Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iREAD Study.

Background: The Kardia Mobile Cardiac Monitor (KMCM) detects atrial fibrillation (AF) via a handheld cardiac rhythm recorder and AF detection algorithm. The algorithm operates within predefined parameters to provide a "normal" or "possible atrial fibrillation detected" interpretation; outside of these parameters, an "unclassified" rhythm is reported. The system has been increasingly used, but its performance has not been independently tested.

Objective: The objective of this study was to evaluate whether the KMCM system can accurately detect AF.

Methods: A single-center, adjudicator-blinded case series of 52 consecutive patients with AF admitted for antiarrhythmic drug initiation were enrolled. Serial 12-lead electrocardiograms (ECGs) and nearly simultaneously acquired KMCM recordings were obtained.

Results: There were 225 nearly simultaneously acquired KMCM and ECG recordings across 52 enrolled patients (mean age 68 years; 67% male). After exclusion of unclassified recordings, the KMCM automated algorithm interpretation had 96.6% sensitivity and 94.1% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.89. Physician-interpreted KMCM recordings had 100% sensitivity and 89.2% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.85. Sixty-two recordings (27.6%) were unclassified by the KMCM algorithm. In these instances, physician interpretation of KMCM recordings had 100% sensitivity and 79.5% specificity for AF detection as compared with 12-lead ECG interpretation, with a κ coefficient of 0.71.

Conclusion: The KMCM system provides sensitive and specific AF detection relative to 12-lead ECGs when an automated interpretation is provided. Direct physician review of KMCM recordings can enhance diagnostic yield, especially for unclassified recordings.