Diagnostic accuracy of a new detection algorithm for atrial fibrillation in cardiac telemonitoring with portable electrocardiogram devices

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• 作者：Sebastian Winkler MD^a ; ^{sebastian.winkler@charite.de"" rel=""nofollow} ; Carsten Axmann^b ; Brunhilde Schannor MD^a ; Simone Kim MD^c ; Thomas Leuthold^b ; Michael Scherf^b ; Robert Downes^b ; Herbert Nettlau^d ; Friedrich Koehler MD^a
• 关键词：Electrocardiogram ; Atrial fibrillation ; Telemonitoring ; Chronic heart failure
• 刊名：Journal of Electrocardiology
• 出版年：2011
• 出版时间：July-August 2011
• 年：2011
• 卷：44
• 期：4
• 页码：460-464
• 全文大小：475 K

文摘

Background

Telemedical approaches targeting cardiac outpatients try to include electrocardiogram (ECG) analysis. Increasing numbers of monitored patients require automated preanalysis of the ECG to prioritize the evaluation for the clinical professional to enable an efficient intervention.

Methods

ECGs were recorded from 60 patients, both with a standard 12-lead ECG and with a new handheld ECG device having dry electrodes for direct skin contact. Recordings of the handheld device were automatically analyzed by a new algorithm. The 12-lead recordings were evaluated by a blinded cardiologist and then compared to the automated analysis of the handheld ECG. Sensitivity and specificity of the algorithm for the detection of atrial fibrillation (AF) were calculated.

Results

A total of 60 ECG strips having 122 ± 36 beats were registered. One hundred percent of the ECG strips were sufficient for automated heart rate count; 96.6 % , for automated AF analysis; and 80 % , for PQ, QRS, and QTc time measurements. AF detection had a sensitivity of 92.9 % and a specificity of 90.9 % . There was no difference in heart rate count between automated and manual analysis (median, 71 vs 70 beats per minute; P = .51). Automated measurements of a summary complex showed no difference for PQ time (165 vs 161 milliseconds, P = .50) but overestimated QRS (119 vs 90 milliseconds, P = .001) and QTc (489 vs 417 milliseconds, P < .001) times as compared to the 12-lead recordings analyzed manually.

Conclusion

The new algorithm is suitable for automated preanalysis of the ECG data with regard to AF. It could be used for rapid selection of ECGs with relevant rhythm abnormalities from a large pool. Electrocardiographic data remain to be evaluated by health care professionals for exact diagnosis.