Automatic Holter electrocardiogram analysis in ischaemic stroke patients to detect paroxysmal atrial fibrillation : ready to replace physicians?

Background and purpose The detection of paroxysmal atrial fibrillation (pAF) in patients presenting with ischaemic stroke shifts secondary stroke prevention to oral anticoagulation. In order to deal with the time- and resource-consuming manual analysis of prolonged electrocardiogram (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 dataset of the prospective IDEAS cohort of patients with acute ischaemic stroke/transient ischaemic attack presenting in sinus rhythm undergoing prolonged 72-h Holter ECG with central adjudication of atrial fibrillation (AF). This adjudicated diagnosis of AF was compared with a commercially available AA. Discordant results with respect to the diagnosis of pAF were resolved by an additional cardiological reference confirmation. Results Paroxysmal AF was finally diagnosed in 62 patients (5.9%) in the cohort (n = 1043). AA more often diagnosed pAF (n = 60, 5.8%) as compared with SA (n = 47, 4.5%). Due to a high sensitivity (96.8%) and negative predictive value (99.8%), AA was able to identify patients without pAF, whereas abnormal findings in AA required manual review (specificity 96%; positive predictive value 60.6%). SA exhibited a lower sensitivity (75.8%) and negative predictive value (98.5%), and showed a specificity and positive predictive value 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 workload associated with review of prolonged Holter ECG recordings.