The prevalence and association of major ECG abnormalities  with clinical characteristics and the outcomes of real-life heart  failure patients — Heart Failure Registries of the Eu ropean  Society of Cardiology

Tymińska, Agata; Ozierański, Krzysztof; Balsam, Paweł; Kapłon‐Cieślicka, Agnieszka; Maciejewski, Cezary; Marchel, Michał; Crespo‐Leiro, María G.; Maggioni, Aldo P.; Dróżdż, Jarosław; Filipiak‬, Krzysztof J.; Opolski, Grzegorz; Grabowski, Marcin

doi:10.33963/kp.a2021.0053

“…Literature shows that ECG features provide information on the development of HF. 25 , 26 , 27 Results from the variable importance analysis ( Figure 1 ) are consistent with this, showing that changes in the T-wave morphology can detect risk of cardiac arrhythmias 28 , 29 and abnormal QT dispersion can identify people at risk of HF. 30 Although the utilization of these ECG characteristics can be beneficial, they are complex to assess, require full extraction of rhythmic waves, and can run the risk of data gaps when these data are unavailable from different institutes.…”

Section: Discussionsupporting

confidence: 71%

A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

Butler,

Karabayir,

Kitzman

et al. 2023

Cardiovascular Digital Health Journal

View full text Add to dashboard Cite

“…In a registry-based study [ 30 ] conducted in 136 and 211 European cardiology centers, major ECG abnormalities were present in the majority of patients with heart failure. Out of 1460 patients with heart failure, 1222 had major ECG abnormalities with various patterns across the heart failure types [ 30 ], and the Euroheart Failure survey showed that ECG abnormalities were present in 98% of patients with heart failure [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…In a registry-based study [ 30 ] conducted in 136 and 211 European cardiology centers, major ECG abnormalities were present in the majority of patients with heart failure. Out of 1460 patients with heart failure, 1222 had major ECG abnormalities with various patterns across the heart failure types [ 30 ], and the Euroheart Failure survey showed that ECG abnormalities were present in 98% of patients with heart failure [ 31 ]. Older age and being male were associated with an increased risk of ECG abnormalities, as were history of advanced heart valve disease, chronic kidney disease, signs of heart failure decompensation, and use of diuretics and anticoagulants [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation

Campo¹,

Elie²,

Gallard³

et al. 2022

View full text Add to dashboard Cite

Background Atrial fibrillation affects approximately 4% of the world’s population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. Objective We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. Methods Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). Results A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. Conclusions The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch’s single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. Trial Registration ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386

show abstract

“…HR can also contribute to tachyarrhythmic cardiomyopathy and HF decompensation. In the previous analysis from the ESC-HF registries, among all tested major electrocardiography (ECG) abnormalities in patients with HF (regardless of the type of HF), only tachycardia (> 100 bpm) remained an independent predictor of all-cause death in multivariable analysis [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Heart rate control and its predictors in patients with heart failure and sinus rhythm. Data from the European Society of Cardiology Long-Term Registry

Tymińska

¹

,

Ozierański

²

,

Wawrzacz

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

The prevalence and association of major ECG abnormalities with clinical characteristics and the outcomes of real-life heart failure patients — Heart Failure Registries of the Eu ropean Society of Cardiology

Cited by 7 publications

References 13 publications

A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation

Heart rate control and its predictors in patients with heart failure and sinus rhythm. Data from the European Society of Cardiology Long-Term Registry

Contact Info

Product

Resources

About