Automatic Holter-ECG analysis in ischemic stroke patients to detect paroxysmal atrial fibrillation: ready to replace physicians?

BACKGROUND: The detection of paroxysmal atrial fibrillation (pAF) in patients presenting with ischemic stroke shifts secondary stroke prevention to oral anticoagulation. AIMS: In order to deal with the time- and resource-consuming manual analysis of prolonged ECG-monitoring data we investigated the effectiveness of pAF detection with an automated algorithm (AA) in comparison to a manual analysis with software support within the IDEAS study (study analysis (SA)). METHODS: We used the data set of the prospective IDEAS cohort of patients with acute ischemic stroke/TIA presenting in sinus rhythm undergoing prolonged 72h-Holter-ECG with central adjudication of AF. This adjudicated diagnosis of AF was compared to a commercially available AA. Discordant results with respect to the diagnosis of pAF were resolved by an additional cardiological reference confirmation. RESULTS: pAF was finally diagnosed in 62 patients (5.9%) of the cohort (n=1043). AA more often diagnosed pAF (n=60, 5.8%) as compared to SA (n=47, 4.5%). Due to a high sensitivity (96.8%) and negative predictive value ((NPV) 99.8%), AA is able to identify patients without pAF, while abnormal findings in AA require manual review (specificity 96%; positive predictive value (PPV) 60.6%). SA exhibited a lower sensitivity (75.8%) and NPV (98.5%) whilst showing a specificity and PPV of 100%. Agreement between the two methods classified by Kappa coefficient was moderate (0.591). CONCLUSION: Automated determination of 'absence of pAF' could be used to reduce the manual review work load associated with review of prolonged Holter ECG recordings.