Prospective blinded Evaluation of the smartphone-based AliveCor Kardia ECG monitor for Atrial Fibrillation detection: The PEAK-AF study

Eur J Intern Med. 2020 Mar;73:72-75. doi: 10.1016/j.ejim.2019.11.018. Epub 2019 Dec 2.


Introduction: The AliveCor Kardia ECG monitor (ACK) offers a smartphone-based one-lead ECG recording for the detection of atrial fibrillation. We compared ACK lead I recordings with the 12-lead ECG and introduce a novel parasternal lead (NPL).

Methods: Consecutive cardiac inpatients were recruited. In all patients a 12-lead ECG, ACK lead I and NPL were obtained. Two experienced electrophysiologists were blinded and separately evaluated all ECG. We calculated sensitivity, specificity, and predictive values of the ACK ECG compared to the 12-lead ECG.

Results: 296 ECG from 99 patients (38 female, age 64 ± 15 years, BMI 27.8 ± 5.1 kg/m2) were analyzed. 20% of ACK lead I recordings contained a critical amount of artifact. The electrophysiologists' interpretation of the ACK recordings yielded a sensitivity of 100% and specificity of 94% for atrial fibrillation or flutter in lead I (κ = 0.90) and a sensitivity of 96% and specificity of 97% in the NPL (κ = 0.92). The ACK diagnostic algorithm displayed a significantly lower sensitivity (55-70%), specificity (60-69%), and accuracy (κ = 0.4-0.53) but a high negative predictive value (100%). Patients with atrial flutter (n = 5) and with ventricular stimulation (n = 12) had a high likelihood of being misclassified by the algorithm.

Conclusion: The AliveCor Kardia ECG monitor allows a highly accurate detection of atrial fibrillation by an interpreting electrophysiologist both in the standard lead I and a novel parasternal lead. The diagnostic algorithm offered by the system may be useful in screening recordings for further review. Diagnostic challenges present in atrial flutter and ventricular pacemaker stimulation.

Keywords: Atrial fibrillation; EHealth; Prevention; Stroke, ECG.

MeSH terms

  • Aged
  • Atrial Fibrillation* / diagnosis
  • Electrocardiography
  • Female
  • Humans
  • Middle Aged
  • Mobile Applications*
  • Prospective Studies
  • Smartphone