECG-Precision-Analysis V-1-2023". and clinical CHA2DS2-VASc Score was assessed in 80 patients with ischemic stroke (no history of AF, no oral anticoagulation (OAC)) and compared to 80 control patients (without OAC, AF or ischemic stroke within the next five years). Results: Patients with ischemic stroke vs control patients presented a higher CHA2DS2-VASc Score (3.7 vs 3.2, p=0.04), had more frequently heart failure (25% vs 13%, p=0.04) and cerebrovascular atherosclerotic disease with stenosis >40% ((22.5% vs 10%, p=0.032). P-wave indices were as follows in patients who developed ischemic stroke within the next 12 months versus patients without stroke within a follow-up period of five years, respectively: APWD (144 vs 128ms, p<0.0001), non-amplified SPWD (118 vs 116ms, p=0.081), A-PTF-V1 (-4660 vs -3860µV*ms, p=0.005), durations and amplitudes of terminal-p-wave in V1 (A-Duration-V1 (83 vs 79ms, p=0.119), A-Amplitude-V1 (-50µV vs -50µV, p=0.028), and advanced interatrial block (aIAB in 25% vs 3.8% of patients; p140ms) was the best predictor for future ischemic stroke (accuracy: 74%, sensitivity and specificity: 61% and 86%, respectively; PPV: 82%; area under ROC: 0.776). Sensitivity and specificity of the SPWD, aPTFV1 and aIAB were significantly lower (SPWD>120ms: sensitivity 35% and specificity 78%, PPV: 61%; aPTFV1 ≤-5000µV*ms, accuracy: 61%, sensitivity 46%% and specificity 75%, PPV: 65%; aIAB: accuracy: 61%, sensitivity 25% and specificity 96%, PPV: 87%). Conclusions: Among the currently identified ECG markers of atrial cardiomyopathy, the novel APWD and advanced interatrial block have the best predictive performances for diagnosis of persons at risk for ischemic stroke.Comparison between ECG ParametersPredictive Accuracy of Parameters

Background: Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important. Purpose: To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure. Methods: 598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software "ECG-Precision-Analysis V-1-2023" to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using C-statistics for each endpoint. Results: Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (C-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean C-statistics 0.75. Conclusions: In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predominantly predicted by maximum exercise capacity but not APWD.Kaplan-Meier curves for MACCE/MortalitytimeROC curves for MACCE/Mortality