Diagnostic accuracy of a home blood pressure monitor to detect atrial fibrillation

Abstract

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with an increased long-term risk of stroke. A screening test for early diagnosis has the potential to prevent AF-related strokes. This study assessed the diagnostic accuracy of an automated device for self-home blood pressure (BP) monitoring, which implements an algorithm for AF detection. A modified, automated oscillometric device for self-home BP monitoring (Microlife BPA100 Plus, Microlife, Heerbrugg, Switzerland) with an AF detector was used to carry out triplicate BP measurements in subjects with sinus rhythm, AF and non-AF arrhythmias. During each BP measurement, the electrocardiogram (ECG) was recorded simultaneously. A total of 217 simultaneous BP measurements and ECG recordings were obtained from 73 subjects. Twenty-seven subjects (37%) had AF, 23 (31%) non-AF arrhythmias and 23 (31%) had sinus rhythm. A single measurement had 93% sensitivity and 89% specificity for detecting AF. For two measurements, in which one of them was required to detect AF, the sensitivity was 100% and specificity 76%, whereas for three measurements, in which two of them were required to detect AF, the sensitivity was 100% and specificity 89% (κ=0.86 for an agreement with ECG). Using the latter approach, there were five false positive cases all having irregularities in 50% of the heartbeats. In patients with tachyarrhythmia, the device underestimated heart rate. These data suggest that an electronic device for self-home BP monitoring, which implements an algorithm for AF diagnosis has an excellent diagnostic accuracy and might, therefore, be used as a reliable screening test for the early diagnosis.

Introduction

Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice.1, 2 In the last two decades, the prevalence of AF has increased considerably1, 2 and has been described as a ‘growing epidemic’.3 In the general population, the prevalence of AF is 0.5–1%, yet it is clearly related to age with 5% of subjects over the age of 65 suffering from AF in which 10% of subjects over 80.1, 2 Approximately, 70% of AF patients are older than 65 years.1, 2 Atrial fibrillation is associated with an increased long-term risk of stroke with one in every six strokes occurring in relation with it.1, 2 As AF is frequently asymptomatic, stroke is often the initial presentation that leads to AF detection. Therefore, screening programmes have attempted to address this important issue.4 With early diagnosis and effective anticoagulation, many AF-related strokes could be prevented.

Approximately 25–30% of the adults in the general population and more than 50% of those older than 65 years are hypertensives.5, 6 Devices for self-monitoring blood pressure (BP) by patients at home are used widely in the community in several countries, and numerous hypertensive societies have endorsed the use of this method for long-term follow-up of treated hypertension.7, 8 An algorithm that assesses pulse irregularity and applies a threshold for detecting AF, during routine BP measurement, has been integrated in a home BP monitor.9 Preliminary evidence shows that this method has a good diagnostic ability for AF detection.9, 10 Such a screening tool for AF, in the population, has considerable potential for the early detection and management of AF and thereby for stroke prevention.

This study was designed to assess the diagnostic accuracy of an automated device for self-home BP monitoring for detecting AF among subjects with sinus rhythm, sustained AF and other non-AF arrhythmias.

Subjects and methods

Participants

Subjects with known sustained AF, or other non-AF arrhythmias, and controls with sinus rhythm were recruited among those attending an Outpatients Hypertension Clinic, patients admitted in a University Department of Medicine wards and healthy volunteers. Exclusion criteria were age <35 years, presence of a pacemaker, and/or an implanted defibrillator and refusal to participate.

Device

An automated oscillometric device for self-home BP monitoring, which has been validated earlier for BP measurement accuracy,11 and an additional function, which allows AF detection during routine BP measurement, has been developed (Microlife BPA100 Plus, Microlife, Heerbrugg, Switzerland). Atrial fibrillation is detected during the usual BP recording by the application of an in-built algorithm, which analyses the irregularity of the pulse rate.9 The average time interval of the last 10 beats, during deflation, is calculated and intervals that are 25% shorter or longer than that of the average are discarded. The mean of the remaining intervals is calculated with its s.d., and an AF diagnosis is made, if the s.d. per mean ratio is >0.06.9 Four devices were donated by the manufacturer for carrying out this study.

Procedure

A medical history and a baseline 12-lead electrocardiogram (ECG) were obtained from each participant. Triplicate BP measurements were then taken after at least a 5-min rest in the lying position and with at least 30 s between measurements, using the tested home BP monitor with the AF detector. Simultaneously during the deflation phase of each BP measurement (when the AF detector of the device operates), the ECG was recorded continuously (one lead with a clear appearance of p-waves selected from the individual's baseline ECG). When an error occurred in BP measurement, this was repeated in order to obtain three sets of measurements with the corresponding ECGs per participant. The systolic and diastolic BP values and pulse rates measured by the device and the AF diagnosis carried out (AF, yes/no), were recorded for each measurement. The ECG heart rate and diagnosis of AF or other arrhythmias at baseline, and during each measurement, were made by one of the investigators and verified by an expert cardiologist. The protocol was approved by the hospital scientific committee and signed informed consent was obtained from all participants.

Analysis

The sensitivity, specificity and kappa-statistic for the AF diagnosis, carried out using the tested device and taking the ECG diagnosis of AF as reference method, were assessed for individual measurements and also for duplicate or triplicate measurements. Student's t-tests and one-way analysis of variance were used for the comparison of continuous variables in two or more groups of subjects, respectively, and the chi-squared tests were used for categorical variables. Paired t-tests were used to compare continuous variables in the same subjects. Statistical analysis was carried out using the MINITAB INC Statistical Software (Release 13.31). A probability value of P<0.05 was considered statistically significant.

Results

A total of 73 subjects were recruited and all were included in the analysis. Twenty-seven (37%) had AF, 23 (31%) non-AF arrhythmias and 23 had (31%) sinus rhythm (Table 1). The ECG showed AF during all three BP measurements in 27 subjects (two subjects had two measurements), sinus rhythm during all three measurements in 23 subjects and non-AF arrhythmias during all three measurements in 16 subjects. Seven subjects (10%) had a change in the ECG rhythm during their three BP measurements. Four subjects had non-AF arrhythmia during two BP measurements and sinus rhythm during the third one; two subjects had sinus rhythm during two measurements and non-AF arrhythmia during the third; and one subject had non-AF arrhythmia during two measurements and AF during the third one (the latter subject was excluded from ‘cases’ analysis because of intermittent AF).

Table 1 Characteristics of participants with atrial fibrillation, other arrhythmias and sinus rhythm

Overall, 217 simultaneous BP measurements and ECG recordings were obtained (two subjects had two readings instead of three). The ECG showed sinus rhythm during 77 BP measurements, AF during 80 and non-AF arrhythmia during 60 measurements. With regard to the AF diagnosis, 77 of the 80 BP measurements obtained while the ECG showed AF, were effectively detected as AF by the tested device (three were missed). Furthermore, 76 of the 77 BP measurements obtained while ECG showed sinus rhythm, were correctly diagnosed by the tested device (no AF), and one was misdiagnosed as AF. Finally, of the 60 measurements obtained, while the ECG showed non-AF arrhythmia, 21 (35%) were misdiagnosed as AF by the tested device.

The evaluation of the diagnostic value of the AF detector is presented in Table 2. When the diagnosis was based on a single measurement (all individual readings, n=217 or only the first one of each subject, n=72), the sensitivity of the device for diagnosing AF was >90% and the specificity >80% with the kappa-statistic suggesting a substantial agreement (Table 2). When the first two or all three measurements were taken into account and only one measurement was needed to diagnose AF, the sensitivity was 100% but the specificity <80%. Finally, when all three measurements were taken into account and two of them were required to diagnose AF, the sensitivity was 100% and specificity 89% with the kappa-statistic suggesting an almost perfect agreement (Table 2).

Table 2 Diagnostic value of the atrial fibrillation detector by basing the diagnosis on different measurements and taking ECG diagnosis as reference

Applying the optimal diagnostic approach (three measurements taken and at least two of them needed to diagnose AF), five cases were misdiagnosed as AF by the tested device. All these patients had non-AF arrhythmias in all their three measurements, which were misdiagnosed by the device as AF in all their three measurements (three subjects) or in two of their three measurements (two subjects). In these cases, the ECG during the BP measurements showed irregular RR intervals in 50% of the beats (range: 24–72%).

The ECG-calculated heart rate was on average 6 b.p.m. (beats per minute) higher than the pulse rate measured using the tested device in subjects with AF and in those with non-AF arrhythmia, but not in those with sinus rhythm (Table 2). Among subjects with AF or non-AF arrhythmia, those with a 10-b.p.m. pulse rate, underestimation by the tested device had a faster ECG heart rate (97.0±28.3 (s.d.) b.p.m.) compared with those with <10-b.p.m. difference (74.8±17.4 b.p.m., P<0.001).

Discussion

This prospective study assessed the diagnostic accuracy of an automated device for self-home BP monitoring in detecting AF. Overall, the device appeared to have a good diagnostic value, and even a single measurement achieved agreement with ECG diagnosis in more than 80% of the cases. For triplicate BP measurements, in which two of them were required to make the diagnosis of AF, the device has an almost perfect agreement with ECG diagnosis (93%) with 100% sensitivity and 89% specificity.

Until recently, the diagnosis of AF was only made on the basis of an ECG. This method was regarded as a very accurate one, but proved to be so only when carried out by a specialist. This is shown clearly in a study that compared the AF diagnosis, made by general practitioners, and a computer software algorithm using a 12-lead ECG, versus a reference diagnosis made by two cardiologists.12 The general practitioners’ diagnosis proved to be imperfect with a sensitivity of 80% and specificity of 92%. When taking into account the general practitioner's diagnosis together with the interpretative software (either or both positive), the diagnostic performance was improved, but only reached a sensitivity of 92% and specificity of 91%.12 The investigators concluded that many primary care professionals cannot accurately detect AF on an ECG, even when helped by an interpretative software.12 Self-diagnosis by patients of the pulse irregularity as a screening test for the detection of AF has been evaluated in a community education program with 6203 participants.13 Unfortunately, 27% of the trained participants could not find their pulse, and of those who did, 9% could not tell whether it was irregular.13

Owing to the widespread devices for self-home BP monitoring in the community, the idea that such devices also monitor pulse rate by implementing specific algorithms that are able to screen for arrhythmias is challenging. Early arrhythmia detectors integrated in home BP monitors picked up any pulse rate irregularity (even occasional ectopic beats) and could not distinguish between AF and other arrhythmias. Thus, their specificity for AF diagnosis is unacceptably low.

Wiesel et al.9 developed an algorithm for AF detection during routine BP measurement, which has been integrated in the home BP monitor tested in this study. The first study that assessed the diagnostic accuracy of an AF-detecting home BP monitor (modified Omron 712C) included 450 subjects, of whom 54 had AF on ECG.9 When single BP readings were used, the sensitivity of the device for diagnosing AF was 100% and the specificity 84%, whereas for two readings (diagnosis made only if both were positive), the sensitivity and specificity were 100 and 91%, respectively.9 A recent study by the same group tested the same device as in this study in 205 subjects, of whom 52 had AF on ECG.10 The sensitivity and specificity of a single measurement for detecting AF was 98 and 88%, respectively. For three measurements, in which two of them were required to diagnose AF, the diagnostic accuracy was improved (sensitivity, 100%; specificity, 89%),10 that is identical with the finding of this study (Table 2). The feasibility of using the device by patients at home for detecting intermittent AF has been tested in a small study of 19 patients with a history of AF.14 Self-monitoring at home, once per day for a period from 5 days to 5 months, seven patients with recurrent AF were identified by the monitor.14

The important features of this study that ensured a comprehensive assessment of the diagnostic accuracy of the AF detector are, first, that all BP measurements (and AF detector operation) were carried out simultaneously with continuous ECG recording, and, second, that three distinct groups of subjects (sinus rhythm, AF and non-AF arrhythmia) were investigated. This study design allowed the detection of changes in rhythm during the triplicate BP measurements, which actually occurred in 10% of the participants. In addition, the performance of the AF detector was tested in rather difficult diagnostic conditions, because of the inclusion of a group of subjects with non-AF arrhythmias. We hypothesized that a false positive diagnosis of AF might be common in the latter group. Such a drawback (poor specificity) of this screening method would often cause the users to be alarmed unnecessarily.

Interestingly, even in these more complex diagnostic conditions, the device proved to have a good diagnostic value. In line with the findings of Wiesel et al.,10 and after testing several approaches including single, duplicate and triplicate measurements, and one of two, one of three and two of three positive measurements needed for diagnosis, these data confirmed that triplicate measurements, in which two of them were required to make an AF diagnosis, is the optimal approach in terms of diagnostic accuracy (Table 2). In fact this screening tool appears to be more accurate in diagnosing AF than the general practitioners’ diagnosis carried out by ECG together with an interpretative software.12

Even after applying the optimal diagnostic approach (two of the three measurements needed for diagnosis), the tested device overdiagnosed AF in five patients. All these subjects had non-AF arrhythmias with irregular RR intervals in 50% of the beats, which were regarded as clinically important non-AF arrhythmias. Thus, even in these cases with a false positive AF diagnosis, patients are not alarmed unnecessarily by the device because they may benefit from medical consultation.

As this device is designed to detect AF by assessing the consistency of pulse rate irregularity, it cannot detect atrial flutter. On the other hand, the device will not alert patients with sporadic ectopic beats, which is a considerable improvement compared with that of other arrhythmia detectors implemented in the current oscillometric manometers. At present, and based on the prevalence of these arrhythmias and the associated cardiovascular risk, this technology seems to offer the optimal screening method.

Another issue raised by this study is that in case of arrhythmia (AF or other), heart rate is underestimated by the device, particularly in cases with tachyarrhythmia. Given the recent evidence that validated, automated BP monitors give accurate BP measurements in patients with AF and can be used in everyday clinical practice,15, 16 this issue deserves special attention.

In conclusion, this study showed that a home BP monitor with an integrated algorithm for AF diagnosis appears to have a very good diagnostic accuracy, which might be superior to that of a general practitioner. The widespread application of this technology in devices for routine home BP monitoring in the community appears to be an excellent screening test, yet its ability to detect intermittent AF requires further investigation.14 Subjects with AF, detected by the device, should consult their physician as soon as possible in order to confirm the AF diagnosis by ECG before a decision for pharmacologic intervention is made.

References

  1. 1

    Fuster V, Rydén LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA et al. American College of Cardiology/American Heart Association Task Force on Practice Guidelines; European Society of Cardiology Committee for Practice Guidelines; European Heart Rhythm Association; Heart Rhythm Society. ACC/AHA/ESC 2006 Guidelines for the Management of Patients with Atrial Fibrillation. Circulation 2006; 114: e257–e354.

  2. 2

    Dewar RI, Lip GY . Guidelines Development Group for the NICE clinical guideline for the management of atrial fibrillation. Identification, diagnosis and assessment of atrial fibrillation. Heart 2007; 93: 25–28.

  3. 3

    Lip GY, Kakar P, Watson T . Atrial fibrillation—the growing epidemic. Heart 2007; 93: 542–543.

  4. 4

    Fitzmaurice DA, Hobbs FD, Jowett S, Mant J, Murray ET, Holder R et al. Screening versus routine practice in detection of atrial fibrillation in patients aged 65 or over: cluster randomised controlled trial. BMJ 2007; 335: 383–386.

  5. 5

    Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J . Global burden of hypertension: analysis of worldwide data. Lancet 2005; 365: 217–223.

  6. 6

    Kannel WB . Prevalence, incidence, and hazards of hypertension in the elderly. Am Heart J 1986; 112: 1362–1363.

  7. 7

    Parati G, Stergiou GS, Asmar R, Bilo G, de Leeuw P, Imai Y et al. ESH Working Group on Blood Pressure Monitoring. European Society of Hypertension guidelines for blood pressure monitoring at home: a summary report of the Second International Consensus Conference on Home Blood Pressure Monitoring. J Hypertens 2008; 26: 1505–1526.

  8. 8

    Pickering TG, Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D . American Heart Association; American Society of Hypertension; Preventive Cardiovascular Nurses Association. Call to action on use and reimbursement for home blood pressure monitoring: executive summary: a joint scientific statement from the American Heart Association, American Society of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertension 2008; 52: 1–9.

  9. 9

    Wiesel J, Wiesel D, Suri R, Messineo FC . The use of a modified sphygmomanometer to detect atrial fibrillation in outpatients. Pacing Clin Electrophysiol 2004; 27: 639–643.

  10. 10

    Wiesel J, Herschman Y, Messinao FC . Detection of atrial fibrilation using a Microlife automatic blood pressure monitor (abstract). J Clin Hypertens 2008; 10 (suppl A): A84–A85.

  11. 11

    Stergiou GS, Giovas PP, Neofytou MS, Adamopoulos DN . Validation of the Microlife BPA100 Plus device for self-home blood pressure measurement according to the International Protocol. Blood Press Monit 2006; 11: 157–160.

  12. 12

    Mant J, Fitzmaurice DA, Hobbs FD, Jowett S, Murray ET, Holder R et al. Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial. BMJ 2007; 335: 380–385.

  13. 13

    Munschauer FE, Sohocki D, Carrow SS, Priore R . A community education program on atrial fibrillation: implications of pulse self-examination on awareness and behavior. J Stroke Cerebrovasc Dis 2004; 13: 208–213.

  14. 14

    Wiesel J, Wiesel DJ, Messineo FC . Home monitoring with a modified automatic sphygmomanometer to detect recurrent atrial fibrillation. J Stroke Cerebrovasc Dis 2007; 16: 8–13.

  15. 15

    Jani B, Bulpitt CJ, Rajkumar C . The accuracy of blood pressure measurement in atrial fibrillation. J Hum Hypertens 2006; 20: 543–545.

  16. 16

    Watson T, Lip GY . Blood pressure measurement in atrial fibrillation: goodbye mercury? J Hum Hypertens 2006; 20: 638–640.

Download references

Acknowledgements

This study was funded by the Hypertension Center, Third University Department of Medicine, Athens.

Author information

Correspondence to G S Stergiou.

Additional information

Conflict of interest

The Hypertension Center has received grants from the manufacturer of the tested device (Microlife) for other research activities. GSS was a consultant to Microlife for the design of other blood pressure monitors. Microlife was not involved in the esign of this study.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Stergiou, G., Karpettas, N., Protogerou, A. et al. Diagnostic accuracy of a home blood pressure monitor to detect atrial fibrillation. J Hum Hypertens 23, 654–658 (2009). https://doi.org/10.1038/jhh.2009.5

Download citation

Keywords

  • atrial fiblillation
  • diagnosis
  • screening
  • blood pressure measurement
  • self-home monitoring

Further reading