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.
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.
Authors: Jelle C L Himmelreich; Evert P M Karregat; Wim A M Lucassen; Henk C P M van Weert; Joris R de Groot; M Louis Handoko; Robin Nijveldt; Ralf E Harskamp Journal: Ann Fam Med Date: 2019-09 Impact factor: 5.166
Authors: Felix K Wegner; Simon Kochhäuser; Gerrit Frommeyer; Philipp S Lange; Christian Ellermann; Patrick Leitz; Patrick Müller; Julia Köbe; Lars Eckardt; Dirk G Dechering Journal: Clin Res Cardiol Date: 2021-05-07 Impact factor: 5.460
Authors: Onni E Santala; Jukka A Lipponen; Helena Jäntti; Tuomas T Rissanen; Jari Halonen; Indrek Kolk; Hanna Pohjantähti-Maaroos; Mika P Tarvainen; Eemu-Samuli Väliaho; Juha Hartikainen; Tero Martikainen Journal: Clin Cardiol Date: 2021-02-25 Impact factor: 2.882