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Diagnostic Performance of a Smartphone ‐ Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

View Article: PubMed Central - PubMed

ABSTRACT

Background: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera–based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting.

Methods and results: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]).

Conclusions: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF.

No MeSH data available.


Related in: MedlinePlus

Smartphone camera‐based photoplethysmography (PPG) measurements of the pulse waveform. A, The Cardiio Rhythm standalone smartphone application. B, A finger is placed in contact with the smartphone camera and is illuminated by the adjacent LED flash. Examples of PPG recordings from a patient in (C) sinus rhythm and a patient in (D) atrial fibrillation (Video S1).
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jah31595-fig-0001: Smartphone camera‐based photoplethysmography (PPG) measurements of the pulse waveform. A, The Cardiio Rhythm standalone smartphone application. B, A finger is placed in contact with the smartphone camera and is illuminated by the adjacent LED flash. Examples of PPG recordings from a patient in (C) sinus rhythm and a patient in (D) atrial fibrillation (Video S1).

Mentions: A bipolar lead I ECG recording was first obtained from all patients using an AliveCor Heart Monitor (1st generation; AliveCor Inc). The AliveCor Heart Monitor is Food and Drug Administration cleared, CE marked, and clinically validated for the recording of single‐channel lead I ECGs.17, 18 For each patient, a single‐lead ECG tracing was acquired for 30 seconds with placement of ≥2 fingers from each hand on the device electrodes. The ECG recordings were transmitted to an iPad mini (Apple Inc installed with the AliveECG application (version 2.2.2) that interpreted the ECGs with an automated algorithm. For patients whose ECG tracings were initially affected by artifacts, they were instructed by the trained observers to repeat the recording so as to provide optimal tracing for subsequent reading by cardiologists. Immediately following completion of the ECG recording, 3 PPG waveforms were acquired sequentially from each patient using an iPhone 4S (Apple Inc) running the Cardiio Rhythm smartphone application (Cardiio Inc). PPG waveform recordings were performed by the patients under the supervision of trained observers. Patients were instructed to place the tip of their index finger of either hand on the camera of the iPhone (Figure 1, Video S1). Each PPG waveform recording lasted 17.1 seconds and was classified automatically by the Cardiio Rhythm smartphone application as “Regular” or “Irregular.” A diagnosis of AF was produced if at least 2 of 3 PPG waveform recordings from a single patient were classified as “Irregular.” When a diagnosis of AF was made by the Cardiio Rhythm application, the AliveCor automated AF detection algorithm, or both, a full 12‐lead ECG was performed within 15 minutes of the initial screening. An independent individual printed out the AliveCor ECG tracing with the automated rhythm interpretation redacted. Finally, 2 cardiologists who were blinded to the Cardiio Rhythm classifications, AliveCor automated interpretations, and patient baseline information independently reviewed the single‐lead ECG printouts to provide a reference diagnosis by using standard criteria.19


Diagnostic Performance of a Smartphone ‐ Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting
Smartphone camera‐based photoplethysmography (PPG) measurements of the pulse waveform. A, The Cardiio Rhythm standalone smartphone application. B, A finger is placed in contact with the smartphone camera and is illuminated by the adjacent LED flash. Examples of PPG recordings from a patient in (C) sinus rhythm and a patient in (D) atrial fibrillation (Video S1).
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jah31595-fig-0001: Smartphone camera‐based photoplethysmography (PPG) measurements of the pulse waveform. A, The Cardiio Rhythm standalone smartphone application. B, A finger is placed in contact with the smartphone camera and is illuminated by the adjacent LED flash. Examples of PPG recordings from a patient in (C) sinus rhythm and a patient in (D) atrial fibrillation (Video S1).
Mentions: A bipolar lead I ECG recording was first obtained from all patients using an AliveCor Heart Monitor (1st generation; AliveCor Inc). The AliveCor Heart Monitor is Food and Drug Administration cleared, CE marked, and clinically validated for the recording of single‐channel lead I ECGs.17, 18 For each patient, a single‐lead ECG tracing was acquired for 30 seconds with placement of ≥2 fingers from each hand on the device electrodes. The ECG recordings were transmitted to an iPad mini (Apple Inc installed with the AliveECG application (version 2.2.2) that interpreted the ECGs with an automated algorithm. For patients whose ECG tracings were initially affected by artifacts, they were instructed by the trained observers to repeat the recording so as to provide optimal tracing for subsequent reading by cardiologists. Immediately following completion of the ECG recording, 3 PPG waveforms were acquired sequentially from each patient using an iPhone 4S (Apple Inc) running the Cardiio Rhythm smartphone application (Cardiio Inc). PPG waveform recordings were performed by the patients under the supervision of trained observers. Patients were instructed to place the tip of their index finger of either hand on the camera of the iPhone (Figure 1, Video S1). Each PPG waveform recording lasted 17.1 seconds and was classified automatically by the Cardiio Rhythm smartphone application as “Regular” or “Irregular.” A diagnosis of AF was produced if at least 2 of 3 PPG waveform recordings from a single patient were classified as “Irregular.” When a diagnosis of AF was made by the Cardiio Rhythm application, the AliveCor automated AF detection algorithm, or both, a full 12‐lead ECG was performed within 15 minutes of the initial screening. An independent individual printed out the AliveCor ECG tracing with the automated rhythm interpretation redacted. Finally, 2 cardiologists who were blinded to the Cardiio Rhythm classifications, AliveCor automated interpretations, and patient baseline information independently reviewed the single‐lead ECG printouts to provide a reference diagnosis by using standard criteria.19

View Article: PubMed Central - PubMed

ABSTRACT

Background: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera–based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting.

Methods and results: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]).

Conclusions: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF.

No MeSH data available.


Related in: MedlinePlus