The relationship between atrial fibrillation and coronary artery disease: Understanding common denominators

Mekhael, Mario; Marrouche, Nassir F.; Hajjar, Abdel Hadi El; Donnellan, Eoin

doi:10.1016/j.tcm.2022.09.006

Cited by 18 publications

(13 citation statements)

References 86 publications

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“…3D mapping, ablation, arrhythmia, atrial flutter, focal atrial tachycardia, HD catheter factors. [1][2][3] The arrhythmia may be sustained or incessant. Dynamic forms with recurrent interruptions and reinitiating may be frequent.…”

Section: Introductionmentioning

confidence: 99%

“…There are several types of arrhythmias with various etiologies, risk factors, treatments, and prognoses 1 . Many of these arrhythmias such as atrial fibrillation and atrial flutter (AFL) which are part of the most prevalent ones have shown their correlation with aging, ischemic heart diseases, hypertension, diabetes, and several other factors 1–3 . The arrhythmia may be sustained or incessant.…”

Section: Introductionmentioning

confidence: 99%

“…4,6 On electrocardiogram, it is characterized by discrete P-waves and isoelectric activity between P waves. It is usually the result of one or a combination of these conditions: (1) Reentry (it is seen when activated myocytes in propagation reenter already recovered myocytes), ( 2) automatic (abnormal initiation of an impulse in one or several myocytes), or (3). triggered arrhythmia (caused by oscillations of membrane potential, early or delayed after-depolarizations).…”

Section: Introductionmentioning

confidence: 99%

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Navigating the fine line between focal atrial tachycardia and atrial flutter?

Rahimpour,

Hemmati,

Anafje

et al. 2024

Clinical Case Reports

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Key Clinical MessageFocal atrial tachycardia (FAT) is an organized atrial rhythm >100 beats per minute initiated from a discrete origin and spreading over both atria in a centrifugal pattern. The arrhythmia may be sustained or incessant. Dynamic forms with recurrent interruptions and reinitiating may be frequent. In this report, we present a 36‐year‐old man who came to the emergency room complaining of palpitation and shortness of breath. All laboratory evaluations were normal. With an initial electrocardiogram (ECG) the patient was admitted with the initial diagnosis of atrial flutter. Finally, after the electrophysiologist's examination, with the diagnosis of FAT, ablation was successfully performed. Atrial tachycardia (AT), excluding atrial fibrillation (AF) and cavotricuspid isthmus‐dependent atrial flutter (AFL), account for 10% of supraventricular tachycardia referred for ablation procedures. More than 70% of these cases are focal and occur in patients with no records of cardiac surgery or ablation of AF. FAT originating from the right pulmonary veins (PV) can be challenging to differentiate from atrial flutter due to their proximity and overlapping symptoms. The right PV is close to the right atrium, and the abnormal electrical activity in FAT may mimic the organized circuit found in atrial flutter. Distinguishing between FAT and atrial flutter is crucial for choosing the best therapeutic option. This can be done most of the time by focusing on the differences in the pattern of their P and QRS waves, R‐R wave intervals, and also their baseline changes on ECG, as well as their cycle duration, response to adenosine and risk factors of the patient.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Navigating the fine line between focal atrial tachycardia and atrial flutter?

Rahimpour,

Hemmati,

Anafje

et al. 2024

Clinical Case Reports

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“…These studies have been combined to develop joint risk scores, factoring in both physiological covariates (blood pressure, cholesterol) as well as demographic covariates (age, race, gender) [5,8,9,21]. Despite the strong literature studying the risk factors for CAD, most studies focus upon hypothesis testing or epidemiology focusing upon specific risk factors of interest [22][23][24]. While CAD is recognized as being of "multifactorial" cause, little is known regarding the relative predictive power of different risk factors (lifestyle vs genetic vs chronic disease comorbidities).…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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“…However, previous MR studies have mostly focused on the unidirectional causal relationship between the risk factors and the diseases. The bidirectional causal relationship between MI and AF has not been thoroughly investigated using MR (15,16).…”

Section: Introductionmentioning

confidence: 99%

Causal association between myocardial infarction and atrial fibrillation: A bidirectional Mendelian randomization study

Rao

Wang

2023

Preprint

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Background At present, many studies have shown a significant correlation between myocardial infarction (MI) and atrial fibrillation (AF), but few focus on the bidirectional causal relationship between MI and AF. Therefore, this Mendelian randomization (MR) study was designed to examine the bidirectional causality between MI and AF.Methods We used the publicly available summary statistical dataset of MI from genome-wide analysis studies (GWAS; ebi-a-GCST011364; case = 14,825, control = 2,680). The summary statistical dataset of AF was obtained from a European population GWAS (finn-b-I9_AF_REIMB; case = 10,516, control = 116,926). A two-sample bidirectional MR analysis was performed using analysis methods including inverse-variance weighted (IVW), MR-Egger, and weighted median.Results In site-specific MI analyses, we screened 30 single nucleotide polymorphisms (SNPs) from GWAS as instrumental variables (IVs). Causal association between MI and AF can be supported by IVW (beta = 0.349, SE = 0.057, P < 0.001), MR-Egger (beta = 0.398, SE = 0.131, P = 0.005), and weighted median (beta = 0.352, SE = 0.068, P < 0.001). In the reverse MR analyses, we screened 20 SNPs as IVs and the casual effect of AF on MI was observed by IVW (beta = 0.047, SE = 0.022, P = 0.033).Conclusions Our MR study results showed a good causal effect of MI on AF. The MR analysis also found a causal effect of AF on MI.

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The relationship between atrial fibrillation and coronary artery disease: Understanding common denominators

Cited by 18 publications

References 86 publications

Navigating the fine line between focal atrial tachycardia and atrial flutter?

Navigating the fine line between focal atrial tachycardia and atrial flutter?

Use of machine learning to identify risk factors for coronary artery disease

Causal association between myocardial infarction and atrial fibrillation: A bidirectional Mendelian randomization study

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