Background:
Atrial fibrillation (AF) is the most common cardiac arrythmia. In AF, the normal electrical impulses that are generated by the sinoatrial node are overwhelmed by disorganized electrical impulses that originate in the atria and pulmonary veins, leading to conduction of irregular impulses to the ventricles that generate the heartbeat. The result is an irregular heartbeat which may occur in episodes lasting from minutes to weeks, or it could occur all the time for years. The natural tendency of AF is to become a chronic condition. Chronic AF leads to a small increase in the risk of death(1,2). In the intensive care setting, AF can be a life threatening arrythmia; the higher frequency and less efficient cardiac function associated with AF can result in abrupt hemodynamic collapse in critically ill patients. Thus it is essential to detect AF without delay so appropriate therapy can be given to restore sinus rhythm.
AF can be detected by ECG, where it is indicated by the absence of P-waves with disorganized electrical activity in their place and an irregular RR interval. Automatic detection is feasable, with a high specificity, however limited sensitivity. Approaches for automated algorithms typically evaluate the regularity in RR interval, and may label other conditions with an irregular heartrate as atrial fibrillation. P-wave detection is a different, more complex approach, and is limited by electrical noise or when T and P wave overlap occurs.
Aim:
To collect recordings of patients with episodes of atrial fibrillation while admitted to the Intensive Cardiac Care Unit
To evaluate the sensitivity and specificity of existing AF detection algorithms
To improve the existing AF detection algorithm and propose possibilities for AF prediction.
Hypothesis:
AF detection algorithms using both P wave and RR interval analysis have a higher accuracy then AF detection algorithms using only one modality.
Methods:
Prospective, observational study
Patients selection:
40 Patients admitted to the Intensive Cardiac Care Unit (ICCU), with AF as determined from resting ECG
80 Patients admitted to the ICCU without AF
Informed consent
Data collection:
12-lead ECG signal collected using software (from Draëger?) with a sampling frequency of ?Hz
Patient data from chart
Baseline variables:
demographic: age, gender, height, weight, smoking, alcohol consumption
clinical: hx AF, hx Cardiovascular disease (myocardial infarction, percutaneous intervention, acute coronary syndrome, CABG), hx (CVA/TIA), hx stable angina, pacemaker/ICD (type), valvular disease (type), pulmonary disease, malignancy, hypertension, diabetes
medication at admission: list
admission: diagnosis, apache/saps, other relevant information
Predictor variable:
type of algorithm used
Outcome variable:
% of recordings correctly identified as AF or SR
Workplan:
November (supervision Jonathan, Teus, Stefan)
reading, background information
set up recording equipment, determine algorithms
finalize protocol
December/January
data collection (supervision Teus)
January/February
analysis for results (supervision Stefan, Jonathan, Teus)
March
finalize report (supervision Jonathan, Teus, Stefan)
References:
1. Benjamin EJ, Wolf PA, D'Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation. 1998 Sep 8;98(10):946-952.
2. Wattigney WA, Mensah GA, Croft JB. Increased atrial fibrillation mortality: United States, 1980-1998. Am. J. Epidemiol. 2002 May 1;155(9):819-826.