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For peer review only

Diagnostic Performance of an Automatic Blood Pressure Measurement device, Microlife Afib, for Atrial Fibrillation

Screening in a Real World Primary Care Setting

Journal: BMJ Open

Manuscript ID bmjopen-2016-013685

Article Type: Research

Date Submitted by the Author: 15-Aug-2016

Complete List of Authors: Chan, Pak Hei; The University of Hong Kong, Cardiology Divison, Department of Medicine Wong, Chun Ka; The University of Hong Kong, Cardiology Divison,

Department of Medicine Pun, Louise; Department of Family Medicine and Primary Healthcare, HKEC Wong, Yu Fai; Department of Family Medicine and Primary Healthcare, HKEC Wong, Michelle Man-Ying; Department of Family Medicine and Primary Healthcare, HKEC Chu, Daniel Wai-Sing; Department of Family Medicine and Primary Healthcare, HKEC Wah Siu, Chung ; The University of Hong Kong, Cardiology Divison, Department of Medicine

<b>Primary Subject Heading</b>:

Cardiovascular medicine

Secondary Subject Heading: General practice / Family practice

Keywords: Atrial fibrillation, Microlife, Screening

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Diagnostic Performance of an Automatic Blood

Pressure Measurement device, Microlife Afib, for

Atrial Fibrillation Screening in a Real World Primary

Care Setting

#Pak-Hei CHAN, MBBS;1 #Chun-Ka, WONG, MBBS;1 Louise PUN, BA;2

Yu-Fai WONG, MBBS;2 Michelle Man-Ying, WONG, MBBS;2 †Daniel Wai-Sing CHU, MBBS;2 †Chung-Wah Siu, MD1

Institution: 1Cardiology Division, Department of Medicine, Li Ka Shing Faculty

of Medicine, the University of Hong Kong, Hong Kong SAR, China;

2Department of Family Medicine and Primary Healthcare, Hong Kong East

Cluster, Hong Kong, SAR, China

#These authors are co-first authors.

†These authors are co-senior and co-corresponding authors.

Cover title: Microlife for AF screening

Number of Tables: 1

Number of Figures: 4

Keywords: Atrial fibrillation; microlife; screening

Address of Correspondence: Chung-Wah Siu, MD Cardiology Division, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China Tel: (852) 2255-4694, Fax: (852) 2818-6304, E-mail: [email protected]

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ABSTRACT

Objective: To evaluate the diagnostic performance of a UK National Institute

for Health and Care Excellence (NICE)-recommended automatic oscillometric

blood pressure (BP) measurement device incorporated with an AF detection

algorithm (Microlife WatchBP Home) for real-world AF screening in a primary

healthcare setting.

Setting: Primary healthcare setting in Hong Kong.

Interventions: This was a prospective AF screening study carried out

between 1st September 2014 and 14th January 2015. The Microlife device was

evaluated for AF detection and compared with a reference standard of lead-I

ECG.

Primary outcome measures: Diagnostic performance of Microlife for AF

detection

Results: 5,969 patients (mean age: 67.2±11.0 years; 53.9% female) were

recruited. The mean CHA2DS2-VASc score was 2.8±1.3. AF was diagnosed in

72 patients (1.21%) and confirmed by a 12-lead ECG. The Microlife device

correctly identified AF in 58 patients and produced 79 false positives. The

corresponding sensitivity and specificity for AF detection was 80.6% (95% CI:

69.5-88.9) and 98.7% (95% CI: 98.3-98.9) respectively. Amongst patients with

a false positive by the Microlife device, 30.4% were in sinus rhythm; 35.4%

had sinus arrhythmia; and 29.1% exhibited premature atrial complexes. With

the low prevalence of AF in this population, the positive and negative

predictive values of Microlife device for AF detection were 42.4% (95% CI:

34.0-51.2) and 99.8% (95% CI: 99.6-99.9) respectively. The overall diagnostic

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performance of Microlife device to detect AF as determined by area under the

curves was 0.90 (95% CI: 0.89-0.90).

Conclusions: In the primary care setting, Microlife WatchBP Home was an

effective means to screen for AF with a high sensitivity of 80.6% and

specificity of 98.7%, in addition to its routine function of BP measurement. In a

younger patient population aged <65 years with a lower prevalence of AF,

Microlife WatchBP Home demonstrated a similar diagnostic accuracy.

Strength of study:

• Prospective study evaluating the diagnostic value for atrial fibrillation of

a commercially-available UK NICE-recommended device: Microlife

• Large study population in a primary care setting

• Study result support the use of Microlife device can be extended to <65

years old for AF detection, extending the UK NICE recommendations

for AF screening

Limitation of study:

• Due to the primary healthcare setting, 12-lead ECG is not feasible in

every recruited patient thus single lead-I ECG tracing was used as

reference instead to provide rhythm diagnosis

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INTRODUCTION

Atrial fibrillation (AF) has emerged as a global epidemic with a progressive

increase in incidence, prevalence, and consequent stroke and mortality.1

Although AF-related stroke and mortality are highly preventable with the use

of long-term oral anticoagulants, up to 25% of patients with AF-related stroke

have AF diagnosed only at the time of stroke,2-4 precluding any form of

primary preventive measure. As a result, diagnosing AF prior to stroke

occurrence is now recognized as a priority. The European Society of

Cardiology recommends opportunistic screening for AF (pulse palpitation,

followed by standard ECG if irregular pulse detected) in patients aged 65

years or older.5 6

In addition to age, hypertension is another important risk factor for AF,7

accounting for 14% of the AF burden in both men and women.8 Hypertension

contributes more AF cases than any other risk factor because of its high

prevalence (~1 billion individuals worldwide).

Recently, an automatic oscillometric blood pressure measurement device with

an incorporated specific algorithm to detect AF (Microlife WatchBP Home) has

been recommended by the UK National Institute for Health and Care

Excellence (NICE) to screen for AF during routine office blood pressure

measurement in primary care patients aged 65 years or older.9

Although the diagnostic performance of the device for AF detection has been

previously investigated,7-12 these studies have been limited by their relatively

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small sample size, typically less than 1000 participants.7-12 The total number

of participants was around 2,000 only. More importantly, most studies were

carried out in a high-risk population such as a general cardiology clinic,8 9 11 or

in patients with recent stroke.12 The generalizability to a primary care setting,

the target environment for mass AF screening, remains questionable. In

addition, the diagnostic procedure has evolved throughout these studies; for

instance, the number of readings used for diagnosis of AF increased from one

in the initial study11 to three in the latest study, and has substantially improved

the diagnostic performance.12 Therefore, the performance of the Microlife

WatchBP Home for AF screening using the current diagnostic procedure in a

real world mass AF screening setting remains unclear. The primary aim of this

study was to assess the diagnostic performance of the Microlife WatchBP

Home for AF screening.

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METHODS

Study Design

This prospective screening study was coordinated by the University of Hong

Kong and the Department of Family Medicine and Primary Healthcare

Service, Hong Kong East Cluster, Hospital Authority, Hong Kong. The study

protocol was approved by the local Institutional Review Board. Patients were

recruited from the Violet Peel General Outpatient Clinic in Hong Kong from

September 2014 to January 2015. Patients were eligible if they had a history

of hypertension and/or diabetes mellitus, or were ≥65 years of age. Patients

with a pacemaker or implantable defibrillator were excluded from the study.

Informed consent was obtained from all patients who fulfilled the inclusion

criteria.

Screening Procedure

A bipolar lead I ECG recording was first obtained from all patients using an

AliveCor Heart Monitor (AliveCor Inc., San Francisco, CA, USA). The AliveCor

Heart Monitor is FDA-cleared, CE marked, and clinically validated for the

recording of single-channel lead I ECGs.13 14 For each patient, a single-lead

ECG tracing was acquired for 30 seconds with placement of two or more

fingers from each hand on the device electrodes. The ECG recordings were

transmitted to an iPad mini (Apple Inc., Cupertino, CA, USA) installed with the

AliveECG application (version 2.1.1), and were reviewed by two independent

cardiologists who were blinded to the Microlife WatchBP Home AF

classifications to provide a reference diagnosis using standard criteria.15

When a diagnosis of AF was made, a full 12-lead ECG was performed.

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Immediately following completion of the ECG recording, three blood pressure

measurements were taken using the automatic oscillometric blood pressure

monitor (the Microlife WatchBP Home; Microlife USA, Dunedin, FL) with AF

detection algorithm. The “Afib” icon flashed when AF was detected.

Statistical Analysis

Continuous and discrete variables are expressed as mean ± standard deviation

and percentages, respectively. Sensitivity, specificity, likelihood ratio, and

predictive value for AF diagnosis were calculated as simple proportions with

corresponding 95% confidence interval (CI) for the Microlife WatchBP Home

classifications for AF detection. The diagnostic performance for AF detection

was further assessed using the c-statistic (area under the curve). The c-statistic

for receiver operating characteristic curve was calculated using Analyze-It for

Excel with the Delong-Delong comparison for c-statistic. The c-statistic

integrates measures of sensitivity and specificity of the range of a variable.

Ideal prediction yields a c-statistic of 1.00 whereas a value of <0.5 indicates

that the prediction is no better than chance. Calculations were performed using

SPSS software (version 21.0) and MedCal (version 13.1.2).

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RESULTS

Between 1st September 2014 and 14th January 2015, 6,075 patients who

fulfilled the inclusion criteria were invited to participate in the AF screening

study, of whom 106 declined (1.7%). As a result, 5,969 patients were included

in this study (Figure 1). Table 1 summarizes their characteristics. The mean

age was 67.2 ± 11.0 years and 2,751 patients (46.1%) were male.

Hypertension was present in 4,948 patients (82.9%) and diabetes mellitus in

2,742 (45.9%). Coronary artery disease was present in 313 patients (5.2%)

and 271 (4.5%) had a history of previous ischemic stroke. The mean

CHA2DS2-VASc score was 2.8 ± 1.3.

Of these 5,969 patients, 5,467 (91.59%) were in sinus rhythm based on

interpretation by two cardiologists of the single-lead ECG recording (Figure

2A). AF was diagnosed in 72 patients (1.21%) and confirmed by a standard

12-lead ECG. Other abnormal non-AF rhythms detected in the study

population included premature atrial contractions (n=171, 2.86%), premature

ventricular contractions (n=144, 2.41%), and sinus arrhythmias (n=115,

1.93%). The prevalence of AF increased with increasing age from 0.51%

amongst those aged <65 years, to 0.91% amongst those aged 65-74 years,

and 2.71% amongst those aged ≥75 years (Figure 2B).

The Microlife WatchBP Home correctly identified the presence of AF in 58 out

of 72 AF patients and produced 79 false positive results (Figure 3A). The

corresponding sensitivity of the Microlife WatchBP Home to detect AF was

80.6% (95% CI: 69.5-88.9)(Figure 4A). The Microlife WatchBP Home

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produced a false positive result for AF in 79 of 5,897 non-AF patients with a

corresponding specificity of 98.7% (95% CI: 98.3-98.9). Amongst these 79

patients, 24 were in sinus rhythm (30.4%); 28 had sinus arrhythmia (35.4%);

23 had premature atrial contractions (29.1%); and 4 had premature ventricular

contractions (5.1%) (Figure 3A). Nonetheless the specificity of the Microlife

WatchBP Home for AF detection remained high in these patients: 99.6% in

patients with sinus rhythm, 97.2% in patients with premature ventricular

contractions, 86.5% in patients with premature atrial contractions, and 75.7%

in patients with sinus arrhythmia. Given the relatively low prevalence of AF

(1.21%) in this population, the positive and negative predictive value of the

Microlife WatchBP Home to detect AF was 42.4% (95% CI: 34.0-51.2) and

99.8% (95% CI: 99.6-99.9) respectively. The positive likelihood ratio and the

negative likelihood ratio of the Microlife WatchBP Home was 60.1 (95% CI:

47.0-77.0) and 0.2 (95% CI: 0.1-0.3) respectively. The diagnostic performance

of Microlife WatchBP Home including sensitivity, specificity, positive and

negative predictive values remained comparable across patients with different

age (Figure 3B to 3D, and Figure 4A). The overall diagnostic performance of

the Microlife WatchBP Home to detect AF as determined by area under the

curves was 0.90 (95% CI: 0.89-0.90), and was largely the same across

different age groups (Figure 4B).

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DISCUSSION

To the best of our knowledge, this is the largest screening study for AF

utilizing the UK NICE guideline recommended- Microlife WatchBP Home

automatic blood pressure monitoring machine. In this study, the diagnostic

performance of Microlife WatchBP Home was compared with a reference

standard of single-lead I ECG. First, our results demonstrate that in the

primary care setting where the prevalence of AF is relatively low, the Microlife

WatchBP Home machine detected AF with a high sensitivity of 80.6% and

specificity of 98.7%. Second, the device detected AF with high diagnostic

accuracy as determined by area under the curves of 0.9, when compared with

reference single-lead I ECG. Third, the device effectively detected AF in

patients younger than 65 years – not the usual target population for AF

screening. Finally, the diagnostic performance of the device, in terms of

sensitivity, specificity, positive and negative predictive values, did not differ

across different age groups with a mean age of patients in this study of 67.2 ±

11.0 years.

It is well established that AF is associated with a 5-fold increased risk of

ischemic stroke.16 With effective anticoagulation by warfarin, such risk can be

reduced by 64%.17 Nonetheless in the absence of a firm diagnosis of AF, for

instance in patients who are asymptomatic, anticoagulation therapy cannot be

commenced. Underlying AF is newly diagnosed in up to 25% of patients with

ischemic stroke.2-4 Thus, AF screening was recommended by the European

Society of Cardiology in those aged 65 years or older to diagnose AF in this

high-risk population,5 where advanced age is one of the important underlying

risk factors.18 To achieve effective AF screening, the availability of a reliable

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easy-to-use screening tool is of paramount importance. A conventional 12-

lead ECG records cardiac rhythm for 10 seconds and is the gold standard for

diagnosis of cardiac arrhythmia. Nonetheless although a 12-lead ECG can be

employed as a screening tool for AF,19 its cumbersome and time-consuming

nature make it less appealing, particularly on a large-scale. As a result, there

has been a recent surge in the availability of various easy-to-use devices.

The automated oscillometric blood pressure monitoring machine - Microlife

WatchBP Home - can distinguish AF from normal sinus rhythm based on the

detection of pulse irregularities during BP measurement.8-11 By measuring the

time interval between successive R-R cycles and calculating the ratio of the

standard deviation of these time intervals to the mean R-R interval, an

irregularity index is generated. Previous study confirmed that an irregularity

index with cut-off >0.06 corresponds to AF with high sensitivity and

specificity.9 Theoretically, lowering the cut-off irregularity index might increase

sensitivity for AF detection albeit at the cost of lower specificity. In addition,

the Microlife WatchBP Home device used in this study automatically

measured blood pressure three times: this further improved the diagnostic

accuracy for pulse irregularities or AF. Of note, the programmed AF detector

in Microlife WatchBP Home device differs from all other arrhythmia detectors

incorporated in many automated blood pressure monitors in that it is specific

for AF.8-10 20-23 Arrhythmia detectors installed in other automated blood

pressure monitors provide only a warning that the blood pressure recorded

may be inaccurate due to the possible presence of arrhythmia, rather than

specifically diagnosing AF.24

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Other devices such as the AliveCor Heart Monitor that is equipped with

automatic algorithms for interpreting a lead-I ECG tracing have also been

tested in previous studies,14 25 including the recently published STROKESTOP

study.26 This study utilized the Microlife WatchBP Home device to detect AF,

but it was only validated in a population aged 65 years or above, the target

population for AF screening. One of the important findings in this study was

that the diagnostic performance of Microlife WatchBP Home machine in a

younger patient population, which was characterized by a lower prevalence of

AF and other arrhythmias including premature atrial complex, was not

negatively affected. Current guidelines5 27 promote AF screening only in those

aged ≥65 years, because of the lack of clinical evidence and possibly higher

prevalence of sinus arrhythmia and thus false-positive results in younger

subjects. The current study provides evidence that the Microlife WatchBP

Home machine achieves comparable diagnostic accuracy in those aged <65

and those ≥65. This device is also the first to be validated for AF screening in

younger patients aged <65, potentially extending the indication of UK NICE

guideline for the Microlife WatchBP Home machine. In this study, detection of

AF during automated BP measurement was feasible in the primary care

setting and appeared to be superior to routine pulse palpation in terms of

diagnostic accuracy for AF, hence facilitating AF screening in high-risk

patients in the primary care setting.

One of the potential drawbacks of the Microlife WatchBP Home machine is a

false-positive result that necessitates subsequent confirmation by an ECG for

an accurate arrhythmia diagnosis. This may be anxiety promoting in patients

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who are found to have AF. Theoretically, repetition of measurements with the

device should improve diagnostic accuracy. This also applies to patients with

paroxysmal AF: repeated measurement might enhance the sensitivity of the

test, as demonstrated in the recent study using a smartphone-based device

for AF screening – rate of AF detection increased with longer duration of

measurement.26 It also helps to identify AF in those at-risk patients who

regularly perform home blood pressure monitoring, for instance, elderly

hypertensive patients who are usually asymptomatic despite the presence of

underlying AF. From the 79 patients with a false-positive Microlife test result,

the presence of premature atrial complex or sinus arrhythmia each accounted

for around one-third of patients. Given the low false-positive rate of 1.3% and

high specificity, the device is regarded as a good screening tool.

An advantage of the Microlife WatchBP Home machine as an AF screening

tool is its ease of use and less time-consuming nature compared with

performing a routine 12-lead ECG in every patient who attends a primary care

clinic. Blood pressure is measured in most patients as a matter of routine

during follow-up with a family physician so it is advantageous to

simultaneously be able to detect AF. This study demonstrated that the

Microlife WatchBP Home machine is an invaluable means of screening for AF

in an at-risk population in the primary care setting with a relatively lower

prevalence of AF, including patients aged <65 years.

Study limitation

A formal 12-lead ECG was not recorded in every participant. Instead, two

cardiologists independently over-read a single-lead ECG of each patient and

provided a diagnosis. This was necessary given the time and cost constraints

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inherent in dealing with a large number of patients. Nonetheless all patients

identified by the cardiologists to have AF underwent a follow-up 12-lead ECG

for further confirmation of the diagnosis.

Conclusion

In the primary care setting with an AF prevalence of 1.21%, Microlife

WatchBP Home was shown to be an effective screening tool for AF with a

high sensitivity of 80.6% and specificity of 98.7%, as well as providing a

routine function of blood pressure measurement. Diagnostic accuracy of the

Microlife WatchBP in a younger patient population aged <65 years and a

lower prevalence of AF, achieved a similar diagnostic accuracy compared

with its use in an older population, thus potentially extending the NICE

guideline indication as an AF screening tool to a younger at-risk population.

FOOTNOTE

Ethical approval: This study was approved by the ethics committee of the

Hong Kong East Cluster, Hospital Authority (HKEC-2014-079). Patient

consent was obtained for each participating patient.

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Contribution Statement Contributions to the conception or design of the work - Chan PH, Wong CK,

Chu DW, Siu CW

Contributions to the acquisition, analysis, or interpretation of data for the work

- all authors

Drafting the work or revising it critically for important intellectual content -

Chan PH, Wong CK, Siu CW

Final approval of the version to be published - all authors

Agreement to be accountable for all aspects of the work in ensuring that

questions related to the accuracy or integrity of any part of the work are

appropriately investigated and resolved - all authors

Competing Interests None related to the current study. Acknowledgement None. Funding The current study does not receive any funding. Data Sharing Statement No additional data available.

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Disclaimer I, Chung-Wah Siu, the Corresponding Author of this article contained within

the original manuscript which includes any diagrams & photographs within

and any related or stand alone film submitted (the Contribution”) has the right

to grant on behalf of all authors and does grant on behalf of all authors, a

licence to the BMJ Publishing Group Ltd and its licencees, to permit this

Contribution (if accepted) to be published in the BMJ Open and any other

BMJ Group products and to exploit all subsidiary rights, as set out in our

licence set out at: http://www.bmj.com/about-bmj/resources-authors/forms-

policies-and-checklists/copyright-open-access-and-permission-reuse.”

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Table 1: Demographics of Study Population

Characteristics Number (%)

(n = 5,969)

Age, mean ± SD, years 67.2 ± 11.0

Male 2,751 (46.1)

Hypertension 4,948 (82.9)

Diabetes mellitus 2,742 (45.9)

Coronary artery disease 313 (5.2)

Previous myocardial infarction 46 (0.8)

Heart failure 54 (0.9)

Previous stroke 271 (4.5)

Previous intracranial hemorrhage 35 (0.6)

CHA2DS2-VASc score 2.8 ± 1.3

Abbreviation: CHA2DS2-VASc score: Congestive heart failure = 1 point; Hypertension = 1 point; Age≥75 years = 1 point and Age=65-74 years = 1 point; Diabetes mellitus = 1 point; previous stroke =2 points; VA: vascular disease = point; Sex category (female) = 1 point.

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Figure Legends:

Figure 1. Study enrollment and flow.

Figure 2. (A) Rhythm diagnoses of the study population based on

interpretation by two independent cardiologists of a 30-second bipolar lead I

ECG. (B) Prevalence of AF categorized into different age groups.

Figure 3. Contingency tables for atrial fibrillation detection and rhythm

diagnoses of an automatic oscillometric blood pressure measurement device

incorporated with a specific algorithm for AF detection (Microlife WatchBP

Home), categorized into different age groups: (A) Overall; (B) age <65 years;

(C) age 65-74 years; (D) age ≥75 years.

Figure 4. Diagnostic performance of the an automatic oscillometric blood

pressure measurement device incorporated with a specific algorithm for AF

detection (Microlife WatchBP Home) (A) Sensitivity, specificity, positive

predictive values (PPV), and negative predictive values; and (B) Receiver

operating characteristic (ROC) curves and the area under the curve (AUC).

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4. Friberg L, Rosenqvist M, Lindgren A, et al. High prevalence of atrial fibrillation among patients with ischemic stroke. Stroke 2014;45(9):2599-605.

5. Camm AJ, Lip GY, De Caterina R, et al. 2012 focused update of the ESC Guidelines for the management of atrial fibrillation: an update of the 2010 ESC Guidelines for the management of atrial fibrillation. Developed with the special contribution of the European Heart Rhythm Association. European heart journal 2012;33(21):2719-47.

6. Fitzmaurice DA, Hobbs FD, Jowett S, et al. Screening versus routine practice in detection of atrial fibrillation in patients aged 65 or over: cluster randomised controlled trial. Bmj 2007;335(7616):383.

7. Kearley K, Selwood M, Van den Bruel A, et al. Triage tests for identifying atrial fibrillation in primary care: a diagnostic accuracy study comparing single-lead ECG and modified BP monitors. BMJ Open 2014;4(5):e004565.

8. Wiesel J, Arbesfeld B, Schechter D. Comparison of the Microlife blood pressure monitor with the Omron blood pressure monitor for detecting atrial fibrillation. The American journal of cardiology 2014;114(7):1046-8.

9. Wiesel J, Fitzig L, Herschman Y, et al. Detection of atrial fibrillation using a modified microlife blood pressure monitor. American journal of hypertension 2009;22(8):848-52.

10. Stergiou GS, Karpettas N, Protogerou A, et al. Diagnostic accuracy of a home blood pressure monitor to detect atrial fibrillation. Journal of human hypertension 2009;23(10):654-8.

11. Wiesel J, Wiesel D, Suri R, et al. The use of a modified sphygmomanometer to detect atrial fibrillation in outpatients. Pacing and clinical electrophysiology : PACE 2004;27(5):639-43.

12. Gandolfo C, Balestrino M, Bruno C, et al. Validation of a simple method for atrial fibrillation screening in patients with stroke. Neurol Sci 2015;36(9):1675-8.

13. Garabelli P, Albert D, Reynolds D. Accuracy and novelty of an inexpensive iPhone-based event recorder. Heart Rhythm Scientific Sessions 2012.

14. Lowres N, Neubeck L, Salkeld G, et al. Feasibility and cost effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost 2014;99(2):295-304.

15. January CT, Calkins H, FAHA F, et al. 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation. Circulation 2014;129:000-00.

16. Lip GY, Tse HF, Lane DA. Atrial fibrillation. Lancet 2012;379(9816):648-61. 17. Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent

stroke in patients who have nonvalvular atrial fibrillation. Annals of internal medicine 2007;146(12):857-67.

18. Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 2009;373(9665):739-45.

19. Hobbs FD, Fitzmaurice DA, Mant J, et al. A randomised controlled trial and cost-effectiveness study of systematic screening (targeted and total population screening) versus routine practice for the detection of atrial fibrillation in

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people aged 65 and over. The SAFE study. Health Technol Assess 2005;9(40):iii-iv, ix-x, 1-74.

20. Marazzi G, Iellamo F, Volterrani M, et al. Comparison of Microlife BP A200 Plus and Omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients. Advances in therapy 2012;29(1):64-70.

21. Verberk WJ, de Leeuw PW. Accuracy of oscillometric blood pressure monitors for the detection of atrial fibrillation: a systematic review. Expert Rev Med Devices 2012;9(6):635-40.

22. Willits I, Keltie K, Craig J, et al. WatchBP Home A for opportunistically detecting atrial fibrillation during diagnosis and monitoring of hypertension: a NICE Medical Technology Guidance. Applied health economics and health policy 2014;12(3):255-65.

23. Verberk WJ, Omboni S, Kollias A, et al. Screening for atrial fibrillation with automated blood pressure measurement: Research evidence and practice recommendations. International journal of cardiology 2015;203:465-73.

24. Kearley K, Selwood M, Van den Bruel A, et al. Triage tests for identifying atrial fibrillation in primary care: a diagnostic accuracy study comparing single-lead ECG and modified BP monitors. BMJ Open 2014;4(5).

25. Orchard J, Freedman SB, Lowres N, et al. iPhone ECG screening by practice nurses and receptionists for atrial fibrillation in general practice: the GP-SEARCH qualitative pilot study. Aust Fam Physician 2014;43(5):315-9.

26. Svennberg E, Engdahl J, Al-Khalili F, et al. Mass Screening for Untreated Atrial Fibrillation: The STROKESTOP Study. Circulation 2015;131(25):2176-84.

27. Jones C, Pollit V, Fitzmaurice D, et al. The management of atrial fibrillation: summary of updated NICE guidance. Bmj 2014;348:g3655.

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Figure 1

5,969 patients

underwent atrial

fibrillation screening

No atrial fibrillation

N=5,897

Atrial fibrillation

N=72

Cardiologists’

interpretation

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Figure 2

Sinus rhythm 91.59%

Atrial fibrillation 1.21%

PAC 2.86%

PVC 2.41%

Sinus arrhythmia 1.93%

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1.21%

0.51%

0.91%

2.71%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

Overall Age <65 Age 65-75 Age≥75

Figure 2

B

Pre

va

len

ce

of

Atr

ial F

ibri

lla

tio

n (

%)

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True AF

Mic

roli

fe A

fib

dia

gn

osed

AF

Rhythm diagnosis of 79 false positives

• 24 Sinus rhythm (30.4%)

• 23 PAC (29.1%)

• 4 PVC (5.1%)

• 28 Sinus arrhythmia (35.4%)

Yes No

+ 58 79

- 14 5,818

Rhythm diagnosis of 5,818 true negatives

• 5,443 Sinus rhythm (93.6%)

• 148 PAC (2.5%)

• 140 PVC (2.4%)


Figure 3

A

Overall

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True AF

Mic

roli

fe A

fib

dia

gn

osed

AF



• 4 PAC (20.0%)

• 1 PVC (5.0%)


Yes No

+ 11 20

- 2 2,512


• 2,410 Sinus rhythm (95.9%)

• 32 PAC (1.3%)

• 42 PVC (1.7%)


Figure 3

B

AGE <65 years

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True AF

Mic

roli

fe A

fib

dia

gn

osed

AF



• 3 PAC (16.7%)


Yes No

+ 13 18

- 4 1,842


• 1,738 Sinus rhythm (94.4%)

• 39 PAC (2.1%)

• 42 PVC (2.3%)


Figure 3

C

AGE 65-74 years

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True AF

Mic

roli

fe A

fib

dia

gn

osed

AF



• 16 PAC (39.0%)

• 3 PVC (7.3%)


Yes No

+ 34 41

- 8 1,464


• 1,295 Sinus rhythm (88.5%)

• 77 PAC (5.3%)

• 56 PVC (3.7%)


Figure 3

D

AGE ≥75 years

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81%

99%

42%

100%

85%

99%

35%

100%

77%

99%

42%

100%

81%

97%

45%

99%

0%

20%

40%

60%

80%

100%

120%

Sensitivity Specificity PPV NPV

Overall Age <65 Age 65-75 Age≥75

Figure 4

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0

20

40

60

80

100

0 20 40 60 80 100

AUC

------ Overall: 0.90, p<0.001

------ Age <65: 0.92, p<0.001

------ Age 65-74: 0.92, p<0.001

------ Age ≥75: 0.91, p<0.001

Sen

sit

ivit

y

100-Specificity Figure 4

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2, 3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4, 5

Objectives 3 State specific objectives, including any prespecified hypotheses 5

Methods

Study design 4 Present key elements of study design early in the paper 6

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

6

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 6

(b) For matched studies, give matching criteria and number of exposed and unexposed

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

6, 7

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

6

Bias 9 Describe any efforts to address potential sources of bias 6

Study size 10 Explain how the study size was arrived at 6

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

6

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 7

(b) Describe any methods used to examine subgroups and interactions 7

(c) Explain how missing data were addressed 7

(d) If applicable, explain how loss to follow-up was addressed 7

(e) Describe any sensitivity analyses 7

Results

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Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

8

(b) Give reasons for non-participation at each stage 8

(c) Consider use of a flow diagram 17

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

16

(b) Indicate number of participants with missing data for each variable of interest 8

(c) Summarise follow-up time (eg, average and total amount) 8

Outcome data 15* Report numbers of outcome events or summary measures over time 8, 9

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

8, 9

(b) Report category boundaries when continuous variables were categorized 8, 9

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period 8, 9

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses 9

Discussion

Key results 18 Summarise key results with reference to study objectives 10

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

13, 14

Generalisability 21 Discuss the generalisability (external validity) of the study results 13, 14

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

15

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Journal: BMJ Open

Manuscript ID bmjopen-2016-013685.R1


Date Submitted by the Author: 03-Jan-2017








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Care Setting










Number of Tables: 1




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2

ABSTRACT





healthcare setting.





ECG.


detection












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3


curves was 0.90 (95% CI: 0.89-0.90).


effective means to screen for AF with a high sensitivity of 80.6% and negative

predictive value of 99.8%, in addition to its routine function of BP

measurement. In a younger patient population aged <65 years with a lower

prevalence of AF, Microlife WatchBP Home demonstrated a similar diagnostic

accuracy.

Strength of study:






for AF screening





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5

INTRODUCTION










years or older.5 6

In addition to advanced age and diabetes mellitus, hypertension is another

important risk factor for AF,7 accounting for 14% of the AF burden in both men

and women.8 Hypertension contributes more AF cases than any other risk

factor because of its high prevalence (~1 billion individuals worldwide).

Different risk factors had various impacts on the development of incident AF;

for instance, hypertension had an odd ratio of 1.8 on 10-year risk of AF while

advanced age and diabetes mellitus had odd ratios of 2.3 and 1.1 respectively.





measurement in primary care patients aged 65 years or older.9 The ability to

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6

detect AF in the Microlife device is based on measuring the time interval

between successive R-R cycles and computing the ratio of the standard

deviation of these time intervals to the mean R-R intervals. If this irregularity

index generated is above certain cut-off, this would be interpreted as positive

for AF by the device.7
















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7

METHODS

Study Design










criteria.

Screening Procedure












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8


















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9

RESULTS




















of 72 AF patients and produced 79 false positive results (Figure 3). The


80.6% (95% CI: 69.5-88.9). The Microlife WatchBP Home produced a false

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10

positive result for AF in 79 of 5,897 non-AF patients with a corresponding

specificity of 98.7% (95% CI: 98.3-98.9). Amongst these 79 patients, 24 were

in sinus rhythm (30.4%); 28 had sinus arrhythmia (35.4%); 23 had premature

atrial contractions (29.1%); and 4 had premature ventricular contractions

(5.1%) (Figure 3). Nonetheless the specificity of the Microlife WatchBP Home

for AF detection remained high in these patients: 99.6% in patients with sinus

rhythm, 97.2% in patients with premature ventricular contractions, 86.5% in

patients with premature atrial contractions, and 75.7% in patients with sinus

arrhythmia. Given the relatively low prevalence of AF (1.21%) in this

population, the positive and negative predictive value of the Microlife

WatchBP Home to detect AF was 42.4% (95% CI: 34.0-51.2) and 99.8%

(95% CI: 99.6-99.9) respectively. The positive likelihood ratio and the negative

likelihood ratio of the Microlife WatchBP Home was 60.1 (95% CI: 47.0-77.0)

and 0.2 (95% CI: 0.1-0.3) respectively. The overall diagnostic performance of


curves was 0.90 (95% CI: 0.89-0.90).

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11

DISCUSSION







WatchBP Home machine detected AF with a high sensitivity of 80.6%, high

specificity of 98.7% and negative predictive value of 99.8%. Second, the

device detected AF with high diagnostic accuracy as determined by area

under the curves of 0.9, when compared with reference single-lead I ECG.

Third, the device effectively detected AF in patients younger than 65 years –

not the usual target population for AF screening. Finally, the diagnostic

performance of the device, in terms of sensitivity, specificity, positive and

negative predictive values, did not differ across different age groups with a

mean age of patients in this study of 67.2 ± 11.0 years.









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12










detection of pulse irregularities during BP measurement.7 9-11 By measuring

the time interval between successive R-R cycles and calculating the ratio of

the standard deviation of these time intervals to the mean R-R interval, an










for AF.7 9 10 20-23 Arrhythmia detectors installed in other automated blood


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study on Caucasian population and the head-to-head comparison study in the

primary care setting in Chinese population.26 27 This study utilized the Microlife

WatchBP Home device to detect AF, but it was only validated in a population

aged 65 years or above, the target population for AF screening. One of the

important findings in this study was that the diagnostic performance of

Microlife WatchBP Home machine in a younger patient population, which was

characterized by a lower prevalence of AF and other arrhythmias including

premature atrial complex, was not negatively affected. Current guidelines5 28

promote AF screening only in those aged ≥65 years, because of the lack of

clinical evidence and possibly higher prevalence of sinus arrhythmia and thus

false-positive results in younger subjects. The current study provides

evidence that the Microlife WatchBP Home machine achieves comparable

diagnostic accuracy in those aged <65 and those ≥65. This device is also the

first to be validated for AF screening in younger patients aged <65, potentially

extending the indication of UK NICE guideline for the Microlife WatchBP

Home machine. In this study, detection of AF during automated BP

measurement was feasible in the primary care setting and appeared to be

superior to routine pulse palpation in terms of diagnostic accuracy for AF,

hence facilitating AF screening in high-risk patients in the primary care setting.

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Importantly, as the incidence of AF in the general population is around 1-2%

depending on ethnicity, usually lower in Chinese population.29 30 Therefore, in

population with relatively low incidence of AF, the high sensitivity and negative

predictive value of the device for AF screening would be invaluable and ideal

for a screening tool.



















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15







Study limitation







Conclusion



high sensitivity of 80.6% and negative predictive value of 99.8% as well as

providing a routine function of blood pressure measurement. Diagnostic

accuracy of the Microlife WatchBP in a younger patient population aged <65

years and a lower prevalence of AF, achieved a similar diagnostic accuracy

compared with its use in an older population, thus potentially extending the

NICE guideline indication as an AF screening tool to a younger at-risk

population.

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FOOTNOTE




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17


Chu DW, Siu CW


- all authors








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19



(n = 5,969)


Male 2,751 (46.1)










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Figure Legends:





Figure 3. Contingency table for atrial fibrillation detection and rhythm



Home)

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21

References




















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27. Chan PH, Wong CK, Pun L, et al. Head-to-Head Comparison of the AliveCor Heart Monitor and Microlife WatchBP Office AFIB for Atrial Fibrillation Screening in a Primary Care Setting. Circulation 2017;135(1):110-12.


29. Go AS, Hylek EM, Phillips KA, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. Jama 2001;285(18):2370-5.

30. Zhou Z, Hu D. An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of mainland China. Journal of epidemiology / Japan Epidemiological Association 2008;18(5):209-16.

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Figure 1

127x95mm (300 x 300 DPI)

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Figure 2A

127x95mm (300 x 300 DPI)

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Figure 2B

127x95mm (300 x 300 DPI)

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Figure 3

127x95mm (300 x 300 DPI)

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Section/Topic Item




Introduction



Methods



collection

6




applicable

6, 7

Data sources/

measurement



6




why

6






Results

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8




confounders

16






8, 9




Discussion


Limitations



13, 14


Other information



15





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Journal: BMJ Open

Manuscript ID bmjopen-2016-013685.R2


Date Submitted by the Author: 16-Jan-2017








BMJ Open on A


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Care Setting










Number of Tables: 1




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ABSTRACT





healthcare setting.





ECG.


detection












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curves was 0.90 (95% CI: 0.89-0.90).


effective means to screen for AF with a reasonable sensitivity of 80.6% and a

high negative predictive value of 99.8%, in addition to its routine function of

BP measurement. In a younger patient population aged <65 years with a

lower prevalence of AF, Microlife WatchBP Home demonstrated a similar

diagnostic accuracy.

Strength of study:






for AF screening





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INTRODUCTION










years or older.5 6

In addition to advanced age and diabetes mellitus, hypertension is another

important risk factor for AF,7 accounting for 14% of the AF burden in both men

and women.8 Hypertension contributes more AF cases than any other risk

factor because of its high prevalence (~1 billion individuals worldwide).

Different risk factors had various impacts on the development of incident AF;

for instance, hypertension had an odd ratio of 1.8 on 10-year risk of AF while

advanced age and diabetes mellitus had odd ratios of 2.3 and 1.1 respectively.





measurement in primary care patients aged 65 years or older.9 The ability to

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detect AF in the Microlife device is based on measuring the time interval

between successive R-R cycles and computing the ratio of the standard

deviation of these time intervals to the mean R-R intervals. If this irregularity

index generated is above certain cut-off, this would be interpreted as positive

for AF by the device.7
















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METHODS

Study Design










criteria.

Screening Procedure












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7


















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RESULTS




















of 72 AF patients and produced 79 false positive results (Figure 3). The


80.6% (95% CI: 69.5-88.9). The Microlife WatchBP Home produced a false

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positive result for AF in 79 of 5,897 non-AF patients with a corresponding

specificity of 98.7% (95% CI: 98.3-98.9). Amongst these 79 patients, 24 were

in sinus rhythm (30.4%); 28 had sinus arrhythmia (35.4%); 23 had premature

atrial contractions (29.1%); and 4 had premature ventricular contractions

(5.1%) (Figure 3). Nonetheless the specificity of the Microlife WatchBP Home

for AF detection remained high in these patients: 99.6% in patients with sinus

rhythm, 97.2% in patients with premature ventricular contractions, 86.5% in

patients with premature atrial contractions, and 75.7% in patients with sinus

arrhythmia. Given the relatively low prevalence of AF (1.21%) in this

population, the positive and negative predictive value of the Microlife

WatchBP Home to detect AF was 42.4% (95% CI: 34.0-51.2) and 99.8%

(95% CI: 99.6-99.9) respectively. The positive likelihood ratio and the negative

likelihood ratio of the Microlife WatchBP Home was 60.1 (95% CI: 47.0-77.0)

and 0.2 (95% CI: 0.1-0.3) respectively. The overall diagnostic performance of


curves was 0.90 (95% CI: 0.89-0.90).

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DISCUSSION







WatchBP Home machine detected AF with a reasonable sensitivity of 80.6%,

high specificity of 98.7% and negative predictive value of 99.8%. Second, the

device detected AF with high diagnostic accuracy as determined by area

under the curves of 0.9, when compared with reference single-lead I ECG.

Third, the device effectively detected AF in patients younger than 65 years –

not the usual target population for AF screening. Finally, the diagnostic

performance of the device, in terms of sensitivity, specificity, positive and

negative predictive values, did not differ across different age groups with a

mean age of patients in this study of 67.2 ± 11.0 years.









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detection of pulse irregularities during BP measurement.7 9-11 By measuring

the time interval between successive R-R cycles and calculating the ratio of

the standard deviation of these time intervals to the mean R-R interval, an










for AF.7 9 10 20-23 Arrhythmia detectors installed in other automated blood


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study on Caucasian population and the head-to-head comparison study in the

primary care setting in Chinese population.26 27 This study utilized the Microlife

WatchBP Home device to detect AF, but it was only validated in a population

aged 65 years or above, the target population for AF screening. One of the

important findings in this study was that the diagnostic performance of

Microlife WatchBP Home machine in a younger patient population, which was

characterized by a lower prevalence of AF and other arrhythmias including

premature atrial complex, was not negatively affected. Current guidelines5 28

promote AF screening only in those aged ≥65 years, because of the lack of

clinical evidence and possibly higher prevalence of sinus arrhythmia and thus

false-positive results in younger subjects. The current study provides

evidence that the Microlife WatchBP Home machine achieves comparable

diagnostic accuracy in those aged <65 and those ≥65. This device is also the

first to be validated for AF screening in younger patients aged <65, potentially

extending the indication of UK NICE guideline for the Microlife WatchBP

Home machine. In this study, detection of AF during automated BP

measurement was feasible in the primary care setting and appeared to be

superior to routine pulse palpation in terms of diagnostic accuracy for AF,

hence facilitating AF screening in high-risk patients in the primary care setting.

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Importantly, as the incidence of AF in the general population is around 1-2%

depending on ethnicity, usually lower in Chinese population.29 30 Therefore, in

population with relatively low incidence of AF, the good sensitivity and high

negative predictive value of the device for AF screening would be invaluable

and ideal for a screening tool.
















Of note, the sensitivity of the device in this study is 80.6% (95% CI: 69.5-88.9),

which means there is probability that 2 to 3 out of 10 patients with underlying

AF could be missed by this screening tool. Physicians utilizing this device to

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screen for AF should be well aware of this potential drawback and should not

solely rely on this device and hence producing a false sense of security. The

possible ways to improve the sensitivity include repeated measurements with

Microlife device at intervals and combining the use of other screening tools in

AF detection.










Study limitation







Conclusion

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reasonable sensitivity of 80.6% and high negative predictive value of 99.8%

as well as providing a routine function of blood pressure measurement.

Diagnostic accuracy of the Microlife WatchBP in a younger patient population

aged <65 years and a lower prevalence of AF, achieved a similar diagnostic

accuracy compared with its use in an older population, thus potentially

extending the NICE guideline indication as an AF screening tool to a younger

at-risk population.

FOOTNOTE




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Chu DW, Siu CW


- all authors








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(n = 5,969)


Male 2,751 (46.1)










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Figure Legends:





Figure 3. Contingency table for atrial fibrillation detection and rhythm



Home)

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References




















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27. Chan PH, Wong CK, Pun L, et al. Head-to-Head Comparison of the AliveCor Heart Monitor and Microlife WatchBP Office AFIB for Atrial Fibrillation Screening in a Primary Care Setting. Circulation 2017;135(1):110-12.


29. Go AS, Hylek EM, Phillips KA, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. Jama 2001;285(18):2370-5.

30. Zhou Z, Hu D. An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of mainland China. Journal of epidemiology / Japan Epidemiological Association 2008;18(5):209-16.

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Figure 1

127x95mm (300 x 300 DPI)

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Figure 2A

127x95mm (300 x 300 DPI)

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Figure 2B

127x95mm (300 x 300 DPI)

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Figure 3

127x95mm (300 x 300 DPI)

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Section/Topic Item




Introduction



Methods



collection

6




applicable

6, 7

Data sources/

measurement



6




why

6






Results

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8




confounders

16






8, 9




Discussion


Limitations



13, 14


Other information



15





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