automated atrial fibrillation detection algorithm using ... · t d accepted manuscript 1 automated...

Accepted Manuscript

Automated Atrial Fibrillation Detection Algorithm Using Smartwatch Technology

Joseph M. Bumgarner, MD, Cameron T. Lambert, MD, Ayman A. Hussein, MD,Daniel J. Cantillon, MD, Bryan Baranowski, MD, Kathy Wolski, MPH, Bruce D.Lindsay, MD, Oussama M. Wazni, MD MBA, Khaldoun G. Tarakji, MD MPH

PII: S0735-1097(18)33486-7

DOI: 10.1016/j.jacc.2018.03.003

Reference: JAC 24734

To appear in: Journal of the American College of Cardiology

Received Date: 14 February 2018

Revised Date: 1 March 2018

Accepted Date: 2 March 2018

Please cite this article as: Bumgarner JM, Lambert CT, Hussein AA, Cantillon DJ, Baranowski B,Wolski K, Lindsay BD, Wazni OM, Tarakji KG, Automated Atrial Fibrillation Detection AlgorithmUsing Smartwatch Technology, Journal of the American College of Cardiology (2018), doi: 10.1016/j.jacc.2018.03.003.

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https://doi.org/10.1016/j.jacc.2018.03.003

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Automated Atrial Fibrillation Detection Algorithm U sing Smartwatch Technology Joseph M. Bumgarner MDa, Cameron T. Lambert MDa, Ayman A. Hussein MDa, Daniel J. Cantillon MDa, Bryan Baranowski MDa, Kathy Wolski MPHb, Bruce D. Lindsay MDa, Oussama M. Wazni MD MBAa, Khaldoun G. Tarakji MD MPHa From the aDepartment of Cardiovascular Medicine and bthe Cleveland Clinic Coordinating Center for Clinical Research (C5Research), Cleveland Clinic, Cleveland Ohio Funding: AliveCor (AliveCor, Mountain View, CA) provided the Kardia Band monitors which were connected to an Apple Watch and paired via Bluetooth to a smartphone device for utilization in the study. AliveCor was not involved in the design, implementation, data analysis, or manuscript preparation of the study. Brief Title: Smartwatch CV Study Disclosures: JMB: None CTL: None AAH: Consulting Abbott, Biosense Webster DJC: Consulting Abbott, Boston Scientific, Stryker Sustainability, LifeWatch BB: None KW: None BDL: None OMW: Speaker honorarium, Spectranetics KGT: Medical advisory board for Medtronic, AliveCor Corresponding Author: Khaldoun G. Tarakji, MD MPH Section of Cardiac Pacing and Electrophysiology Heart and Vascular Institute Cleveland Clinic 9500 Euclid Avenue, J2-2 Cleveland, Ohio 44195 Telephone: (216) 445-9225 Fax: (216) 445-6149 E-mail: [email protected] Twitter: @khaldountarakji

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ABSTRACT Background: The Kardia Band (KB) is a novel technology that enables patients to record a rhythm strip using an Apple smartwatch. The band is paired with an app providing automated detection of atrial fibrillation (AF). Objectives: To examine whether the KB could accurately differentiate sinus rhythm (SR) from AF compared to physician-interpreted 12-lead ECGs and KB recordings. Methods: Consecutive patients with AF presenting for cardioversion (CV) were enrolled. Patients underwent pre-CV ECG along with a KB recording. If CV performed, a post-CV ECG was obtained along with a KB recording. The KB interpretations were compared to physician-reviewed ECGs. The KB recordings were reviewed by blinded electrophysiologists and compared to ECG interpretations. Sensitivity, specificity and K coefficient were measured. Results: One hundred patients were enrolled (Age 68 ± 11 years). Eight patients did not undergo CV. There were 169 simultaneous ECG and KB recordings. Fifty-seven were non-interpretable by the KB. Compared to ECG, the KB interpreted AF with 93% sensitivity, 84% specificity and K coefficient 0.77. Physician-interpretation of KB recordings demonstrated 99% sensitivity, 83% specificity and K coefficient 0.83. Of 57 non-interpretable KB recordings, interpreting electrophysiologists diagnosed AF with 100% sensitivity, 80% specificity and K coefficient 0.74. Among 113 cases where KB and physician readings of the same recording were interpretable, agreement was excellent (K coefficient 0.88). Conclusions: The KB algorithm for AF detection, supported by physician review can accurately differentiate AF from SR. This technology can help screen patients prior to elective CV and avoid unnecessary procedures. CONDENSED ABSTRACT: The KB is a novel technology recently cleared by the FDA which allows patients to record a rhythm strip instantaneously interpreted by an algorithm as AF or SR. Our study is the first investigation to assess the accuracy and clinical utility of this device in a clinical setting. In a blinded and prospective manner, we specifically compared automated KB algorithm recordings to both simultaneous ECG tracings and physician-interpreted KB recordings and found that the KB algorithm for AF detection, when supported by physician review, can accurately differentiate AF from SR. Key Words: Smartwatch, ECG monitoring, atrial fibrillation, digital health, cardioversion Abbreviations: KB – Kardia Band AF – Atrial Fibrillation SR – Sinus Rhythm ECG - Electrocardiogram CV – Cardioversion BPM – Beats per minute ILR – Implantable Loop Recorder

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Introduction

Atrial fibrillation (AF) is the most commonly encountered arrhythmia in clinical practice

and population-based studies forecast over 6 million individuals living with this diagnosis by

2050 (1,2). It is a chronic condition whose prevalence increases with age, and represents a

growing economic burden for our healthcare system (3, 4). While the journey of AF begins with

an initial diagnosis, its management is long term, nuanced, and often involves hospital-based

interventions along the way including electrical cardioversion (CV).

Recently, commercially available handheld cardiac rhythm recorders have been

developed that can record a rhythm strip using smartphone technology (5). In November 2017

the Kardia Band (KB) (AliveCor, Mountain View, CA) was introduced as the first FDA cleared

Apple Watch accessory that allows a patient to record a rhythm strip equivalent to lead I for 30

seconds. The KB is coupled with an application that provides an instantaneous and automatic

rhythm adjudication algorithm for the diagnosis of AF. The application can inform the patient

when AF is detected and transmit these results to the patient’s caring physician instantaneously.

The primary objective of our study was to examine whether the KB and AF detection

algorithm could accurately and reliably differentiate sinus rhythm (SR) from AF when compared

to physician interpreted 12-lead ECGs and KB recordings in patients with known AF presenting

to a high volume hospital-based electrophysiology practice for scheduled electrical CV.

Methods

Study design

This was a prospective, non-randomized and adjudicator-blinded study completed at a

tertiary care hospital-based electrical CV lab designed to evaluate the accuracy of the KB

automated algorithm for the detection of AF. AliveCor provided the KB connected to an Apple

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Watch which was paired via Bluetooth to a smartphone device (Apple Inc., Cupertino, CA) for

utilization in the study. AliveCor was not involved in the design, implementation, data analysis,

or manuscript preparation of the study. The study was approved by the Cleveland Clinic

Institutional Review Board.

Study participants

Consecutive patients with a diagnosis of AF who presented for scheduled elective CV

with or without a planned transesophageal echocardiogram were screened for enrollment.

Inclusion criteria included all adult patients age 18 to 90 years old who were able to provide

informed consent and willing to wear the KB before and after CV. We excluded all patients with

an implanted pacemaker or defibrillator.

Once enrolled, patients underwent a pre CV ECG followed immediately by KB

recording. These paired recordings were considered simultaneous. If the CV was performed a

post CV ECG was then obtained along with another KB recording. The KB tracing was

automatically analyzed using the KB algorithm. This algorithm measures rhythm irregularity and

P wave absence in real time to classify the rhythm strip as “possible AF”. If the criteria for AF is

not met the KB algorithm classifies regular rhythms with P waves as “normal” if the rate is

between 50-100 bpm or “unclassified” for those rhythms with rates below 50 bpm or over 100

bpm or if the recording is noisy or shorter than 30 seconds. The KB rhythm strips were

automatically transferred to the secure AliveCor server, downloaded, and printed for review.

All automated KB rhythm strips and ECGs were anonymized and distributed to two

blinded electrophysiologists who independently interpreted each tracing and assigned a diagnosis

of SR, AF or atrial flutter, or unclassified. If the two electrophysiologists disagreed on the

diagnosis a third electrophysiologist reviewed the tracing and assigned a final diagnosis. In order

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to assess the accuracy of the KB algorithm at appropriately identifying AF, the automated KB

interpretations were compared to both the physician interpreted KB rhythm strips and the

physician reviewed simultaneous ECGs.

Statistical analysis

Sensitivity and specificity were calculated for KB automated interpretation compared to

physician interpreted 12-lead ECG, for physician interpreted KB rhythm strip compared to

physician interpreted 12-lead ECG, and for KB automated interpretation compared to physician

interpreted KB recordings. Kappa (κ) coefficients for inter-observer agreement were assessed. κ

coefficients of >0.8 were considered to represent excellent agreement. AF and atrial flutter were

considered as a single disease state for all interpretations.

Results

One hundred patients were enrolled in the study from March 2017 through June 2017.

Demographics and clinical characteristics are summarized in Table 1. CV was performed in

85% of study participants. Of the 15 patients who did not undergo CV, 8 were cancelled due to

presentation in SR. There were 169 simultaneous 12-lead ECG and KB recordings obtained from

study participants, and 57 KB recordings were determined as unclassified by the KB algorithm.

Of the 57 unclassified KB tracings, 16 (28%) were due to baseline artifact and low amplitude of

the recording, 12 (21%) were due to a recording of less than 30 seconds in duration, 6 (10%)

were due to a heart rate of less than 50 bpm, 5 (9%) were due to a heart rate of greater than 100

bpm, and the remaining 18 (32%) were unclassified due to an unclear reason. Electrophysiologist

interpreted 12-lead ECGs were all interpretable.

In order to test the ability of the KB algorithm to detect AF, automated KB rhythm

interpretations and electrophysiologist interpreted 12-lead ECGs were compared. Among the

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recordings where the KB provided a diagnosis, it correctly diagnosed AF with 93% sensitivity,

84% specificity and a K coefficient of 0.77 (95% confidence interval 0.65-0.89) when compared

to the electrophysiologist interpreted 12-lead ECG (Table 2). Because our analysis used multiple

observations from the same individual, we evaluated for possible intra-individual correlations by

comparing only pre CV KB recordings to electrophysiologist interpreted 12-lead ECGs and

found the performance of the KB algorithm to be unchanged (Online Table 1).

In order to determine whether the automated KB recordings labeled as “unclassified” by

the algorithm were still clinically useful, these tracings were interpreted by our blinded

electrophysiologists and compared to the electrophysiologist interpreted 12-lead ECGs. Of the 57

automated unclassified KB recordings, the interpreting electrophysiologists were able to

correctly diagnose AF with 100% sensitivity, 80% specificity and a K coefficient of 0.74 (Table

3).

In order to assess the fidelity and overall quality of the KB tracings produced by the

smartwatch, electrophysiologist interpreted KB recordings were compared to corresponding 12-

lead ECG tracings. Twenty-two recordings were determined to be non-interpretable by the

reading electrophysiologist, and these were predominately due to baseline artifact. Of the

remaining 147 simultaneous recordings, the electrophysiologist interpreted 12-lead ECGs and

electrophysiologist interpreted KB recordings, physician interpretation of the KB tracings

demonstrated 99% sensitivity, 83% specificity and a K coefficient of 0.83 (Table 4).

Additionally, in order to measure the quality of the KB recordings, we compared the KB

automated algorithm interpretation to physician interpretation of the same recordings. Of the

cases where both methods were interpretable, the KB automated algorithm was 93% sensitive

and 97% specific in detecting AF with a K coefficient of 0.88 (Table 5).

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Discussion

The era of mobile healthcare technology has proliferated over the past decade.

Consumers from the general public now have direct access to devices and applications which

offer real-time measurements of cardiovascular physiology, and some technologies extrapolate

this data in order to provide diagnostic information (6). It is estimated that by 2019 annual sales

of such devices will reach 50 billion dollars worldwide (7). However, the ability of some devices

to accurately measure biometric endpoints has been questioned, and some mobile health

technologies are available without verification through rigorous clinical studies (8).

Alongside the growth of mobile healthcare technology has been the desire of many

physicians and patients to accurately monitor disease-related metrics of chronic conditions in the

ambulatory setting. AF is a good example of a relapsing condition that requires frequent

monitoring of clinical endpoints in order to assess the efficacy of treatment choices and plan

future interventions. The KB is the first smartwatch accessory cleared by the FDA and available

to the general public without a prescription which claims to instantaneously detect AF and

transmit this information to a patient’s treating physician.

In this study, we aimed to assess whether the KB and AF detection algorithm could

accurately and reliably differentiate SR from AF in patients with known AF presenting for

scheduled electrical CV (Central Illustration ). We compared automated KB interpretations to

simultaneously recorded ECGs read by blinded electrophysiologists and found very good

agreement between the two. When able to provide an interpretation, the automated KB readings

correctly identified AF with 93% sensitivity and 84% specificity (Figure 2). Of the 169 total KB

recordings, 57 (33.7%) were interpreted as unclassified by the automated KB algorithm. Reasons

that these recordings were deemed non-interpretable included short recordings less than 30

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seconds, low amplitude P waves, and baseline artifact. For those recordings where the automatic

KB tracing was non-interpretable, direct physician interpretation could be used to correctly

identify AF with 100% sensitivity and 80% specificity (Figure 3). In general, the KB recordings

when interpreted by the physician had excellent agreement with simultaneous 12 ECG

interpretation with 99% sensitivity and 83% specificity.

Prior to the development of the KB smartwatch algorithm, several algorithms used by

implantable loop recorders (ILRs) were validated for the detection of AF. Currently available

ILRs detect AF by sensing R waves and applying a variety of regularity algorithms to detect AF.

The Confirm DM2101 (Abbott, Chicago, Illinois) detects RR interval regularity and measures

suddenness of an irregular rhythm’s onset and offset to diagnose AF using two probabilistic

scoring models. The BioMonitor (Biotronik, Berlin, Germany) also measures R wave variability

and allows the clinician to adjust the number of cycle lengths used and the confirmation time

needed to detect AF. The most studied of the ILRs is the Reveal LINQ (Medtronic, Minneapolis,

MN) system whose algorithm for AF detection uses both R wave irregularity and a

programmable P wave evidence discrimination tool that can be modified based on the individual

needs of a given patient (9-11). The Reveal LINQ system was evaluated in the XPECT trial. In

this study the sensitivity and specificity for identifying patients with any AF was 96.1% and

85.4% respectively (12). In our study, the accuracy of the KB algorithm for the detection of AF

was comparable to these results.

Wearable devices like the KB require a safe and durable platform upon which recordings

can be reviewed and stored. A secure cloud-based platform has been developed to view and

download KB recordings. The applicability of this platform to the outpatient management of

patients with AF needs to be evaluated and studied in future trials. Our study also demonstrated

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that a subset of patients (8%) who presented for CV was found to be in SR. For each of these

patients, the automated KB algorithm did not erroneously identify AF, and the physician

interpretation of the KB recording correctly confirmed SR in each case. While this study was not

powered to assess the financial consequences of cancelled CVs, it is reasonable to conclude that

a measurable number of resources were forfeited by both the patient and the healthcare system in

anticipation of a procedure that was ultimately deemed unnecessary once SR was confirmed. As

data from the KB can be reviewed remotely, the resources used in preparation of these patients’

cancelled CVs could have been saved. The KB system has been previously shown to be cost

effective for AF screening. Our study suggests potential use of these products to provide more

effective healthcare delivery (13).

Limitations

This was a single center study at a tertiary referral center with a small sample size. The

population represented in this study had a known history of AF and a sufficient burden of AF to

prompt electrical CV. The performance of the KB smartwatch algorithm may be more variable in

a population with a lower AF burden. We did not evaluate socioeconomic status in our study and

only 17% of our enrolled patients were female. Additionally, none of the patients who

participated in our study had previously used the KB. These facts may limit the generalizability

of our findings in the general public, and future studies should consider measuring these

variables. Patients with cardiac implantable electronic devices were excluded from this study,

and further evaluation of the KB algorithm is needed in this patient population. Participants were

instructed on how to use the KB wristband while seated in a hospital bed immediately prior to

obtaining each recording. Their ability to record each tracing was directly observed. As a result,

the performance of the KB algorithm and the clarity of the recorded tracings may be less

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accurate in an outpatient or ambulatory setting. For the same reason, some of the unclassified

recordings could have been avoided with more patient practice on the proper use of the KB

device. Additionally, the KB prototype used in our study did not display a real-time ECG tracing

on the watch screen at the time of recording. Since FDA clearance, the KB app is now permitted

to display this information. We anticipate the real-time display of the ECG recording will

improve quality of the recordings obtained by users of the device.

Conclusions

The KB smartwatch automated algorithm for AF detection, supported by physician

review of these recordings, can reliably differentiate AF from SR. Avoiding scheduling

unnecessary electrical CVs is one example of a clinical application of the KB system. Many

other potential applications warrant further investigation and might transform our longitudinal

care of AF patients.

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CLINICAL PERSPECTIVES:

Competency in Patient Care: Among patients with AF who are being considered for elective

CV, the KB smartwatch algorithm can be used with physician oversight to accurately

differentiate between SR and AF.

Translational Outlook: As the prevalence of AF continues to rise within the era of expanding

access to mobile healthcare technology, randomized controlled trials will be needed to further

validate the clinical and financial risks and benefits of mobile healthcare devices.

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10. Passman RS, Rogers JD, Sarkar S, et al., Development and validation of a dual sensing

scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac

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13. Lowres N, Neubeck L, Salkeld G, et al., Feasibility and cost-effectiveness of stroke

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pharmacies. The SEARCH-AF study. Thromb Haemost, 2014;111(6):1167-76.

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FIGURE LEGENDS

Central Illustration: Assessment of the Accuracy of the Kardia Band Smartwatch

Algorithm for AF Detection Compared to 12-Lead ECG in Patients Undergoing

Cardioversion. Left Side of Figure: Automated KB recordings are compared to physician-

interpreted 12-lead ECGs and detect AF with 93% sensitivity, 84% specificity. Physician-

reviewed unclassified automated KB recordings are compared to physician-interpreted 12-lead

ECGs and detect AF with 100% sensitivity, 80% specificity. Right Side of Figure: Physician-

interpreted KB recordings are compared to physician-interpreted 12-lead ECGs and detect AF

with 99% sensitivity and 83% specificity. Twenty-two physician-interpreted KB recordings were

non-interpretable. AF: Atrial Fibrillation, ECG: Electrocardiogram, KB: Kardia Band.

Figure 1: The Kardia Band from AliveCor Paired with an Apple Smartwatch.

AliveCor (AliveCor, Mountain View, California) Kardia Band monitors were connected to an

Apple smartwatch and paired via Bluetooth to a smartphone device for utilization in the study.

Figure 2: Correct KB Interpretations Compared to Simultaneous ECG

A. Simultaneous recordings of SR using KB (left) and 12-lead ECG (right). The KB

automated algorithm identifies SR for this sample.

B. Simultaneous recordings of AF using KB (left) and 12-lead ECG (right). The KB

automated algorithm identifies AF for this sample.

Figure 3: Incorrect KB Interpretations Compared to Simultaneous ECG

A. Heart rhythm recording defined by KB (left) as unclassified with simultaneous 12-lead

ECG (right) interpreted as SR

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B. Heart rhythm recording defined by KB (left) as unclassified with simultaneous 12-lead

ECG (right) interpreted as AF.

C. Heart rhythm recording defined by KB (left) as too short to analyze with simultaneous

12-lead ECG (right) interpreted as AF.

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Table 1: Demographics, Clinical, and Procedural Characteristics of Enrolled Patients

PARAMETER All Patients

Total Number of Patients

(n) 100

Age (average ± SD) 68.2 ± 10.86

Gender (n, %)

Female 17 (17.0)

Anticoagulant (n, %)

Warfarin (Coumadin) 32 (32.0)

Dabigatran (Pradaxa) 2 (2.0)

Rivaroxaban (Xarelto) 19 (19.0)

Apixaban (Eliquis) 47 (47.0)

TEE Performed (n, %)

Yes, scheduled 21 (21.0)

Yes, added on 2 (2.0)

TEE Finding (n,%)

No Thrombus 20 (20.2)

Sludge 1 (1.0)

Thrombus 2 (2.0)

CV Performed

Yes 85 (85.0)

No 15 (15.0)

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Reason if No CV

Performed

Sub-therapeutic INR 4 (26.7)

Found to be in NSR 8 (53.3)

Thrombus on TEE 2 (13.3)

Hypotension during TEE 1 (6.7)

CV Outcome

Successful 78 (91.7)

Transient 3 (3.5)

Failed 4 (4.7)

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Table 2 – KB Algorithm Reading Compared to Electrophysiologist Interpreted 12-Lead

ECG

Electrophysiologist Interpreted 12-lead ECG

KB Algorithm

Interpretation AF/Flutter SR

Non-

interpretable Total

AF/Flutter 63 7 0 42

SR 5 37 0 70

Missing/Unclassified 23 34 0 57

Total 91 78 0 169

Sensitivity, specificity and κ coefficient are calculated only for the

simultaneous transmission with interpretation (in bold). Sensitivity 93%

(63/68), (95% confidence interval 0.86-0.99), specificity 84% (37/44), (95%

confidence interval 0.73-0.95).

κ coefficient of 0.77 (95% confidence interval 0.65-0.89) for numbers in bold.

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Table 3 – Unclassified KB Readings when Read by Electrophysiologist Compared to

Electrophysiologist Interpreted 12-Lead ECG


Electrophysiologist

Interpreted KB Reading AF/Aflutter SR

Non-

interpretable Total


SR 0 20 0 20

Missing/Non-interpretable 9 9 0 18

Total 23 34 0 57

Sensitivity, specificity and κ coefficient are calculated only for the simultaneous

transmission with interpretation (in bold). Sensitivity 100% (14/14), (95% confidence

interval 0.77-1.0), specificity 80% (20/25), (95% confidence interval 0.64-0.96).


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Table 4 – Electrophysiologist Interpreted KB Reading Compared to Electrophysiologist

Interpreted 12-Lead ECG


Electrophysiologist

Interpreted KB Reading AF/Aflutter SR

Non-

interpretable Total


SR 1 55 0 56

Missing/Non-interpretable 10 12 0 22

Total 91 78 0 169





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Table 5: KB Automated Reading Compared to Electrophysiologist Interpreted KB

Recordings

Electrophysiologist Interpreted KB Recordings

Kardia Band Automatic

Reading AF/Aflutter SR

Missing/Non-

Interpretable Total


SR 5 36 2 43

Missing/Unclassified 20 21 18 59

Total 96 58 22 176





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Online Appendix

Online Table 1 – KB Algorithm Reading Compared to Electrophysiologist Interpreted 12-

Lead ECG Prior to CV (excludes post CV and unclassified measurements)

KB Algorithm

Interpretation


AF/Flutter SR

Total

AF/Flutter 60 0 60

SR 5 5 10

Total 65 5 70

Sensitivity 92% (60/55), (95% confidence interval 0.86-0.99); Specificity 100% (5/5), (95%

confidence interval 0.48-1.00).


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