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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 6  |  Issue : 2  |  Page : 52-55

Diagnostic accuracy of apex-pulse deficit for detecting atrial fibrillation


Department of Cardiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India

Date of Submission19-Apr-2019
Date of Acceptance02-Nov-2019
Date of Web Publication02-Jan-2020

Correspondence Address:
Raja J Selvaraj
Department of Cardiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Dhanvantri Nagar, Puducherry - 605 006
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAMR.IJAMR_48_19

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  Abstract 


Background: Screening for asymptomatic atrial fibrillation (AF) can identify patients at risk of stroke and help initiate treatment. Apex-pulse deficit, the difference between apex beat rate and peripheral pulse rate, has been described as a clinical sign to identify AF. However, the accuracy of this measure to identify AF has not been studied before. Aims: The primary aim of this study was to determine the sensitivity and specificity of apex-pulse deficit more than 10, measured over 1 min, to identify AF using 12-lead electrocardiogram (ECG) as the gold standard. Methods: This was a prospective cross-sectional study. Subjects were those above 30 years of age with known AF (cases) or not in AF (controls). Apex-pulse deficit was measured in each of them and correlated with rhythm detected in 12-lead ECG. Results: A total of 70 patients were studied, 35 cases and 35 controls. Apex-pulse deficit was significantly larger for cases as compared to controls and was a good discriminant to identify AF. Receiver operating characteristic curve analysis showed an area under the curve of 0.86. With a cutoff of 10, sensitivity and specificity to identify AF were 62.8% and 85.7%, respectively. Using a cutoff of 5 increased the sensitivity to 80%. Counting over 30 s was significantly less accurate than counting over one full minute. Conclusion: Apex-pulse deficit is a low-cost method to identify AF and may be useful for screening. A cutoff of 5 may enhance the sensitivity of measurement as compared to the traditional cutoff of 10.

Keywords: Atrial fibrillation, pulse deficit, screening


How to cite this article:
Rajkumar A, Bhattacharjee A, Selvaraj RJ. Diagnostic accuracy of apex-pulse deficit for detecting atrial fibrillation. Int J Adv Med Health Res 2019;6:52-5

How to cite this URL:
Rajkumar A, Bhattacharjee A, Selvaraj RJ. Diagnostic accuracy of apex-pulse deficit for detecting atrial fibrillation. Int J Adv Med Health Res [serial online] 2019 [cited 2020 Jul 11];6:52-5. Available from: http://www.ijamhrjournal.org/text.asp?2019/6/2/52/274627




  Introduction Top


Atrial fibrillation (AF) is the most common sustained disorder of heart rhythm.[1] Normally, the atria are activated by regular pacemaker impulses generated by the sinoatrial node. In AF, there is rapid, chaotic activation of the atria. This results in an irregular and often rapid heartbeat.

AF is present in 1%–4% of the population in the West[2] and becomes more prevalent with increasing age. AF often produces symptoms from the fast heart rate or heart failure, resulting in the patients seeking treatment. However, AF can also be asymptomatic. AF is associated with a risk of stroke due to the formation of clots in the heart, and this risk is the same in asymptomatic patients also. About 25% of stroke patients have been diagnosed with asymptomatic AF.[3] Although the evidence is not clear at present if screening for asymptomatic patients with AF will provide a net benefit, asymptomatic patients are also at risk of stroke. If these asymptomatic patients are identified by screening, treatment with antiplatelet or anticoagulant drugs, especially in patients with additional risk factors can reduce the risk of stroke.

Various approaches have been used to screen for AF at a single time point. Devices have been developed to detect AF by identifying pulse irregularity, and these include blood pressure monitors with AF detection function.[4] Opportunistic detection of AF using such blood pressure monitors has been proved to have reasonable sensitivity and specificity.[5] Smartphone applications with pulse detection by finger photoplethysmography have also been used.[6] These are, however, expensive, especially if required in large numbers for community screening, and require training for use. Low technology methods such as palpation of pulse by a trained nurse to detect irregularity have been employed[7] but require training and are subjective. AF can be detected in an electrocardiogram (ECG) and this remains the gold standard. For the purposes of screening, a 3-lead or single-lead ECG has been tried; however, the need for equipment and trained personnel for the interpretation has precluded its use for widespread community screening purposes in the background of insufficient evidence for screening.

An ideal screening technique must satisfy three important criteria – it must be rapid and preferably noninvasive, it must be available at low cost, and minimal training is desirable with a view to cover large populations, including remote areas. Apex-pulse deficit has been described as a clinical sign in patients with AF. This is the difference between the heart rate counted from heart sounds (in terms of apex beat) and peripheral pulse palpated at the radial artery. The deficit is seen in AF because some of the heartbeats which are audible do not produce a palpable pulse owing to the irregularity. A deficit of 10 or more has been described to indicate AF.[8] However, the sign has not been clinically validated, and the accuracy to identify AF is not known. We designed this study to determine the diagnostic accuracy of apex-pulse deficit to identify AF as compared to the gold standard of a 12-lead ECG.


  Methods Top


This was a prospective cross-sectional study among patients attending the cardiology outpatient department in a tertiary care center in southern India. The study was approved by the Institute Ethics Committee (no: JIP/lEC/2018/0203) and informed consent was obtained from all participants.

Inclusion criteria

  1. Patients above the age of 30 years, with known AF (cases)
  2. Patients above the age of 30 years, attending the cardiology outpatient clinic with conditions such as hypertension or coronary artery disease but not in AF (controls).


For each patient, informed consent was first obtained. Apex-pulse deficit was measured as described below by two operators. Immediately following this, a 12-lead ECG with a rhythm strip was recorded. Care was taken to avoid a delay longer than 5 min between the calculation of apex-pulse deficit and recording of the ECG.

Measurement of apex-pulse deficit

Two operators, both 3rd year medical students, performed the measurements for all the patients after brief training. With the patient seated, operator 1 used a stethoscope to listen to the heart sounds, while operator 2 palpated the radial pulse. Once both the operators were ready, a timer was used to start counting simultaneously. Heart rate counted by each operator was recorded at the end of 30 s. The procedure was then repeated and the heart rate was recorded at the end of 1 min. The two operators interchanged their roles in different subjects to avoid bias. Apex-pulse deficit was calculated as the difference between the rates counted by the two operators at the end of 1 min. The apex-pulse deficit was also derived from the 30-s recording by multiplying the difference by 2. In addition, each operator also designated the rhythm as regular or irregular.

Analysis

The ECG was read by a cardiologist to identify the rhythm as AF or not AF. The use of an apex-pulse deficit measurement of more than 10 to identify AF was compared with this gold standard method. Based on these results, sensitivity and specificity of the apex-pulse deficit to identify AF were calculated. For the secondary endpoints, (1) the apex-pulse deficit calculated by doubling the difference at 30 s was compared with the measurement at 1 min and (2) receiver operating characteristic (ROC) curve analysis was performed to identify the optimal cut-off of apex-pulse deficit to identify AF.

With a case-to-control ratio of 1:1, we estimated that a minimum sample size of 66 patients will be required to provide a power of 0.9 to detect an area under the curve of 0.7 using ROC curve analysis.


  Results Top


A total of 70 patients were studied, 35 in sinus rhythm and 35 in AF. The baseline characteristics of the patients are shown in [Table 1].
Table 1: Baseline characteristics of the patients

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The mean heart rate (counted from apex beat over 1 min) was 81 ± 15 bpm in cases and 90 ± 20 bpm in the controls. Among the cases, eight patients had a heart rate of more than or equal to 100 bpm, while this was present in five patients from the control group.

Apex pulse deficit measured at 30 s and at 60 s was significantly larger for cases as compared to controls. Irregular pulse by palpation or auscultation was also more frequently noted in patients with AF [Table 2]. None of the patients in the control group had premature atrial or ventricular beats on the ECG.
Table 2: Comparison of apex-pulse deficit in the control group (sinus) versus atrial fibrillation

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The analysis of apex-pulse deficit measured at 60 s to identify AF showed an area under the ROC curve of 0.86. The ROC curve is shown in [Figure 1]. The analysis of apex-pulse deficit measured at 30 s to identify AF showed an area under the ROC curve of 0.75. The sensitivity and specificity for pulse irregularity and different cutoffs of apex-pulse deficit are listed in [Table 3] and [Table 4] respectively. The positive predictive value and negative predictive value of apex-pulse deficit of 10 or more to identify AF were 81.5% and 69.8%, respectively.
Figure 1: Receiver operating characteristic curve of apex-pulse deficit to identify atrial fibrillation

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Table 3: Irregular pulse detection

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Table 4: Sensitivity and specificity of apex-pulse deficit and irregular pulse on auscultation and palpation

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  Discussion Top


In this study, we assessed the diagnostic accuracy of apex-pulse deficit counted over a minute to identify AF. We found that an apex-pulse deficit of 10 or more counted over 1 min identified AF with reasonable accuracy. Although apex-pulse deficit has been described as a clinical sign to identify AF, its diagnostic accuracy has not been studied before. This assumes importance in the current era where screening for asymptomatic AF is assuming importance given the potential to reduce strokes. The sensitivity of 62.8% and specificity of 85.7% suggest that it is a reasonably accurate method to identify AF. This makes it a promising approach for screening of AF in the community since it requires minimal training, is cheap, and takes only a couple of minutes to perform.

For a screening test, it is preferable to have a high sensitivity, even at the cost of loss of some specificity to reduce false negatives. Our ROC curve analysis suggests that a cutoff of 5 will improve the sensitivity to 80%, while the specificity is still 80%. This cutoff may be preferable for the use for screening.

We explored if counting for 30 s may be sufficient instead of for a full minute as this will significantly reduce the time taken for a measurement. However, this was less accurate to identify AF.

Apex-pulse deficit is likely to have reduced sensitivity to identify AF when the heart rate is well controlled. In our patients with AF, among the lowest tertile of heart rate (heart rate ≤78 bpm), only 15.4% had an apex-pulse deficit more than 10, whereas in the highest tertile (heart rate >90 bpm), 100% had an apex-pulse deficit more than 10. While this assumes significance in a hospital setting where the patients are already diagnosed with AF and are on treatment to control the ventricular rate, it may be less significant in a community screening of previously undiagnosed patients. If used for screening, an additional method of screening may be considered in patients with a controlled ventricular rate.

The sensitivity and specificity of irregular pulse/heartbeat detection seem to be better than the apex-pulse deficit. Similar studies using trained nurses have shown sensitivity ranging from 91% to 100%, while specificity ranged from 70% to 77%.[9] However, this is a subjective measure, and it is unclear if the accuracy in the identification of irregular pulse achieved in this study by medical students and in other studies by nurses would apply to other health-care workers.

Smartphone applications have been developed to screen for AF. A smartphone application using photoplethysmography to identify the pulse can help in screening for AF, according to late-breaking results from the DIGITAL-AF study presented at European Society of Cardiology (ESC) congress.[10] Blood pressure monitors have also been used for opportunistic detection and screening for AF and have generally shown sensitivity >85% and specificity >90%.[4] Our study looked at a low-cost, low-technology option that may be more applicable for large-scale screening.

In addition to evaluating apex-pulse deficit as a screening tool, our study also has value in being the first time, this classically described clinical sign in AF is evaluated in an evidence-based fashion. Our findings suggest that while this could be used as a clinical sign to identify AF with reasonable accuracy, the detection of an irregular pulse itself may have equal or better accuracy.

Limitations

A significant limitation of the study is the use of a two-gate design where patients with previously diagnosed AF were used as cases, while those without diagnosed AF were controls. Sensitivity and specificity measured from the study may not accurately reflect the sensitivity and specificity of apex-pulse deficit when used for screening in the community. However, it does provide a preliminary estimate of the utility as a diagnostic test.

Similarly, the prevalence of AF in the general population is expected to be much lower than 50%. The positive predictive value of the test in a population with a low prevalence of the condition would be much lower. As with any screening test, it would be preferable to use a prespecified population at higher risk, older age, for example, to reduce false positives.


  Conclusion Top


Apex-pulse deficit is a viable tool to identify AF. Using a cutoff of 5 may improve the sensitivity of AF detection. This raises the possibility of using apex-pulse deficit as a low-cost tool to screen for AF without the need for extensive training. However, an irregular pulse on palpation may be more sensitive and specific.

Financial support and sponsorship

One of the authors (A.R.) obtained funding from the Indian Council of Medical Research for this study as a Short-Term Studentship project in the year 2018.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Carlsson J, Miketic S, Windeler J, Cuneo A, Haun S, Micus S, et al. Randomized trial of rate-control versus rhythm-control in persistent atrial fibrillation: The strategies of treatment of atrial fibrillation (STAF) study. J Am Coll Cardiol 2003;41:1690-6.  Back to cited text no. 1
    
2.
Friberg L, Bergfeldt L. Atrial fibrillation prevalence revisited. J Intern Med 2013;274:461-8.  Back to cited text no. 2
    
3.
Healey JS, Connolly SJ, Gold MR, Israel CW, Van Gelder IC, Capucci A, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med 2012;366:120-9.  Back to cited text no. 3
    
4.
Wiesel J, Wiesel D, Suri R, Messineo FC. The use of a modified sphygmomanometer to detect atrial fibrillation in outpatients. Pacing Clin Electrophysiol 2004;27:639-43.  Back to cited text no. 4
    
5.
Kane SA, Blake JR, McArdle FJ, Langley P, Sims AJ. Opportunistic detection of atrial fibrillation using blood pressure monitors: A systematic review. Open Heart 2016;3:e000362.  Back to cited text no. 5
    
6.
6. Chan PH, Wong CK, Poh YC, Pun L, Leung WW, Wong YF, et al. Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting. J Am Heart Assoc 2016;5. pii: e003428.  Back to cited text no. 6
    
7.
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. Br Med J (Clin Res Ed) 2007;335:383.  Back to cited text no. 7
    
8.
Longo D, Fauci A, Kasper D, Hauser S, Jameson JL, Loscalzo J. Harrison's Principles of Internal Medicine, 18th Edition. Published by McGraw Hill Medical; 2011.  Back to cited text no. 8
    
9.
Cooke G, Doust J, Sanders S. Is pulse palpation helpful in detecting atrial fibrillation? A systematic review. J Fam Pract 2006;55:130-4.  Back to cited text no. 9
    
10.
Digital AF, Vandervoort PM. Late-Breaking Science in Telemedicine. Munich: European Society of Cardiology Congress; 2018. p. 25-9.  Back to cited text no. 10
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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