Mobile heart monitor proves effective in detecting atrial fibrillation
Study results suggest a new device, coupled with smartphone technology, can accurately and effectively detect atrial fibrillation.
Study results suggest a new device, coupled with smartphone technology, can accurately and effectively detect atrial fibrillation.
The KardiaMobile Cardiac Monitor from AliveCor, which records a rhythm strip using a smartphone app that enables patients to securely send their data to their physician, was evaluated in a single-center, nonrandomized and adjudicator-blinded study.
“This study is the first independent validation of this smartphone monitor system in the clinical setting using near simultaneous 12-lead ECGs,” said lead investigator Khaldoun Tarakji, MD, director of the Cleveland Clinic’s Center for Digital Health Technologies at its Heart and Vascular Institute. “In addition to providing an instantaneous rhythm interpretation, this smartphone monitor system is able to transmit a recording to a secure server where the recordings can be directly reviewed.”
Also See: Cleveland Clinic using system that transmits pacemaker data via Bluetooth
The 52-patient study, published in the journal HeartRhythm, found that the KardiaMobile Cardiac Monitor demonstrated 96.6 percent sensitivity and 94.1 percent specificity for the detection of atrial fibrillation, compared to physician-interpreted electrocardiograms.
At the same time, while the study showed that false negative detection rate was only 3.4 percent, the automated system failed to classify some of the results as either “normal” or “possible AF,” with the majority of these “unclassified” readings falling outside the predefined bounds for the algorithm.
“Given the predefined algorithm operating parameters and high rate of ‘unclassified’ recordings with resultant missed AF instances, this smartphone monitor’s algorithm is not suited to be a replacement for physician analysis,” observed Tarakji. “However, given its highly accurate performance when able to provide an interpretation, it holds potential as an adjunct to clinical decision making. Patients can use an automated AF detection as a basis for pursuing additional medical follow-up, and clinicians may use it to develop treatment plans supported by more objective data rather than relying only on symptoms.”
While AliveCor provided the monitors paired with a Wi-Fi enabled smart device for use in the study, the vendor was not involved in the study’s design, implementation, data analysis or manuscript preparation, according to investigators.
The KardiaMobile Cardiac Monitor from AliveCor, which records a rhythm strip using a smartphone app that enables patients to securely send their data to their physician, was evaluated in a single-center, nonrandomized and adjudicator-blinded study.
“This study is the first independent validation of this smartphone monitor system in the clinical setting using near simultaneous 12-lead ECGs,” said lead investigator Khaldoun Tarakji, MD, director of the Cleveland Clinic’s Center for Digital Health Technologies at its Heart and Vascular Institute. “In addition to providing an instantaneous rhythm interpretation, this smartphone monitor system is able to transmit a recording to a secure server where the recordings can be directly reviewed.”
Also See: Cleveland Clinic using system that transmits pacemaker data via BluetoothThe 52-patient study, published in the journal HeartRhythm, found that the KardiaMobile Cardiac Monitor demonstrated 96.6 percent sensitivity and 94.1 percent specificity for the detection of atrial fibrillation, compared to physician-interpreted electrocardiograms.
At the same time, while the study showed that false negative detection rate was only 3.4 percent, the automated system failed to classify some of the results as either “normal” or “possible AF,” with the majority of these “unclassified” readings falling outside the predefined bounds for the algorithm.
“Given the predefined algorithm operating parameters and high rate of ‘unclassified’ recordings with resultant missed AF instances, this smartphone monitor’s algorithm is not suited to be a replacement for physician analysis,” observed Tarakji. “However, given its highly accurate performance when able to provide an interpretation, it holds potential as an adjunct to clinical decision making. Patients can use an automated AF detection as a basis for pursuing additional medical follow-up, and clinicians may use it to develop treatment plans supported by more objective data rather than relying only on symptoms.”
While AliveCor provided the monitors paired with a Wi-Fi enabled smart device for use in the study, the vendor was not involved in the study’s design, implementation, data analysis or manuscript preparation, according to investigators.
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