Automated Atrial Fibrillation Detection

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.