Age and Sex Specific Prevalence of Clinical and Screen-Detected Atrial Fibrillation in Hospitalized Patients

Roten, Laurent; Goulouti, Eleni; Lam, Anna; Elchinova, Elena; Nozica, Nikolas; Spirito, Alessandro; Wittmer, Severin; Branca, Mattia; Servatius, Helge; Noti, Fabian; Seiler, Jens; Baldinger, Samuel H.; Haeberlin, Andreas; Marchi, Stefano de; Asatryan, Babken; Rodondi, Nicolas; Donzé, Jacques; Aujesky, Drahomir; Tanner, Hildegard; Reichlin, Tobias; Jüni, Peter

doi:10.3390/jcm10214871

Cited by 8 publications

(7 citation statements)

References 26 publications

(33 reference statements)

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“…The proposed methods and results presented in the paper might contribute to developing practical AF detection solutions in miniature wearable, bio-patches, implantable devices, and hand-held single- or multi-lead ECG devices [ 3 , 67 , 68 , 69 , 70 ].…”

Section: Discussionmentioning

confidence: 99%

“…For an ECG recorded with good quality, HRV parameters combined with ML methods are valuable for their potential clinical use. The number of devices dedicated to long-term ECG monitoring increases as they are vital in transient AF detection [ 3 , 68 , 69 , 70 ]. In these solutions, computational and energy efficiency are of crucial importance.…”

Section: Discussionmentioning

confidence: 99%

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Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Buś

Jędrzejewski

Guzik

2022

JCM

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Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation—standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Buś

Jędrzejewski

Guzik

2022

JCM

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“…Left ventricular hypertrophy was defined as a left ventricular myocardial mass index above normal range according to the published criteria (≥102 g/m 2 in males and ≥88 g/m 2 in females) [ 19 ]. The STAR-FIB cohort study was an atrial fibrillation screening study that included 795 patients without atrial fibrillation, aged 65–84 years [ 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

Differences in Atrial Remodeling in Hypertrophic Cardiomyopathy Compared to Hypertensive Heart Disease and Athletes’ Hearts

Servatius

Raab

Asatryan

et al. 2022

JCM

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Background: Hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD) and athletes’ heart share an increased prevalence of atrial fibrillation. Atrial cardiomyopathy in these patients may have different characteristics and help to distinguish these conditions. Methods: In this single-center study, we prospectively collected and analyzed electrocardiographic (12-lead ECG, signal-averaged ECG (SAECG), 24 h Holter ECG) and echocardiographic data in patients with HCM and HHD and in endurance athletes. Patients with atrial fibrillation were excluded. Results: We compared data of 27 patients with HCM (70% males, mean age 50 ± 14 years), 324 patients with HHD (52% males, mean age 75 ± 5.5 years), and 215 endurance athletes (72% males, mean age 42 ± 7.5 years). HCM patients had significantly longer filtered P-wave duration (153 ± 26 ms) and PR interval (191 ± 48 ms) compared to HHD patients (144 ± 16 ms, p = 0.012 and 178 ± 31, p = 0.034, respectively) and athletes (134 ± 14 ms, p = 0.001 and 165 ± 26 ms, both p < 0.001, respectively). HCM patients had a mean of 4.9 ± 16 premature atrial complexes per hour. Premature atrial complexes per hour were significantly more frequent in HHD patients (27 ± 86, p < 0.001), but not in athletes (2.7 ± 23, p = 0.639). Left atrial volume index (LAVI) was 43 ± 14 mL/m2 in HCM patients and significantly larger than age- and sex-corrected LAVI in HHD patients 30 ± 10 mL/m2; p < 0.001) and athletes (31 ± 9.5 mL/m2; p < 0.001). A borderline interventricular septum thickness ≥13 mm and ≤15 mm was found in 114 (35%) HHD patients, 12 (6%) athletes and 3 (11%) HCM patients. Conclusion: Structural and electrical atrial remodeling is more advanced in HCM patients compared to HHD patients and athletes.

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“…AF is often asymptomatic, especially in men and older people [2], and its complications, e.g., ischaemic stroke, may be the first presentation of this arrhythmia. The incidence of AF increases with age and is usually associated with many diseases or risk factors like hypertension, obesity, smoking, coronary artery disease, valvular heart disease, lung disease and hyperthyroidism [3]. However, AF is not rare in otherwise healthy people, e.g., those involved in long-term endurance sports [4].…”

Section: Introductionmentioning

confidence: 99%

pRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection

Biczuk,

Buś,

Żurek

et al. 2024

Entropy

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Background: Early detection of atrial fibrillation (AF) is essential to prevent stroke and other cardiac and embolic complications. We compared the diagnostic properties for AF detection of the percentage of successive RR interval differences greater than or equal to 30 ms or 3.25% of the previous RR interval (pRR30 and pRR3.25%, respectively), and asymmetric entropy descriptors of RR intervals. Previously, both pRR30 and pRR3.25% outperformed many other heart rate variability (HRV) parameters in distinguishing AF from sinus rhythm (SR) in 60 s electrocardiograms (ECGs). Methods: The 60 s segments with RR intervals were extracted from the publicly available Physionet Long-Term Atrial Fibrillation Database (84 recording, 24 h Holter ECG). There were 31,753 60 s segments of AF and 32,073 60 s segments of SR. The diagnostic properties of all parameters were analysed with receiver operator curve analysis, a confusion matrix and logistic regression. The best model with pRR30, pRR3.25% and total entropic features (H) had the largest area under the curve (AUC)—0.98 compared to 0.959 for pRR30—and 0.972 for pRR3.25%. However, the differences in AUC between pRR30 and pRR3.25% alone and the combined model were negligible from a practical point of view. Moreover, combining pRR30 and pRR3.25% with H significantly increased the number of false-negative cases by more than threefold. Conclusions: Asymmetric entropy has some potential in differentiating AF from SR in the 60 s RR interval time series, but the addition of these parameters does not seem to make a relevant difference compared to pRR30 and especially pRR3.25%.

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Age and Sex Specific Prevalence of Clinical and Screen-Detected Atrial Fibrillation in Hospitalized Patients

Cited by 8 publications

References 26 publications

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Differences in Atrial Remodeling in Hypertrophic Cardiomyopathy Compared to Hypertensive Heart Disease and Athletes’ Hearts

pRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection

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