Semi-automatic software based detection of atrial fibrillation in acute ischaemic stroke and transient ischaemic attack
Research output: Contribution to journal › Journal article › Research › peer-review
BACKGROUND AND PURPOSE: Paroxysmal atrial fibrillation (PAF) is often asymptomatic and increases the risk of ischaemic stroke. Detection of PAF is challenging but crucial because a change of treatment decreases the risk of ischaemic stroke. Post-stroke investigations recommend at least 24-h continuous cardiac rhythm monitoring. Extended monitoring detects more PAF but is limited by costs due to manual analysis. Interpretive software might be a reasonable screening tool. The aim was to validate the performance and utility of Pathfinder SL software compared to manual analysis.
METHODS: In all, 135 ischaemic stroke patients with no prior history of PAF or atrial fibrillation and who had done a 7-day continuous electrocardiogram monitoring (Holter) were included. Manual analysis was compared with Pathfinder SL software including a systematic control of registered events.
RESULTS: Seventeen (12.6%) patients were diagnosed with PAF (atrial fibrillation > 30 s). Pathfinder SL software including a systematic control of events registered 16 (94.1%) patients with PAF. Manually 15 (88.2%) patients were detected with PAF. Pathfinder SL had a negative predictive value of 99% and sensitivity of 94%.
CONCLUSIONS: Pathfinder SL software including a systematic evaluation of events is an acceptable alternative compared to manual analysis in PAF detection following ischaemic stroke. It is less time consuming and therefore a reliable, cheaper alternative compared to manual analysis.
Original language | English |
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Journal | European Journal of Neurology |
Volume | 24 |
Issue number | 2 |
Pages (from-to) | 322-325 |
ISSN | 1351-5101 |
DOIs | |
Publication status | Published - 2017 |
- Adult, Aged, Aged, 80 and over, Atrial Fibrillation, Automation, Brain Ischemia, Electrocardiography, Ambulatory, Female, Humans, Ischemic Attack, Transient, Male, Middle Aged, Predictive Value of Tests, Software, Stroke, Journal Article
Research areas
ID: 187665479