Relationship of indices of inflammation and thrombogenesis to arrhythmia burden in paroxysmal atrial fibrillation.
BACKGROUND
Atrial fibrillation (AF) is associated with a high risk of stroke. The contribution of arrhythmia to events is clear in sustained forms of AF, but in paroxysmal AF, presently available data have yet to identify what proportion of time spent in AF (ie, arrhythmia burden [AFB]) is of clinical relevance. We aimed to assess this relationship using surrogate blood markers for the hypercoagulable state associated with AF.


METHODS
One hundred twenty-one consecutive outpatients (mean age 74.7 +/- 7.8 years; 73 [60.3%] men) with pacemakers capable of arrhythmia detection were recruited. AFB was assessed over a 1-month period and classified as AFB = 0%, 0.1% to 10%, 10.1% to 50%, or > 50%.


RESULTS
Baseline characteristics and comorbidities were comparable between groups. There were no significant differences in levels of soluble E-selectin (sE-selectin), von Willebrand factor (vWf), high-sensitivity C-reactive protein, interleukin-6, soluble P-selectin (sP-selectin), or tissue factor (TF) across the four patient groups. Levels of plasma brain natriuretic peptide (BNP) were approximately twofold greater in the group with the highest AFB (P < .001). Following a stepwise multiple linear regression analysis, age was a significant predictor of vWf (P = .010), sP-selectin (P = .042), and BNP (P = .012). Left ventricular fractional shortening was predictive of BNP (P = .001) and sE-selectin (P = .012). Anticoagulation was a predictor of vWf levels (P = .005), and hypertension was predictive of TF (P < .001).


CONCLUSIONS
Given no appreciable difference in levels of prothrombotic markers in relation to AFB in this study, it is plausible that these abnormalities do, in fact, relate to underlying risk factors, and that such patients should be anticoagulated if risk factors dictate. Thus, AFB per se should probably not influence the decision to anticoagulate, but rather the presence of AF combined with clinical risk scoring should remain the predominant tool for stroke risk assessment.
Watson
Timothy
T
University of Birmingham Centre for Cardiovascular Sciences, City Hospital, Birmingham B18 7QH, England.
Arya
Anita
A
Sulke
Neil
N
Lip
Gregory Y H
GY
D000368:Aged; D000369:Aged, 80 and over; D001145:Arrhythmias, Cardiac / Q000150:complications* / Q000628:therapy; D001281:Atrial Fibrillation / Q000150:complications* / Q000628:therapy; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D019040:E-Selectin / Q000097:blood; D005260:Female; D006801:Humans; D007249:Inflammation / Q000097:blood*; D015850:Interleukin-6 / Q000097:blood; D016014:Linear Models; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000097:blood; D019007:P-Selectin / Q000097:blood; D010138:Pacemaker, Artificial; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology*; D013927:Thrombosis / Q000097:blood*; D014841:von Willebrand Factor / Q000378:metabolism
D015415:Biomarkers; D019040:E-Selectin; D015850:Interleukin-6; D019007:P-Selectin; D014841:von Willebrand Factor; D020097:Natriuretic Peptide, Brain; D002097:C-Reactive Protein