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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
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
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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: cwdsiu@hku.hk
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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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References
1. Chugh SS, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014;129(8):837-47.
2. Wolf PA, Kannel WB, McGee DL, et al. Duration of atrial fibrillation and imminence of stroke: the Framingham study. Stroke 1983;14(5):664-7.
3. Siu CW, Lip GY, Lam KF, et al. Risk of stroke and intracranial hemorrhage in 9727 Chinese with atrial fibrillation in Hong Kong. Heart rhythm : the official journal of the Heart Rhythm Society 2014;11(8):1401-8.
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%
A Page 22 of 31
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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%)
• 87 Sinus arrhythmia (1.5%)
Figure 3
A
Overall
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True AF
Mic
roli
fe A
fib
dia
gn
osed
AF
Rhythm diagnosis of 20 false positives
• 10 Sinus rhythm (50.0%)
• 4 PAC (20.0%)
• 1 PVC (5.0%)
• 5 Sinus arrhythmia (25.0%)
Yes No
+ 11 20
- 2 2,512
Rhythm diagnosis of 2,512 true negatives
• 2,410 Sinus rhythm (95.9%)
• 32 PAC (1.3%)
• 42 PVC (1.7%)
• 28 Sinus arrhythmia (1.1%)
Figure 3
B
AGE <65 years
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True AF
Mic
roli
fe A
fib
dia
gn
osed
AF
Rhythm diagnosis of 18 false positives
• 8 Sinus rhythm (44.4%)
• 3 PAC (16.7%)
• 7 Sinus arrhythmia (38.9%)
Yes No
+ 13 18
- 4 1,842
Rhythm diagnosis of 1,842 true negatives
• 1,738 Sinus rhythm (94.4%)
• 39 PAC (2.1%)
• 42 PVC (2.3%)
• 23 Sinus arrhythmia (1.2%)
Figure 3
C
AGE 65-74 years
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True AF
Mic
roli
fe A
fib
dia
gn
osed
AF
Rhythm diagnosis of 41 false positives
• 6 Sinus rhythm (14.7%)
• 16 PAC (39.0%)
• 3 PVC (7.3%)
• 16 Sinus arrhythmia (39.0%)
Yes No
+ 34 41
- 8 1,464
Rhythm diagnosis of 1,464 true negatives
• 1,295 Sinus rhythm (88.5%)
• 77 PAC (5.3%)
• 56 PVC (3.7%)
• 36 Sinus arrhythmia (2.5%)
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
B Page 29 of 31
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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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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.R1
Article Type: Research
Date Submitted by the Author: 03-Jan-2017
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
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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: 3
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: cwdsiu@hku.hk
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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 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:
• 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 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.
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 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
Although the diagnostic performance of the device for AF detection has been
previously investigated,7-12 these studies have been limited by their relatively
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,7 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 3). The
corresponding sensitivity of the Microlife WatchBP Home to detect AF was
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
the Microlife WatchBP Home to detect AF as determined by area under the
curves was 0.90 (95% CI: 0.89-0.90).
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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%, 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.
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
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risk factors.18 To achieve effective AF screening, the availability of a reliable
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.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
irregularity index is generated. Previous study confirmed that an irregularity
index with cut-off >0.06 corresponds to AF with high sensitivity and
specificity.7 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.7 9 10 20-23 Arrhythmia detectors installed in other automated blood
pressure monitors provide only a warning that the blood pressure recorded
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may be inaccurate due to the possible presence of arrhythmia, rather than
specifically diagnosing AF.24
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 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.
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
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
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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
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 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
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 table 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)
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References
1. Chugh SS, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014;129(8):837-47.
2. Wolf PA, Kannel WB, McGee DL, et al. Duration of atrial fibrillation and imminence of stroke: the Framingham study. Stroke 1983;14(5):664-7.
3. Siu CW, Lip GY, Lam KF, et al. Risk of stroke and intracranial hemorrhage in 9727 Chinese with atrial fibrillation in Hong Kong. Heart rhythm : the official journal of the Heart Rhythm Society 2014;11(8):1401-8.
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. 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.
8. 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.
9. 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.
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. 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.
28. Jones C, Pollit V, Fitzmaurice D, et al. The management of atrial fibrillation: summary of updated NICE guidance. Bmj 2014;348:g3655.
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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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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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.R2
Article Type: Research
Date Submitted by the Author: 16-Jan-2017
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
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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: 3
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: cwdsiu@hku.hk
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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 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:
• 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 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.
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 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
Although the diagnostic performance of the device for AF detection has been
previously investigated,7-12 these studies have been limited by their relatively
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,7 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 3). The
corresponding sensitivity of the Microlife WatchBP Home to detect AF was
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
the Microlife WatchBP Home to detect AF as determined by area under the
curves was 0.90 (95% CI: 0.89-0.90).
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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 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.
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
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risk factors.18 To achieve effective AF screening, the availability of a reliable
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.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
irregularity index is generated. Previous study confirmed that an irregularity
index with cut-off >0.06 corresponds to AF with high sensitivity and
specificity.7 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.7 9 10 20-23 Arrhythmia detectors installed in other automated blood
pressure monitors provide only a warning that the blood pressure recorded
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may be inaccurate due to the possible presence of arrhythmia, rather than
specifically diagnosing AF.24
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 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.
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
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.
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.
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
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
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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
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
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 table 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)
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References
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2. Wolf PA, Kannel WB, McGee DL, et al. Duration of atrial fibrillation and imminence of stroke: the Framingham study. Stroke 1983;14(5):664-7.
3. Siu CW, Lip GY, Lam KF, et al. Risk of stroke and intracranial hemorrhage in 9727 Chinese with atrial fibrillation in Hong Kong. Heart rhythm : the official journal of the Heart Rhythm Society 2014;11(8):1401-8.
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. 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.
8. 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.
9. 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.
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. 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.
28. Jones C, Pollit V, Fitzmaurice D, et al. The management of atrial fibrillation: summary of updated NICE guidance. Bmj 2014;348:g3655.
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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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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