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Refinement of detecting atrial fibrillation in stroke patients: results from the TRACK-AF Study
F Reinke1, M Bettin1, L S Ross2
1Department of Cardiovascular Medicine, Division of Electrophysiology, University Hospital of Muenster, Muenster, Germany.
Insights
Detecting occult atrial fibrillation (AF) in stroke patients is vital. Combining implantable cardiac monitors (ICM) with stroke risk analysis (SRA) software improves AF detection rates for better secondary prevention.
Area of Science:
- Cardiology
- Neurology
- Medical Technology
Background:
- Occult atrial fibrillation (AF) detection is critical for secondary stroke prevention.
- Current diagnostic methods for AF in stroke patients require optimization.
- Identifying patients at high risk for AF is essential for timely intervention.
Purpose of the Study:
- To compare the AF detection rate of implantable cardiac monitors (ICM) with stroke risk analysis (SRA) software.
- To evaluate the predictive accuracy of SRA for incident AF.
- To propose an optimized AF detection algorithm by combining ICM and SRA.
Main Methods:
- A prospective monocentric study involving 105 patients with cryptogenic stroke.
- Patients underwent 20 months of monitoring with ICM and SRA during hospitalization.
- Comparison of AF detection rates and predictive accuracy between ICM and SRA.
Main Results:
- ICM detected occult AF in 18% of patients (n=19).
- SRA predicted an increased risk for AF in 62% of patients (n=65).
- SRA demonstrated high sensitivity (95%) and negative predictive value (96%) for AF prediction.
Conclusions:
- Combining SRA and ICM is a promising strategy for detecting occult AF.
- SRA is a reliable tool for predicting incident AF, with a high negative predictive value.
- SRA can serve as a cost-effective pre-selection tool for identifying patients who may benefit from ICM monitoring.
Background And Purpose:
Detection of occult atrial fibrillation (AF) is crucial for optimal secondary prevention in stroke patients. The AF detection rate was determined by implantable cardiac monitor (ICM) and compared to the prediction rate of the probability of incident AF by software based analysis of a continuously monitored electrocardiogram at follow-up (stroke risk analysis, SRA); an optimized AF detection algorithm is proposed by combining both tools.
Methods:
In a monocentric prospective study 105 out of 389 patients with cryptogenic stroke despite extensive diagnostic workup were investigated with two additional cardiac monitoring tools: (a) 20 months' monitoring by ICM and (b) SRA during hospitalization at the stroke unit.
Results:
The detection rate of occult AF was 18% by ICM (n = 19) (range 6-575 days) and 62% (n = 65) had an increased risk for AF predicted by SRA. When comparing the predictive accuracy of SRA to ICM, the sensitivity was 95%, specificity 35%, positive predictive value 27% and negative predictive value 96%. In 18 patients with AF detected by ICM, SRA also showed a medium risk for AF. Only one patient with a very low risk predicted by SRA developed AF revealed by ICM after 417 days.
Conclusions:
A combination of SRA and ICM is a promising strategy to detect occult AF. SRA is reliable in predicting incident AF with a high negative predictive value. Thus, SRA may serve as a cost-effective pre-selection tool identifying patients at risk for AF who may benefit from further cardiac monitoring by ICM.
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